Why_this_webapp Bibliography

The AI Index 2022 Annual Report / 2205.03468 / ISBN:https://doi.org/10.48550/arXiv.2205.03468 / Published by ArXiv / on (web) Publishing site

Report highlights
Chapter 1 Reseach and Development
Chapter 2 Technical Performance
Chapter 3 Technical AI Ethics
Chapter 4 The Economy and Education
Chapter 5 AI Policy and Governance
Appendix

Exciting, Useful, Worrying, Futuristic: Public Perception of Artificial Intelligence in 8 Countries / 2001.00081 / ISBN:https://doi.org/10.48550/arXiv.2001.00081 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Methodology
4 Findings
5 Discussion
6 Conclusions
References

Ethics of AI: A Systematic Literature Review of Principles and Challenges / 2109.07906 / ISBN:https://doi.org/10.48550/arXiv.2109.07906 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Research Method
5 Detail results and analysis
6 Threats to validity
7 Conclusions and future directions
References
9 Appendices

AI Ethics Issues in Real World: Evidence from AI Incident Database / 2206.07635 / ISBN:https://doi.org/10.48550/arXiv.2206.07635 / Published by ArXiv / on (web) Publishing site

Abstract
1Introduction
2 Related Work
3 Method
4 Results
5 Discussion
6 Conclusion
References

The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis / 2206.03225 / ISBN:https://doi.org/10.48550/arXiv.2206.03225 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Study Methodology
4 Evaluation of Ethical AI Principles
5 Evaluation of Ethical Principle Implementations
6 Gap Mitigation
8 Conclusion
References

A Framework for Ethical AI at the United Nations / 2104.12547 / ISBN:https://doi.org/10.48550/arXiv.2104.12547 / Published by ArXiv / on (web) Publishing site

Executive summary
Introductionn
1. Problems with AI
2. Defining ethical AI
3. Implementing ethical AI
Conclusion

Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance / 2206.11922 / ISBN:https://doi.org/10.48550/arXiv.2206.11922 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methodology
4 Results
5 Discussion
6 Conclusion
Author Contributions

Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society / 2001.04335 / ISBN:https://doi.org/10.48550/arXiv.2001.04335 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 The Near-Long
3 The Problem with the Near/Long-Term Distinction
4 A Clearer Account of Research Priorities and Disagreements
References

ESR: Ethics and Society Review of Artificial Intelligence Research / 2106.11521 / ISBN:https://doi.org/10.48550/arXiv.2106.11521 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 The ESR Process
4 Deployment and Evaluation
5 Discussion
6 Conclusion
A Appendix: Interview Protocol
References

On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services / 2111.01306 / ISBN:https://doi.org/10.48550/arXiv.2111.01306 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Need forEthical AI in Finance
3 Practical Challengesof Ethical AI
4 Conclusions & Outlook
References

A primer on AI ethics via arXiv- focus 2020-2023 / Kaggle / Published by Kaggle / on (web) Publishing site

Section 1: Introduction and concept
Section 2: History and prospective
Section 3: Current trends 2020-2023
Section 4: Considerations and conclusions
Appendix A: Bibliographical references
Appendix B: Data and charts from arXiv

What does it mean to be a responsible AI practitioner: An ontology of roles and skills / 2205.03946 / ISBN:https://doi.org/10.48550/arXiv.2205.03946 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Methodology
4 Proposed competency framework for responsible AI practitioners
5 Discussion
References
Appendix A supplementary material

GPT detectors are biased against non-native English writers / 2304.02819 / ISBN:https://doi.org/10.48550/arXiv.2304.02819 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Results
Discussion
References
Materials and Methods

Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects / 2304.08275 / ISBN:https://doi.org/10.48550/arXiv.2304.08275 / Published by ArXiv / on (web) Publishing site

I. Introduction
II. Underlying Aspects
III. Interactions between Aspects
IV. Concluding Remarks
References

QB4AIRA: A Question Bank for AI Risk Assessment / 2305.09300 / ISBN:https://doi.org/10.48550/arXiv.2305.09300 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 The Question Bank: QB4AIRA
3 Evaluation
4 Conclusion
References

A multilevel framework for AI governance / 2307.03198 / ISBN:https://doi.org/10.48550/arXiv.2307.03198 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introductioon
2. A Multilevel Approach to AI Governance for Trust-Enhancing Practices
3. International and National Governance
4. Corporate Self-Governance
5. AI Literacy and Governance by Citizen
6. Psychology of Trust
8. Ethics and Trust Lenses in the Multilevel Framework
9. Virtue Ethics
References

From OECD to India: Exploring cross-cultural differences in perceived trust, responsibility and reliance of AI and human experts / 2307.15452 / ISBN:https://doi.org/10.48550/arXiv.2307.15452 / Published by ArXiv / on (web) Publishing site

1. Introduction
2. Method
References

The Ethics of AI Value Chains / 2307.16787 / ISBN:https://doi.org/10.48550/arXiv.2307.16787 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Theory
3. Methodology
4. Ethical Implications of AI Value Chains
5. Future Directions for Research, Practice, & Policy
6. Conclusion

Perceptions of the Fourth Industrial Revolution and Artificial Intelligence Impact on Society / 2308.02030 / ISBN:https://doi.org/10.48550/arXiv.2308.02030 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Literature Review
Results
References

Regulating AI manipulation: Applying Insights from behavioral economics and psychology to enhance the practicality of the EU AI Act / 2308.02041 / ISBN:https://doi.org/10.48550/arXiv.2308.02041 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Clarifying Terminologies of Article-5: Insights from Behavioral Economics and Psychology
3 Enhancing Protection for the General Public and Vulnerable Groups
4 Conclusion
References

From Military to Healthcare: Adopting and Expanding Ethical Principles for Generative Artificial Intelligence / 2308.02448 / ISBN:https://doi.org/10.48550/arXiv.2308.02448 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
What is Generative Artificial Intelligence?
Applications in Military Versus Healthcare
Identifying Ethical Concerns and Risks
GREAT PLEA Ethical Principles for Generative AI in Healthcare
Conclusion
Acknowledgements
References

Ethical Considerations and Policy Implications for Large Language Models: Guiding Responsible Development and Deployment / 2308.02678 / ISBN:https://doi.org/10.48550/arXiv.2308.02678 / Published by ArXiv / on (web) Publishing site

Abstract
System-role
Perturbation
Image-related
Hallucination
Generation-related
Bias and Discrimination of Training Data
Conclusion

Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI / 2308.04448 / ISBN:https://doi.org/10.48550/arXiv.2308.04448 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Policy scope
4 Centralized regulation in the US context
5 Crowdsourced safety mechanism
6 The dual governance framework
7 Limitations

Normative Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions / 2208.12616 / ISBN:https://doi.org/10.48550/arXiv.2208.12616 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Methodology
3 Taxonomy of ethical principles
4 Previous operationalisation of ethical principles
5 Gaps in operationalising ethical principles
6 Conclusion
References
A Methodology

Bad, mad, and cooked: Moral responsibility for civilian harms in human-AI military teams / 2211.06326 / ISBN:https://doi.org/10.48550/arXiv.2211.06326 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Responsibility in War
Computers, Autonomy and Accountability
Moral Injury
Human Factors
AI Workplace Health and Safety Framework
Discussion
References

The Future of ChatGPT-enabled Labor Market: A Preliminary Study / 2304.09823 / ISBN:https://doi.org/10.48550/arXiv.2304.09823 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Results
5 Methods
References

A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation / 2305.11391 / ISBN:https://doi.org/10.48550/arXiv.2305.11391 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Large Language Models
3 Vulnerabilities, Attack, and Limitations
4 General Verification Framework
5 Falsification and Evaluation
6 Verification
7 Runtime Monitor
8 Regulations and Ethical Use
9 Discussions
Reference

Getting pwn'd by AI: Penetration Testing with Large Language Models / 2308.00121 / ISBN:https://doi.org/10.48550/arXiv.2308.00121 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 LLM-based penetration testing
4 Discussion
5 A vision of AI-augmented pen-testing
6 Final ethical considerations
References

Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust / 2308.09979 / ISBN:https://doi.org/10.48550/arXiv.2308.09979 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Results
3 Discussion
4 Conclusions
References

Targeted Data Augmentation for bias mitigation / 2308.11386 / ISBN:https://doi.org/10.48550/arXiv.2308.11386 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related works
3 Targeted data augmentation
4 Experiments
5 Conclusions
References

AIxArtist: A First-Person Tale of Interacting with Artificial Intelligence to Escape Creative Block / 2308.11424 / ISBN:https://doi.org/10.48550/arXiv.2308.11424 / Published by ArXiv / on (web) Publishing site

Abstract
Reflections

Exploring the Power of Creative AI Tools and Game-Based Methodologies for Interactive Web-Based Programming / 2308.11649 / ISBN:https://doi.org/10.48550/arXiv.2308.11649 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Advancements in AI and Web-Based Programming
3 Emergence of Creative AI Tools and Game-Based Methodologies
4 Enhancing User Experience through Creative AI Tools
5 Engaging Web-Based Programming with Game-Based Approaches
6 Unveiling the Potential: Benefits of Interactive Web-Based Programming
7 Navigating Constraints: Limitations of Creative AI and GameBased Techniques
8 Real-World Applications: Showcasing Innovative Implementations
9 Ethical Considerations in Integrating AI and Game Elements
10 Privacy Concerns in Interactive Web-Based Programming for Education
11 Bias Awareness: Navigating AI-Generated Content in Education
12 The Future Landscape: Creative AI Tools and Game-Based Methodologies in Education
13 Case Study Example: Learning Success with Creative AI and Game-Based Techniques
14 Conclusion & Discussion
References

Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection / 2308.12885 / ISBN:https://doi.org/10.48550/arXiv.2308.12885 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work on Data Excellence
3 Reliability and Reproducibility Metrics for Responsible Data Collection
4 Published Annotation Tasks and Datasets
5 Results
6 Discussion
References

Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph / 2308.13534 / ISBN:https://doi.org/10.48550/arXiv.2308.13534 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Methods and training process of LLMs
III. Comprehensive review of state-of-the-art LLMs
IV. Applied and technology implications for LLMs
V. Market analysis of LLMs and cross-industry use cases
VI. Solution architecture for privacy-aware and trustworthy conversational AI
VII. Discussions
VIII. Conclusion
References
Appendix A industry-wide LLM usecases

The Promise and Peril of Artificial Intelligence -- Violet Teaming Offers a Balanced Path Forward / 2308.14253 / ISBN:https://doi.org/10.48550/arXiv.2308.14253 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The evolution of artificial intelligence: from theory to general capabilities
3 Emerging dual-use risks and vulnerabilities in AI systems
4 Integrating red teaming, blue teaming, and ethics with violet teaming
5 Research directions in AI safety and violet teaming
6 A pathway for balanced AI innovation
7 Violet teaming to address dual-use risks of AI in biotechnology
8 Macrostrategy for responsible technology trajectories
9 The path forward
10 Supplemental & additional details
References

Artificial Intelligence in Career Counseling: A Test Case with ResumAI / 2308.14301 / ISBN:https://doi.org/10.48550/arXiv.2308.14301 / Published by ArXiv / on (web) Publishing site

Abstract
4 Results and discussion

Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? / 2308.15399 / ISBN:https://doi.org/10.48550/arXiv.2308.15399 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Works
3 Theory and Method
4 Experiment
References

The AI Revolution: Opportunities and Challenges for the Finance Sector / 2308.16538 / ISBN:https://doi.org/10.48550/arXiv.2308.16538 / Published by ArXiv / on (web) Publishing site

Table of contents and index
Executive summary
1 Introduction
2 Key AI technology in financial services
3 Benefits of AI use in the finance sector
4 Threaths & potential pitfalls
5 Challenges
6 Regulation of AI and regulating through AI
7 Recommendations
References

Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond / 2309.00064 / ISBN:https://doi.org/10.48550/arXiv.2309.00064 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Black box and lack of transparency
3 Bias and fairness
4 Human-centric AI
5 Ethical concerns and value alignment
6 Way forward
7 Conclusion
References

The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie / 2309.02029 / ISBN:https://doi.org/10.48550/arXiv.2309.02029 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related work
3. ChatGPT Training Process
4. Methods
5. Discussion
6. Conclusion
References

Pathway to Future Symbiotic Creativity / 2209.02388 / ISBN:https://doi.org/10.48550/arXiv.2209.02388 / Published by ArXiv / on (web) Publishing site

Contents
Introduction
Part 1 - 1 Generatives Systems: Mimicking Artifacts
Part 1 - 2 Appreciate Systems: Mimicking Styles
Part 1 - 3 Artistic Systems: Mimicking Inspiration
Part 2 - 1 Biometric Signal Sensing Technologies and Emotion Data
Part 2 - 2 Motion Caputer Technologies and Motion Data
Part 2 - 3 Photogrammetry / Volumetric Capture
Part 2 - 4 Aesthetic Descriptor: Labelling Artefacts with Emotion
Part 2 - 5 Immersive Visualisation: Machine to Human Manifestations
Part 3 Towards a Machine Artist Model
Part 3 - 1 Challenges in Endowing Machines with Creative Abilities
Part 3 - 2 Machine Artist Models
Part 3 - 3 Comparison with Generative Models
Part 3 - 4 Demonstration of the Proposed Framework
Part 4 NFTs and the Future Art Economy
Part 5 Ethical AI and Machine Artist
Part 5 - 1 Authorship and Ownership of AI-generated Works of Artt
Part 5 - 2 Algorithmics Bias in Art Generation
Part 5 - 3 Democratization of Art with new Technologies
References
Acknowledgment

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging / 2109.09658 / ISBN:https://doi.org/10.48550/arXiv.2109.09658 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Fairness - For Equitable AI in Medical Imaging
3. Universality - For Standardised AI in Medical Imaging
4. Traceability - For Transparent and Dynamic AI in Medical Imaging
5. Usability - For Effective and Beneficial AI in Medical Imaging
6. Robustness - For Reliable AI in Medical Imaging
7. Explainability - For Enhanced Understanding of AI in Medical Imaging
8. FUTURE-AI Quality Check
9. Discussion and Conclusion
References

The Cambridge Law Corpus: A Corpus for Legal AI Research / 2309.12269 / ISBN:https://doi.org/10.48550/arXiv.2309.12269 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Cambridge Law Corpus
3 Legal and Ethical Considerations
4 Experiments
5 Conclusion
General References
Legal References
A Detailed Information on Corpus Content
F Evaluation of GPT Models
Cambridge Law Corpus: Datasheet

EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval / 2310.00970 / ISBN:https://doi.org/10.48550/arXiv.2310.00970 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Dataset Construction
4 Modeling Ethics
5 Experiments
6 Conclusions
Appendix

Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities / 2310.08565 / ISBN:https://doi.org/10.48550/arXiv.2310.08565 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction and Motivation
II. AI-Robotics Systems Architecture
III. Survey Approach & Taxonomy
IV. Attack Surfaces
V. Ethical & Legal Concerns
VI. Human-Robot Interaction (HRI) Security Studies
VII. Future Research & Discussion
VIII. Conclusion
References

If our aim is to build morality into an artificial agent, how might we begin to go about doing so? / 2310.08295 / ISBN:https://doi.org/10.48550/arXiv.2310.08295 / Published by ArXiv / on (web) Publishing site

Abstract
1 The Top-Down Approach Alone Might Be Insufficient
2 Emotion, Sentience and Morality
3 Proposing a Hybrid Approach
4 AI Governance Principles
5 Towards Moral AI
References

Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks / 2310.07879 / ISBN:https://doi.org/10.48550/arXiv.2310.07879 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 Method
4 Taxonomy of AI Privacy Risks
5 Discussion
6 Conclusion
References

ClausewitzGPT Framework: A New Frontier in Theoretical Large Language Model Enhanced Information Operations / 2310.07099 / ISBN:https://doi.org/10.48550/arXiv.2310.07099 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Nation-State Advances in AI-driven Information Operations
Theoretical Impact of LLMs on Information Operations
ClausewitzGPT and Modern Strategy
Mathematical Foundations
Ethical and Strategic Considerations: AI Mediators in the Age of LLMs
Integrating Computational Social Science, Computational Ethics, Systems Engineering, and AI Ethics in LLMdriven Operations
Looking Forward: ClausewitzGPT
Conclusion
References

The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements / 2310.06269 / ISBN:https://doi.org/10.48550/arXiv.2310.06269 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Research Design and Methodology
3 Analysis and Findings
4 Discussion
References
B Pre-class Questionnaire (Verbatim)
G Statistical Tests

A Review of the Ethics of Artificial Intelligence and its Applications in the United States / 2310.05751 / ISBN:https://doi.org/10.48550/arXiv.2310.05751 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Literature Review
3. AI Ethical Principles
4. Implementing the Practical Use of Ethical AI Applications
5. Conclusions and Recommendations
References
Authors

A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics / 2310.05694 / ISBN:https://doi.org/10.48550/arXiv.2310.05694 / Published by ArXiv / on (web) Publishing site

Abstract
I. INTRODUCTION
II. WHAT LLM S CAN DO FOR HEALTHCARE ? FROM FUNDAMENTAL TASKS TO ADVANCED APPLICATIONS
III. FROM PLM S TO LLM S FOR HEALTHCARE
IV. TRAIN AND USE LLM FOR HEALTHCARE
V. EVALUATION METHOD
VI. IMPROVING FAIRNESS , ACCOUNTABILITY, TRANSPARENCY, AND ETHICS
VII. FUTURE WORK AND CONCLUSION
REFERENCES

STREAM: Social data and knowledge collective intelligence platform for TRaining Ethical AI Models / 2310.05563 / ISBN:https://doi.org/10.48550/arXiv.2310.05563 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 STREAM: Social data and knowledge collective intelligence platform for TRaining Ethical AI Models
3 The applications of STREAM
4 Conclusion and Future Work
References

Regulation and NLP (RegNLP): Taming Large Language Models / 2310.05553 / ISBN:https://doi.org/10.48550/arXiv.2310.05553 / Published by ArXiv / on (web) Publishing site

Abstract
2 Regulation: A Short Introduction
3 LLMs: Risk and Uncertainty
4 Scientific Expertise, Social Media and Regulatory Capture
5 Regulation and NLP (RegNLP): A New Field
References

Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industry / 2310.05414 / ISBN:https://doi.org/10.48550/arXiv.2310.05414 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Research Methodology
3. Ethics of AI and Robotics
4. Systematic Review and Scientometric Analysis
5. Ethical Issues of AI and Robotics in AEC Industry
6. Discussion
7. Future Research Direction
8. Conclusion
References

Commercialized Generative AI: A Critical Study of the Feasibility and Ethics of Generating Native Advertising Using Large Language Models in Conversational Web Search / 2310.04892 / ISBN:https://doi.org/10.48550/arXiv.2310.04892 / Published by ArXiv / on (web) Publishing site

Introduction
Background and Related Work
Pilot Study: Text SERPs with Ads
Evaluation of the Pilot Study
Ethics of GEnerating Native Ads
Conclusion
References

Compromise in Multilateral Negotiations and the Global Regulation of Artificial Intelligence / 2309.17158 / ISBN:https://doi.org/10.48550/arXiv.2309.17158 / Published by ArXiv / on (web) Publishing site

1. Introduction
2. The practice of multilateral negotiation and the mechanisms of compromises
3. The liberal-sovereigntist multiplicity
4. Towards a compromise: drafting the normative hybridity
5. Text negotiations as normative testing
Notes
Bibliography
Annex 1. Text amendments and ambiguity

Towards A Unified Utilitarian Ethics Framework for Healthcare Artificial Intelligence / 2309.14617 / ISBN:https://doi.org/10.48550/arXiv.2309.14617 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Why Ethics
Utilitarian Ethics
Principal Ethics in Healthcare
Method
Results and Discussion
A Unified Utilitarian Ethics Framework
Theory and Practical Implications
Conclusion
References

Risk of AI in Healthcare: A Comprehensive Literature Review and Study Framework / 2309.14530 / ISBN:https://doi.org/10.48550/arXiv.2309.14530 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Methods for Comprehensive Review
3. Clinical Risks
4. Technical Risks
5. Conclusion
References
Appendix

Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward / 2309.14213 / ISBN:https://doi.org/10.48550/arXiv.2309.14213 / Published by ArXiv / on (web) Publishing site

Table of Contents
1. Introduction
2. Autonomous vehicles
5. Cybersecurity Risks
6. Risk management
7. Issues
9. References

The Return on Investment in AI Ethics: A Holistic Framework / 2309.13057 / ISBN:https://doi.org/10.48550/arXiv.2309.13057 / Published by ArXiv / on (web) Publishing site

1. Introduction
2. AI Ethics
3. Return on Investment (ROI)
4. A Holistic Framework
5. Discussion
6. References

An Evaluation of GPT-4 on the ETHICS Dataset / 2309.10492 / ISBN:https://doi.org/10.48550/arXiv.2309.10492 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 Datasets and Methods
3 Results
4 Discussion
References

Who to Trust, How and Why: Untangling AI Ethics Principles, Trustworthiness and Trust / 2309.10318 / ISBN:https://doi.org/10.48550/arXiv.2309.10318 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Trust
Trust in AI
Different Types of Trust
Trust and AI Ethics Principles
Trust in AI as Socio-Technical Systems
Conclusion
References

In Consideration of Indigenous Data Sovereignty: Data Mining as a Colonial Practice / 2309.10215 / ISBN:https://doi.org/10.48550/arXiv.2309.10215 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Definitions of Terms
3 Objectives
4 Methodology
5 Relating Case Studies to Indigenous Data Sovereignty and CARE Principles
6 Discussion
7 Conclusions and Recommendations
References

The Glamorisation of Unpaid Labour: AI and its Influencers / 2308.02399 / ISBN:https://doi.org/10.48550/arXiv.2308.02399 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Harms of Influencer Marketing
3 Ethical Data Collection, Responsible AI Development, and the Path Forward
4 Conclusion
References

AI & Blockchain as sustainable teaching and learning tools to cope with the 4IR / 2305.01088 / ISBN:https://doi.org/10.48550/arXiv.2305.01088 / Published by ArXiv / on (web) Publishing site

Authors
Abstract
1. Introduction
2. AI and blockchain in education: An overview of the benefits and challenges
3. AI-powered personalized learning: Customized learning experiences for learners
4. Blockchain-based credentialing and certification
5. AI-powered assessment and evaluation
6. Blockchain-based decentralized learning networks
7. AI-powered content creation and curation
8. Case studies: AI and blockchain in education
11.References

Toward an Ethics of AI Belief / 2304.14577 / ISBN:https://doi.org/10.48550/arXiv.2304.14577 / Published by ArXiv / on (web) Publishing site

1. Introduction
2. “Belief” in Humans and AI
3. Proposed Novel Topics in an Ethics of AI Belief
4. Nascent Extant Work that Falls Within the Ethics of AI Belief
5. Conclusion
References

Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles / 2304.11530 / ISBN:https://doi.org/10.48550/arXiv.2304.11530 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Ethical concerns of AI in medicine
Ethical datasets and algorithm development guidelines
Towards solving key ethical challenges in Medical AI
Ethical guidelines for medical AI model deployment
Discussion
Conclusion and future directions
References

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering / 2209.04963 / ISBN:https://doi.org/10.48550/arXiv.2209.04963 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Methodology
3 Governance Patterns
4 Process Patterns
5 Product Patterns
6 Related Work
7 Threats to Validity
References

The Ethics of AI Value Chains / 2307.16787 / ISBN:https://doi.org/10.48550/arXiv.2307.16787 / Published by ArXiv / on (web) Publishing site

Bibliography
Appendix A: Integrated Inventory of Ethical Concerns, Value Chains Actors, Resourcing Activities, & Sampled Sources

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare / 2309.12325 / ISBN:https://doi.org/10.48550/arXiv.2309.12325 / Published by ArXiv / on (web) Publishing site

Abstract
INTRODUCTION
METHODS
FUTURE-AI GUIDELINE
DISCUSSION
ACKNOWLEDGMENTS
COMPETING INTERESTS

Language Agents for Detecting Implicit Stereotypes in Text-to-Image Models at Scale / 2310.11778 / ISBN:https://doi.org/10.48550/arXiv.2310.11778 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Agent Design
3 Agent Benchmark
4 Agent Performance
5 Related Work
6 Conclusion and Future Work
References
Appendix A Data Details
Appendix B Experiment Details

Specific versus General Principles for Constitutional AI / 2310.13798 / ISBN:https://doi.org/10.48550/arXiv.2310.13798 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 AI feedback on specific problematic AI traits
3 Generalization from a Simple Good for Humanity Principle
4 Reinforcement Learning with Good-for-Humanity Preference Models
5 Related Work
6 Discussion
7 Contribution Statement
References
B Trait Preference Modeling
C General Prompts for GfH Preference Modeling
D Generalization to Other Traits
G Over-Training on Good for Humanity
H Samples
I Responses on Prompts from PALMS, LaMDA, and InstructGPT

The Self 2.0: How AI-Enhanced Self-Clones Transform Self-Perception and Improve Presentation Skills / 2310.15112 / ISBN:https://doi.org/10.48550/arXiv.2310.15112 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Method
4 Findings
5 Discussion
6 Conclusion
References

Systematic AI Approach for AGI: Addressing Alignment, Energy, and AGI Grand Challenges / 2310.15274 / ISBN:https://doi.org/10.48550/arXiv.2310.15274 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Trifecta of AI Challenges
3 Systematic AI Approach for AGI
4 Systematic AI for Energy Wall
5 System Design for AI Alignment
6 System Insights from the Brain
References

AI Alignment and Social Choice: Fundamental Limitations and Policy Implications / 2310.16048 / ISBN:https://doi.org/10.48550/arXiv.2310.16048 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
3 Arrow-Sen Impossibility Theorems for RLHF
4 Implications for AI Governance and Policy
5 Conclusion
References

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges / 2310.16360 / ISBN:https://doi.org/10.48550/arXiv.2310.16360 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Review Methodology
III. UAV Platform Type
IV. Artificial Intelligence Embedded UAV
V. Challenges and Future Aspect on AI Enabled UAV
VI. Review Summary
VII. Conclusion
References
Authors Bios

Unpacking the Ethical Value Alignment in Big Models / 2310.17551 / ISBN:https://doi.org/10.48550/arXiv.2310.17551 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Risks and Ethical Issues of Big Model
3 Investigating the Ethical Values of Large Language Models
4 Equilibrium Alignment: A Prospective Paradigm for Ethical Value Alignmen
5 Conclusion
References

Moral Responsibility for AI Systems / 2310.18040 / ISBN:https://doi.org/10.48550/arXiv.2310.18040 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Causal Models
3 The BvH and HK Definitions
4 The Causal Condition
6 Degree of Responsibility
References
Appendix

AI for Open Science: A Multi-Agent Perspective for Ethically Translating Data to Knowledge / 2310.18852 / ISBN:https://doi.org/10.48550/arXiv.2310.18852 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 A Formal Language of AI for Open Science
4 Optimizing an Openness Metric in AI for Science
5 Why Openness in AI for Science
6 Conclusion and Future Work
Acknowledgements
References

Artificial Intelligence Ethics Education in Cybersecurity: Challenges and Opportunities: a focus group report / 2311.00903 / ISBN:https://doi.org/10.48550/arXiv.2311.00903 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
AI Ethics in Cybersecurity
Focus Group Protocol and Recruitment
Educational Challenges of Teaching AI Ethics in Cybersecurity and Core Ethical Principles
Pedagogical / Curricular Concerns Now and in the Future
Technical Issues
Learning Challenges
AI tool-specific educational concerns
Broader educational preparedness for work in AI Cybersecurity
Communication skills in cybersecurity and ethics
Conclusion
References

Human participants in AI research: Ethics and transparency in practice / 2311.01254 / ISBN:https://doi.org/10.48550/arXiv.2311.01254 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Contextual Concerns: Why AI Research Needs its Own Guidelines
III. Ethical Principles for AI Research with Human Participants
IV. Principles in Practice: Guidelines for AI Research with Human Participants
V. Conclusion
Acknowledgments
References
Author
Appendix A Evaluating Current Practices for Human-Participants Research
Appendix B Placing Research Ethics for Human Participans in Historical Context
Appendix C Defining the Scope of Research Participation in AI Research

LLMs grasp morality in concept / 2311.02294 / ISBN:https://doi.org/10.48550/arXiv.2311.02294 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 A General Theory of Meaning
3 The Meaning Model
4 The Moral Model
5 Conclusion
A Supplementary Material
References

Educating for AI Cybersecurity Work and Research: Ethics, Systems Thinking, and Communication Requirements / 2311.04326 / ISBN:https://doi.org/10.48550/arXiv.2311.04326 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Literature Review
Research questions
Conclusions
References

Towards Effective Paraphrasing for Information Disguise / 2311.05018 / ISBN:https://doi.org/10.1007/978-3-031-28238-6_22 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 Related Work
3 Methodology
4 Evaluation
5 Conclusion
References

Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metrics / 2311.05227 / ISBN:https://doi.org/10.48550/arXiv.2311.05227 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Overview of Kantian Deontology
3 Measuring Fairness Metrics
4 Deontological AI Alignment
5 Aligning with Deontological Principles: Use Cases
6 Conclusion

Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing / 2304.02017 / ISBN:https://doi.org/10.48550/arXiv.2304.02017 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Overview of ChatGPT and its capabilities
3 Transformers and pre-trained language models
4 Applications of ChatGPT in real-world scenarios
6 Limitations and potential challenges
7 Ethical considerations when using ChatGPT
8 Prompt engineering and generation
11 Conclusion
References

Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies / 2304.07683 / ISBN:https://doi.org/10.48550/arXiv.2304.07683 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Sources of bias in AI
III. Impacts of bias in AI
IV. Mitigation strategies for bias in AI
V. Fairness in AI
VI. Mitigation strategies for fairness in AI
VII. Conclusions
References

Towards ethical multimodal systems / 2304.13765 / ISBN:https://doi.org/10.48550/arXiv.2304.13765 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Crafting an Ethical Dataset
4 A Multimodal Ethics Classifier

A Brief History of Prompt: Leveraging Language Models. (Through Advanced Prompting) / 2310.04438 / ISBN:https://doi.org/10.48550/arXiv.2310.04438 / Published by ArXiv / on (web) Publishing site

Abstract
II. Introduction
III. Prehistoric prompting: pre NN-era
IV. History of NLP between 2010 and 2015: the pre-attention mechanism era
VI. 2015: birth of the transformer
VIII. The third wave 2018: the rise of transformers
IX. 2019: THE YEAR OF CONTROL
X. 2020-2021: the rise of LLMS
XII. Conclusions

Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents / 2310.15065 / ISBN:https://doi.org/10.48550/arXiv.2310.15065 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related work
3 Method
4 Findings
5 Discussion
References

She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models / 2310.18333 / ISBN:https://doi.org/10.48550/arXiv.2310.18333 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Works
3 ReFLeCT: Robust, Fair, and Safe LLM Construction Test Suite
4 Empirical Evaluation and Outcomes
References

Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control / 2311.08943 / ISBN:https://doi.org/10.48550/arXiv.2311.08943 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Humans In, On, and Out-of-the-Loop
III. Safety
IV. Trust
V. Ethics
VI. Conclusion
References

How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities / 2311.09447 / ISBN:https://doi.org/10.48550/arXiv.2311.09447 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methodology
4 Experiments
5 Conclusion
References

Prudent Silence or Foolish Babble? Examining Large Language Models' Responses to the Unknown / 2311.09731 / ISBN:https://doi.org/10.48550/arXiv.2311.09731 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 UnknownBench: Evaluating LLMs on the Unknown
3 Experiments
5 Conclusion
References
A Limitations
C Additional Details of the Dataset Construction
D Additional Results and Figures
E Prompt Templates
F NEC Question Generation Template

Revolutionizing Customer Interactions: Insights and Challenges in Deploying ChatGPT and Generative Chatbots for FAQs / 2311.09976 / ISBN:https://doi.org/10.48550/arXiv.2311.09976 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Chatbots Background and Scope of Research
3. Chatbot approaches overview: Taxonomy of existing methods
4. ChatGPT
5. Applications
6. Open chanllenges
7. Future Research Directions
8. Conclusion
References

Practical Cybersecurity Ethics: Mapping CyBOK to Ethical Concerns / 2311.10165 / ISBN:https://doi.org/10.48550/arXiv.2311.10165 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Methodology
4 Findings
5 Discussion
6 Limitations
7 Conclusion
References
A Ethics of the cyber security profession: interview guide

First, Do No Harm: Algorithms, AI, and Digital Product Liability Managing Algorithmic Harms Though Liability Law and Market Incentives / 2311.10861 / ISBN:https://doi.org/10.48550/arXiv.2311.10861 / Published by ArXiv / on (web) Publishing site

Bloustein Local and the Center for Urban Policy Research
Executive Summary
Preface
Introduction
The Problem
Why Liability Law?
Harms, Risk, and Liability Practices
Mitigation Tools
Conclusion
Appendix A - What is an Algorithmic Harm? And a Bibliography
Appendix B – Common AI Harms as Described by EPIC10
About the Author

Case Repositories: Towards Case-Based Reasoning for AI Alignment / 2311.10934 / ISBN:https://doi.org/10.48550/arXiv.2311.10934 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Proposed Process
3 Related Work and Discussion
4 Conclusion
References

Responsible AI Considerations in Text Summarization Research: A Review of Current Practices / 2311.11103 / ISBN:https://doi.org/10.48550/arXiv.2311.11103 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background & Related Work
3 Methods
4 Findings
5 Discussion and Recommendations
7 Limitations
8 Ethical Considerations
References
B Methodology

Assessing AI Impact Assessments: A Classroom Study / 2311.11193 / ISBN:https://doi.org/10.48550/arXiv.2311.11193 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Study Design
4 Findings
5 Discussion
References
A Overview of AIIA Instruments
B Study Materials

GPT in Data Science: A Practical Exploration of Model Selection / 2311.11516 / ISBN:https://doi.org/10.48550/arXiv.2311.11516 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background
III. Approach: capturing and representing heuristics behind GPT's decision-making process
IV. Comparative results
V. Conclusion and future work
VI. Future work
References

Responsible AI Research Needs Impact Statements Too / 2311.11776 / ISBN:https://doi.org/10.48550/arXiv.2311.11776 / Published by ArXiv / on (web) Publishing site

Abstract
Requiring adverse impact statements for RAI research is long overdue
What are other research communities doing?
What do RAI venues do?
Suggestions for More Meaningful Engagement with the Impact of RAI Research
Concluding Reflections
References

Large Language Models in Education: Vision and Opportunities / 2311.13160 / ISBN:https://doi.org/10.48550/arXiv.2311.13160 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Education and LLMS
III. Key technologies for EDULLMS
IV. LLM-empowered education
V. Key points in LLMSEDU
VI. Challenges and future directions
VII. Conclusion
References

The Rise of Creative Machines: Exploring the Impact of Generative AI / 2311.13262 / ISBN:https://doi.org/10.48550/arXiv.2311.13262 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Extent and impact of generative AI
IV. Risks of generative AI
V. Additional thoughts
VI. Conclusion
References

Towards Auditing Large Language Models: Improving Text-based Stereotype Detection / 2311.14126 / ISBN:https://doi.org/10.48550/arXiv.2311.14126 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Works
3 Methodology
4 Results and Discussion
5 Conclusion and Future Work
References

Ethical Implications of ChatGPT in Higher Education: A Scoping Review / 2311.14378 / ISBN:https://doi.org/10.48550/arXiv.2311.14378 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Research Method
Results
Conclusion
References

Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review / 2311.14381 / ISBN:https://doi.org/10.48550/arXiv.2311.14381 / Published by ArXiv / on (web) Publishing site

Abstract
INTRODUCTION
OVERVIEW OF SOCIETAL BIASES IN GAI MODELS
ANALYTICAL FRAMEWORK
FINDINGS
DISCUSSION
CONCLUSION

RAISE -- Radiology AI Safety, an End-to-end lifecycle approach / 2311.14570 / ISBN:https://doi.org/10.48550/arXiv.2311.14570 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Pre-Deployment phase
3. Production deployment monitoring phase
4. Post-market surveillance phase
5. Conclusion
Funding/Disclosures
Bibliography

Ethics and Responsible AI Deployment / 2311.14705 / ISBN:https://doi.org/10.48550/arXiv.2311.14705 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction: The Role of Algorithms in Protecting Privacy
3. Ethical considerations in AI decision-making
4. Addressing bias, transparency, and accountability
5. Ethical AI design principles and guidelines
6. The role of AI in decision-making: ethical implications and potential consequences
7. Establishing responsible AI governance and oversight
8. AI in sensitive domains: healthcare, finance, criminal justice, defence, and human resources
9. Discussion on engaging stakeholders: fostering dialogue and collaboration between developers, users, and affected communities.
10. Conclusion
11. References

From deepfake to deep useful: risks and opportunities through a systematic literature review / 2311.15809 / ISBN:https://doi.org/10.48550/arXiv.2311.15809 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Material and methods
3. Results
4. Discussion
References

Generative AI and US Intellectual Property Law / 2311.16023 / ISBN:https://doi.org/10.48550/arXiv.2311.16023 / Published by ArXiv / on (web) Publishing site

Abstract
I. Very slowly then all-at-once
II. US Patent law
III. US Copyright law
V. Potential harms and mitigation
VII. Future considerations
References

Survey on AI Ethics: A Socio-technical Perspective / 2311.17228 / ISBN:https://doi.org/10.48550/arXiv.2311.17228 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Privacy and data protection
3 Transparency and explainability
4 Fairness and equity
5 Responsiblity, accountability, and regulations
6 Environmental impact
7 Conclusion
References

Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models / 2311.17394 / ISBN:https://doi.org/10.48550/arXiv.2311.17394 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background
III. The rise of large AI models
IV. Societal implications
V. Technical defense mechanisms
VI. Cross-platform strategies
VII. Ethical considerations
VIII. Proposed integrated defense framework
IX. Discussion
X. Conclusion
Acknowledgement
References

Navigating Privacy and Copyright Challenges Across the Data Lifecycle of Generative AI / 2311.18252 / ISBN:https://doi.org/10.48550/arXiv.2311.18252 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Legal Basis of Privacy and Copyright Concerns over Generative AI
3 Mapping Challenges throughout the Data Lifecycle
4 Lifecycle Approaches
5 Conclusion
References

From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety / 2312.02078 / ISBN:https://doi.org/10.48550/arXiv.2312.02078 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Related works
Software system features
Applications and Visualizations
Community Engagement
System Evaluation and Results
References

Understanding Teacher Perspectives and Experiences after Deployment of AI Literacy Curriculum in Middle-school Classrooms / 2312.04839 / ISBN:https://doi.org/10.48550/arXiv.2312.04839 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Methodology
3 Results
4 Conclusions
References

Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines / 2312.05235 / ISBN:https://doi.org/10.48550/arXiv.2312.05235 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Research questions
3. Literature review
4. Method
5. Results
6. Discussion
7. Conclusion

Contra generative AI detection in higher education assessments / 2312.05241 / ISBN:https://doi.org/10.48550/arXiv.2312.05241 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. The pitfalls in detecting generative AI output
3. Detectors are not useful
4. Teach critical usage of AI
References

Intelligence Primer / 2008.07324 / ISBN:https://doi.org/10.48550/arXiv.2008.07324 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Human intelligence
3 Reasoning
4 Bias, prejudice, and individuality
5 System design of intelligence
6 Measuring intelligence
7 Mathematically modeling intelligence
8 Consciousness
9 Augmenting human intelligence
10 Exceeding human intelligence
11 Control of intelligence
12 Large language models and Generative AI
13 Legal implications
14 Wrong numbers
15 Final thoughts
References

RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems / 2306.01774 / ISBN:https://doi.org/10.48550/arXiv.2306.01774 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related work
3 Methodology
4 Results & analysis
5 Discussion
6 Conclusion & future work
References

Ethical Considerations Towards Protestware / 2306.10019 / ISBN:https://doi.org/10.48550/arXiv.2306.10019 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background
III. Ethics: a primer
IV. Guidelines for promoting ethical responsibility
V. Implications whit future directions
References

Control Risk for Potential Misuse of Artificial Intelligence in Science / 2312.06632 / ISBN:https://doi.org/10.48550/arXiv.2312.06632 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Risks of Misuse for Artificial Intelligence in Science
3 Control the Risks of AI Models in Science
4 Call for Responsible AI for Science
6 Related Works
References
7 Ethical Impacts
Appendix A Assessing the Risks of AI Misuse in Scientific Research
Appendix B Details of Risks Demonstration in Chemical Science
Appendix C Detailed Implementation of SciGuard
Appendix D Details of Benchmark Results

Disentangling Perceptions of Offensiveness: Cultural and Moral Correlates / 2312.06861 / ISBN:https://doi.org/10.48550/arXiv.2312.06861 / Published by ArXiv / on (web) Publishing site

Abstract
...
Data Collection
Study 1: Geo-cultural Differences in Offensiveness
Study 2: Moral Foundations of Offensiveness
Study 3: Implications for Responsible AI
General Discussion
Geo-Cultural Factors
Moral Factors
References
A Appendix

The AI Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment / 2312.07086 / ISBN:https://doi.org/10.48550/arXiv.2312.07086 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Literature
Problematizing The View Of GenAI Content As Academic Misconduct
The AI Assessment Scale
Conclusion
References

Culturally Responsive Artificial Intelligence -- Problems, Challenges and Solutions / 2312.08467 / ISBN:https://doi.org/10.48550/arXiv.2312.08467 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
The concept of multiculturalism and its importance
Artificial intelligence – concept and ethical background
Culturally responsive AI – current landscape
Recommendations
Conclusion
References

Investigating Responsible AI for Scientific Research: An Empirical Study / 2312.09561 / ISBN:https://doi.org/10.48550/arXiv.2312.09561 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background and motivation
III. Research methodology
IV. Results
V. Discussion
VI. Conclusion and future work
References
Appendix A – Survey Questionnaire
Appendix B – Interview Questionnaire

Designing Guiding Principles for NLP for Healthcare: A Case Study of Maternal Health / 2312.11803 / ISBN:https://doi.org/10.48550/arXiv.2312.11803 / Published by ArXiv / on (web) Publishing site

Abstract
1 Objective
2 Background and significance
3 Materials and methods
4 Results
5 Discussion
7 Acknowledgements
References
B Extended Guiding Principles
C Full survey questions

Beyond Fairness: Alternative Moral Dimensions for Assessing Algorithms and Designing Systems / 2312.12559 / ISBN:https://doi.org/10.48550/arXiv.2312.12559 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Reign of Algorithmic Fairness
3 Taking a Step Forward
4 Limitations
5 Conclusion
References

Learning Human-like Representations to Enable Learning Human Values / 2312.14106 / ISBN:https://doi.org/10.48550/arXiv.2312.14106 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related Work
3. Experiments on Synthetic Data
4. Experiments on Human Data using Language Models
References

Improving Task Instructions for Data Annotators: How Clear Rules and Higher Pay Increase Performance in Data Annotation in the AI Economy / 2312.14565 / ISBN:https://doi.org/10.48550/arXiv.2312.14565 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Theoretical background and hypotheses
III. Method
V. Discussion
VI. Conclusion
References

Culturally-Attuned Moral Machines: Implicit Learning of Human Value Systems by AI through Inverse Reinforcement Learning / 2312.17479 / ISBN:https://doi.org/10.48550/arXiv.2312.17479 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Experimental Study
Results
Discussion
Methods
References
Supplementary Material

Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence / 2401.00286 / ISBN:https://doi.org/10.48550/arXiv.2401.00286 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Foundations of AI-driven threat intelligence
3. Autonomous threat hunting: conceptual framework
4. State-of-the-art AI techniques in autonomous threat hunting
5. Challenges in autonomous threat hunting
6. Case studies and applications
7. Evaluation metrics and performance benchmarks
8. Future directions and emerging trends
9. Conclusion
References

Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review / 2401.01519 / ISBN:https://doi.org/10.48550/arXiv.2401.01519 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. LLMs in cognitive and behavioral psychology
3. LLMs in clinical and counseling psychology
4. LLMs in educational and developmental psychology
5. LLMs in social and cultural psychology
6. LLMs as research tools in psychology
7. Challenges and future directions
8. Conclusion

Synthetic Data in AI: Challenges, Applications, and Ethical Implications / 2401.01629 / ISBN:https://doi.org/10.48550/arXiv.2401.01629 / Published by ArXiv / on (web) Publishing site

1. Introduction
2. The generation of synthetic data
3. The usage of synthetic data
4. Risks and Challenges in Utilizing Synthetic Datasets for AI
5. Conclusions
References

MULTI-CASE: A Transformer-based Ethics-aware Multimodal Investigative Intelligence Framework / 2401.01955 / ISBN:https://doi.org/10.48550/arXiv.2401.01955 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related work
III. Methodology: model development
IV. System design
V. Evaluation
VI. Discussion and future work
VII. Conclusion
References

AI Ethics Principles in Practice: Perspectives of Designers and Developers / 2112.07467 / ISBN:https://doi.org/10.48550/arXiv.2112.07467 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related work
III. Methods
IV. Results
V. Discussion and suggestions
VI. Support mechanisms
VII. Conclusion
References

Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models / 2310.19917 / ISBN:https://doi.org/10.48550/arXiv.2310.19917 / Published by ArXiv / on (web) Publishing site

Abstract
Background and significance
Objective
Materials and methods
Results
Discussion
Conclusion
Supplementary material

Resolving Ethics Trade-offs in Implementing Responsible AI / 2401.08103 / ISBN:https://doi.org/10.48550/arXiv.2401.08103 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Approaches for Resolving Trade-offs
III. Discussion and Recommendations
IV. Concluding Remarks
References

Towards Responsible AI in Banking: Addressing Bias for Fair Decision-Making / 2401.08691 / ISBN:https://doi.org/10.48550/arXiv.2401.08691 / Published by ArXiv / on (web) Publishing site

Abstract
Contents / List of figures / List of tables / Acronyms
1 Introduction
I Understanding bias - 2 Bias and moral framework in AI-based decision making
3 Bias on demand: a framework for generating synthetic data with bias
4 Fairness metrics landscape in machine learning
II Mitigating bias - 5 Fairness mitigation
6 FFTree: a flexible tree to mitigate multiple fairness criteria
III Accounting for bias - 7 Addressing fairness in the banking sector
8 Fairview: an evaluative AI support for addressing fairness
9 Towards fairness through time
IV Conclusions
Bibliography

Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems / 2401.09473 / ISBN:https://doi.org/10.48550/arXiv.2401.09473 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Method
4 Results
5 Discussion
6 Conclusion
References

FAIR Enough How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training? / 2401.11033 / ISBN:https://doi.org/10.48550/arXiv.2401.11033 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 FAIR Data Principles: Theoretical Background and Significance
3 Data Management Challenges in Large Language Models
4 Framework for FAIR Data Principles Integration in LLM Development
5 Discussion
6 Conclusion
References
Appendices

Enabling Global Image Data Sharing in the Life Sciences / 2401.13023 / ISBN:https://doi.org/10.48550/arXiv.2401.13023 / Published by ArXiv / on (web) Publishing site

Abstract
1. Motivation for White Paper
2. Background
3. Use cases representing different image data types and their challenges and status for sharing
4. Towards global image data sharing
Towards Global Image Data Sharing: A to-do list for various stakeholders
References
International Working Group Members who contributed to the discussion and writing of the white paper (in alphabetical order)

Beyond principlism: Practical strategies for ethical AI use in research practices / 2401.15284 / ISBN:https://doi.org/10.48550/arXiv.2401.15284 / Published by ArXiv / on (web) Publishing site

Abstract
1 The “Triple-Too” problem of AI ethics
2 A shift to user-centered realism in scientific contexts
3 Five specific goals and action-guiding strategies for ethical AI use in research practices
4 Concluding remarks
References

A Scoping Study of Evaluation Practices for Responsible AI Tools: Steps Towards Effectiveness Evaluations / 2401.17486 / ISBN:https://doi.org/10.48550/arXiv.2401.17486 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related work
3 Methods
4 RAI tool evaluation practices
5 Towards evaluation of RAI tool effectiveness
6 Limitations
References
A List of RAI tools, with their primary publication
B RAI tools listed by target stage of AI development
C List of publications, with their associated RAI tools
D Summary of themes and codes

Detecting Multimedia Generated by Large AI Models: A Survey / 2402.00045 / ISBN:https://doi.org/10.48550/arXiv.2402.00045 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
3 Detection
4 Tools
5 Discussion
References
Authors' bios

Responsible developments and networking research: a reflection beyond a paper ethical statement / 2402.00442 / ISBN:https://doi.org/10.48550/arXiv.2402.00442 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Networking research today
3 Beyond technical dimensions
4 Sense of engagement and responsibility
5 Possible next steps
6 Concluding remarks
References
A Surveyed research group webpages

Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines / 2402.01659 / ISBN:https://doi.org/10.48550/arXiv.2402.01659 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related literature
3. Research study
4. Findings
5. Discussion
6. Conclusions, limitations, and future work
References

Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik's Cubeà / 2402.01760 / ISBN:https://doi.org/10.48550/arXiv.2402.01760 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Literature review
3. Methodology
4. Discussion
5. Conclusion
References

Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology / 2402.01762 / ISBN:https://doi.org/10.48550/arXiv.2402.01762 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Establishing the novel aspect of AI as a crossover technology
3 Moral and ethical obligations when developing crossover AI technology
4 Recommendations to address threats posed by crossover AI technology
5 Conclusion
References

(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice / 2402.01864 / ISBN:https://doi.org/10.48550/arXiv.2402.01864 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related work and our approach
3 Methods: case-based expert deliberation
4 Results
5 Discussion
6 Conclusion
References

POLARIS: A framework to guide the development of Trustworthy AI systems / 2402.05340 / ISBN:https://doi.org/10.48550/arXiv.2402.05340 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 State of the practice
4 The POLARIS framework
5 POLARIS framework application
7 Conclusion
References

Face Recognition: to Deploy or not to Deploy? A Framework for Assessing the Proportional Use of Face Recognition Systems in Real-World Scenarios / 2402.05731 / ISBN:https://doi.org/10.48550/arXiv.2402.05731 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Background
3. Intervention models from other fields
4. Proposed framework
5. The framework in practice
6. Compliance with International Regulations
7. Conclusions and future work

Ethics in AI through the Practitioner's View: A Grounded Theory Literature Review / 2206.09514 / ISBN:https://doi.org/10.48550/arXiv.2206.09514 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Review Methodology
4 Challenges, Threats and Limitations
5 Findings
6 Discussion and Recommendations
7 Methodological Lessons Learned
8 Conclusion
A List of Included Studies
C Glossary of Terms
References
Authors

Generative Artificial Intelligence in Healthcare: Ethical Considerations and Assessment Checklist / 2311.02107 / ISBN:https://doi.org/10.48550/arXiv.2311.02107 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Methods
Results
Discussion
Conclusion
Author contribution
Reference
Appendix

How do machines learn? Evaluating the AIcon2abs method / 2401.07386 / ISBN:https://doi.org/10.48550/arXiv.2401.07386 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Research methodology and text structure
3. AIcon2abs Instructional Unit
4. Results
5. Conclusion
References

I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench / 2401.17882 / ISBN:https://doi.org/10.48550/arXiv.2401.17882 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Awareness in LLMs
4 Awareness Dataset: AWAREEVAL
5 Experiments
6 Conclusion
Ethical Statement
References
A AWAREEVAL Dataset Details
B Experimental Settings & Results

Mapping the Ethics of Generative AI: A Comprehensive Scoping Review / 2402.08323 / ISBN:https://doi.org/10.48550/arXiv.2402.08323 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Methods
3 Results
4 Discussion
6 Conclusion
References
Appendix B
Appendix C

Taking Training Seriously: Human Guidance and Management-Based Regulation of Artificial Intelligence / 2402.08466 / ISBN:https://doi.org/10.48550/arXiv.2402.08466 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Emerging Management-based AI Regulation
3 Management-based Regulation and Human-Guided Training
4 Techniques of Human-Guided Training
5 Advantages of Human-Guided Training
6 Limitations
References

User Modeling and User Profiling: A Comprehensive Survey / 2402.09660 / ISBN:https://doi.org/10.48550/arXiv.2402.09660 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Analysis of the Terminology
3 Paradigm Shifts and New Trends
4 Current Taxonomy
5 Discussion and Future Research Directions
References

Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence / 2402.09880 / ISBN:https://doi.org/10.48550/arXiv.2402.09880 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background and Related Work
III. Unified Evaluation Framework For LLM Benchmarks
IV. Technological Aspects
VI. Human Dynamics
VII. Discussions
VIII. Conclusion
References
Authors
Appendix A Examples of Benchmark Inadequacies in Technological Aspects
Appendix B Examples of Benchmark Inadequacies in Processual Elements
Appendix C Examples of Benchmark Inadequacies in Human Dynamics

Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implications / 2402.12216 / ISBN:https://doi.org/10.48550/arXiv.2402.12216 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 The AIGC Copyright Dilemma: A What-if Analysis
5 Public Perception: A Survey Method
7 Conclusion
References

Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation / 2402.12590 / ISBN:https://doi.org/10.48550/arXiv.2402.12590 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Emergence of Free-Formed AI Collectives
3. Enhanced Performance of Free-Formed AI Collectives
4. Robustness of Free-Formed AI Collectives Against Risks
5. Open Challenges for Free-Formed AI Collectives
References
A. Cocktail Simulation
B. Sentence Making Simulation
C. Public Good Simulation

What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents / 2402.13184 / ISBN:https://doi.org/10.48550/arXiv.2402.13184 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 CosmoAgent Simulation Setting
4 CosmoAgent Architecture
5 Evaluation
6 Experimental Design
7 Results
8 Conclusion
A CosmoAgent Prompt
B Secretary Agent Prompt
References

The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review / 2402.13635 / ISBN:https://doi.org/10.48550/arXiv.2402.13635 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Results
METRIC-framework for medical training data
Discussion
Methods
References
Author contributions

The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success / 2402.14728 / ISBN:https://doi.org/10.48550/arXiv.2402.14728 / Published by ArXiv / on (web) Publishing site

Abstract
1 The increasing importance of AI
2 The EU AI Act
3 There is no reliable AI regulation without a sound theory of human-AI interaction
4 There is no trustworthy AI without HCI
5 There is no community without common language and communication
6 Conclusion: Navigating the future of AI and HCI within the EU AI Act framework
References

Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education / 2402.15027 / ISBN:https://doi.org/10.48550/arXiv.2402.15027 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Materials and Methods
5 Results
6 Discussion
References
Appendix 1 Scenarios

Autonomous Vehicles: Evolution of Artificial Intelligence and Learning Algorithms / 2402.17690 / ISBN:https://doi.org/10.48550/arXiv.2402.17690 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. The AI-Powered Development Life-Cycle in Autonomous Vehicles
III. Ethical Considerations and Bias in AI-Driven Software Development for Autonomous Vehicles
V. Review of Existing Research and Use Cases
VI. AI and Learning Algorithms Statistics for Autonomous Vehicles
VII. Conclusion
References
Authors

Envisioning the Applications and Implications of Generative AI for News Media / 2402.18835 / ISBN:https://doi.org/10.48550/arXiv.2402.18835 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 The Suitability of Generative AI for Newsroom Tasks
3 Conclusion
References

FATE in MMLA: A Student-Centred Exploration of Fairness, Accountability, Transparency, and Ethics in Multimodal Learning Analytics / 2402.19071 / ISBN:https://doi.org/10.48550/arXiv.2402.19071 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Background
3. Methods
4. Results
5. Discussion
6. Conclusion
References

Guidelines for Integrating Value Sensitive Design in Responsible AI Toolkits / 2403.00145 / ISBN:https://doi.org/10.48550/arXiv.2403.00145 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 Methodology
4 Results
5 Discussion
6 Conclusion
References
B Toolkits Considered for Inclusion

Implications of Regulations on the Use of AI and Generative AI for Human-Centered Responsible Artificial Intelligence / 2403.00148 / ISBN:https://doi.org/10.48550/arXiv.2403.00148 / Published by ArXiv / on (web) Publishing site

Abstract
1 Motivation & Background
3 Proposal & SIG’S Goal at CHI 2024
References

The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN) / 2403.02558 / ISBN:https://doi.org/10.48550/arXiv.2403.02558 / Published by ArXiv / on (web) Publishing site

Abstract
Part 1. Study design
Part 2. A new train-test split for prompt development and few-shot learning
Part 4. Model evaluation
Part 5. Interpretability of generative models
Part 6. End-to-end pipeline replication
Conclusions
Disclosures
Table 1. Updated MI-CLAIM checklist for generative AI clinical studies.
References

Towards an AI-Enhanced Cyber Threat Intelligence Processing Pipeline / 2403.03265 / ISBN:https://doi.org/10.48550/arXiv.2403.03265 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction & Motivation
II. Background & Literature Review
III. The AI-Enhanced CTI Processing Pipeline
IV. Challenges and Considerations
V. Conclusions & Future Research
References

A Survey on Human-AI Teaming with Large Pre-Trained Models / 2403.04931 / ISBN:https://doi.org/10.48550/arXiv.2403.04931 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 AI Model Improvements with Human-AI Teaming
3 Effective Human-AI Joint Systems
4 Safe, Secure and Trustworthy AI
5 Applications
6 Conclusion
References

Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines / 2312.05235 / ISBN:https://doi.org/10.48550/arXiv.2312.05235 / Published by ArXiv / on (web) Publishing site

References

Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metrics / 2311.05227 / ISBN:https://doi.org/10.48550/arXiv.2311.05227 / Published by ArXiv / on (web) Publishing site

References

Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance / 2206.11922 / ISBN:https://doi.org/10.48550/arXiv.2206.11922 / Published by ArXiv / on (web) Publishing site

References

How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities / 2311.09447 / ISBN:https://doi.org/10.48550/arXiv.2311.09447 / Published by ArXiv / on (web) Publishing site

B Baseline Setup
D More Results

Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review / 2401.01519 / ISBN:https://doi.org/10.48550/arXiv.2401.01519 / Published by ArXiv / on (web) Publishing site

References

AGI Artificial General Intelligence for Education / 2304.12479 / ISBN:https://doi.org/10.48550/arXiv.2304.12479 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. What is AGI
3. The Potentials of AGI in Transforming Future Education
4. Ethical Issues and Concerns
5. Discussion
6. Conclusion
References

Moral Sparks in Social Media Narratives / 2310.19268 / ISBN:https://doi.org/10.48550/arXiv.2310.19268 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related Work
4. Methods
5. Results
6. Discussion and Conclusion
References

Responsible Artificial Intelligence: A Structured Literature Review / 2403.06910 / ISBN:https://doi.org/10.48550/arXiv.2403.06910 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
3. Analysis
4. Discussion
6. Conclusion
References

Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policies / 2403.08115 / ISBN:https://doi.org/10.48550/arXiv.2403.08115 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
4 Informational Fairness
5 Representational Fairness
6 Ethics and Morality
7 Use Cases and Applications
8 Conclusion
References

Towards a Privacy and Security-Aware Framework for Ethical AI: Guiding the Development and Assessment of AI Systems / 2403.08624 / ISBN:https://doi.org/10.48550/arXiv.2403.08624 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Theoretical Background
3 Research Methodology
4 Results of the Systematic Literature Review
5 Towards Privacy- and Security-Aware Framework for Ethical AI
6 Discussion and Limitations
7 Conclusion
References

Review of Generative AI Methods in Cybersecurity / 2403.08701 / ISBN:https://doi.org/10.48550/arXiv.2403.08701 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Attacking GenAI
3 Cyber Offense
4 Cyber Defence
5 Implications of Generative AI in Social, Legal, and Ethical Domains
6 Discussion
7 Conclusion
References
Appendix A GPT3.5 and GPT4 OCO-scripting

Evaluation Ethics of LLMs in Legal Domain / 2403.11152 / ISBN:https://doi.org/10.48550/arXiv.2403.11152 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 Related Work
3 Method
4 Experiment
6 Limitations
__ The present study possesses certain limitations, specifically including the following: • Sole reliance on Chinese datasets without validation of feasibility on other languages. • Exclusive use of legal cases from the PRC, without addressing applicability in other legal systems. • The evaluation aspects may not be comprehensive, given the vast scope of legal ethics, with only a partial coverage attempted. • There is potential for expanding the number of LLM evaluated.
References

Trust in AI: Progress, Challenges, and Future Directions / 2403.14680 / ISBN:https://doi.org/10.48550/arXiv.2403.14680 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Methodology
3. Findings
4. Discussion
5. Concluding Remarks and Future Directions
Reference

AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps / 2403.14681 / ISBN:https://doi.org/10.48550/arXiv.2403.14681 / Published by ArXiv / on (web) Publishing site

Introduction
Definitions
Method
Results
AI Ethics Development Phases Based on Keyword Analysis
Key AI Ethics Issues
Key Gaps
Limitations and Conclusion
References
Authors bios

Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation / 2403.14706 / ISBN:https://doi.org/10.48550/arXiv.2403.14706 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Methodology
Data
Results
Conclusion
Web Appendix A: Analysis of the Disinformation Manipulations

The Journey to Trustworthy AI- Part 1 Pursuit of Pragmatic Frameworks / 2403.15457 / ISBN:https://doi.org/10.48550/arXiv.2403.15457 / Published by ArXiv / on (web) Publishing site

Abstract
1 Context
2 Trustworthy AI Too Many Definitions or Lack Thereof?
3 Complexities and Challenges
4 AI Regulation: Current Global Landscape
5 Risk
6 Bias and Fairness
7 Explainable AI as an Enabler of Trustworthy AI
8 Implementation Framework
9 A Few Suggestions for a Viable Path Forward
10 Summary and Next Steps
11 About the Authors
A Appendix
References

Analyzing Potential Solutions Involving Regulation to Escape Some of AI's Ethical Concerns / 2403.15507 / ISBN:https://doi.org/10.48550/arXiv.2403.15507 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Various AI Ethical Concerns
A Possible Solution to These Concerns With Business Self-Regulation
Feasibility of Business Self-Regulation
A Possible Solution to These Concerns With Government Regulation
Feasibility of Government Regulation
Conclusion
References

The Pursuit of Fairness in Artificial Intelligence Models A Survey / 2403.17333 / ISBN:https://doi.org/10.48550/arXiv.2403.17333 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Survey
3 Conceptualizing Fairness and Bias in ML
4 Practical cases of unfairness in real-world setting
5 Ways to mitigate bias and promote Fairness
6 How Users can be affected by unfair ML Systems
7 Challenges and Limitations
8 Conclusion
References

Domain-Specific Evaluation Strategies for AI in Journalism / 2403.17911 / ISBN:https://doi.org/10.48550/arXiv.2403.17911 / Published by ArXiv / on (web) Publishing site

1 Motivation
2 Existing AI Evaluation Approaches
3 Blueprints for AI Evaluation in Journalism
References

Power and Play Investigating License to Critique in Teams AI Ethics Discussions / 2403.19049 / ISBN:https://doi.org/10.48550/arXiv.2403.19049 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction and Related Work
2 Methods
3 RQ1: What Factors Influence Members’ “Licens to Critique” when Discussing AI Ethics with their Team?
4 RQ2: How Do AI Ethics Discussions Unfold while Playing a Game Oriented toward Speculative Critique?
5 Discussion
6 Conclusion
References

Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness / 2403.20089 / ISBN:https://doi.org/10.48550/arXiv.2403.20089 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Non-discrimination law vs. algorithmic fairness
3 Implications of the AI Act
4 Practical challenges for compliance
5 Conclusion
References

AI Act and Large Language Models (LLMs): When critical issues and privacy impact require human and ethical oversight / 2404.00600 / ISBN:https://doi.org/10.48550/arXiv.2404.00600 / Published by ArXiv / on (web) Publishing site

Abstract
3. The definition of artificial intelligence systems
4. AI Act and high-risk systems
5. Human Oversight
6. Large Language Models (LLMs) - Introduction
7. Artificial intelligence Liability
8. Conclusions
9. References

Exploring the Nexus of Large Language Models and Legal Systems: A Short Survey / 2404.00990 / ISBN:https://doi.org/10.48550/arXiv.2404.00990 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Applications of Large Language Models in Legal Tasks
3 Fine-Tuned Large Language Models in Various Countries and Regions
4 Legal Problems of Large Languge Models
6 Conclusion and Future Directions
References

A Review of Multi-Modal Large Language and Vision Models / 2404.01322 / ISBN:https://doi.org/10.48550/arXiv.2404.01322 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 What is a Language Model?
3 Proprietary vs. Open Source LLMs
4 Specific Large Language Models
5 Vision Models and Multi-Modal Large Language Models
6 Model Tuning
7 Model Evaluation and Benchmarking
8 Conclusions
References

Balancing Progress and Responsibility: A Synthesis of Sustainability Trade-Offs of AI-Based Systems / 2404.03995 / ISBN:https://doi.org/10.48550/arXiv.2404.03995 / Published by ArXiv / on (web) Publishing site

II. Background and Related Work
III. Study Design
IV. Results
V. Discussion
VII. Conclusion
References

Designing for Human-Agent Alignment: Understanding what humans want from their agents / 2404.04289 / ISBN:https://doi.org/10.1145/3613905.3650948 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
4 Findings
5 Discussion
6 Limitations
References

Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage / 2404.06077 / ISBN:https://doi.org/10.48550/arXiv.2404.06077 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Preliminaries
III. Proposed Design: IBIS
IV. Detailed Construction
V. Implementation on DAML
VI. Evaluation
References

Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of Generative Agents / 2404.06750 / ISBN:https://arxiv.org/abs/2404.06750 / Published by ArXiv / on (web) Publishing site

Introduction
A Primer
Polarised Responses
Rebooting Machine Ethics
Generative Agents in Society
Conclusion
References

Safeguarding Marketing Research: The Generation, Identification, and Mitigation of AI-Fabricated Disinformation / 2403.14706 / ISBN:https://doi.org/10.48550/arXiv.2403.14706 / Published by ArXiv / on (web) Publishing site

Bibliography

The Pursuit of Fairness in Artificial Intelligence Models A Survey / 2403.17333 / ISBN:https://doi.org/10.48550/arXiv.2403.17333 / Published by ArXiv / on (web) Publishing site

A Appendices

Ethical Implications of ChatGPT in Higher Education: A Scoping Review / 2311.14378 / ISBN:https://doi.org/10.48550/arXiv.2311.14378 / Published by ArXiv / on (web) Publishing site

Authors

A Critical Survey on Fairness Benefits of Explainable AI / 2310.13007 / ISBN:https://doi.org/10.1145/3630106.3658990 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Methodology
4 Critical Survey
5 Three Patterns of Critique
6 Conclusion and Outlook
References
A Methodologies of Surveyed Literature
B References for Archetypal Claims

AI Alignment: A Comprehensive Survey / 2310.19852 / ISBN:https://doi.org/10.48550/arXiv.2310.19852 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Learning from Feedback
3 Learning under Distribution Shift
4 Assurance
5 Governance
6 Conclusion
Acknowledgments
References

Regulating AI-Based Remote Biometric Identification. Investigating the Public Demand for Bans, Audits, and Public Database Registrations / 2401.13605 / ISBN:https://doi.org/10.48550/arXiv.2401.13605 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Need for Governance of AI
3 Remote Biometric Identification and the AI Act
4 Public Opinion on AI Governance
5 Research Questions
6 Results
7 Discussion
Ethics Statement
Declaration of Generative AI in Scientific Writing
References
Appendix

Generative Ghosts: Anticipating Benefits and Risks of AI Afterlives / 2402.01662 / ISBN:https://doi.org/10.48550/arXiv.2402.01662 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Generative Ghosts: A Design Space
4 Benefits and Risks of Generative Ghost
5 Discussion
6 Conclusion
References

Epistemic Power in AI Ethics Labor: Legitimizing Located Complaints / 2402.08171 / ISBN:https://doi.org/10.1145/3630106.3658973 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Lower Status of Ethics Work within AI Cultures
3 Automated Model Cards: Legitimacy via Quantified Objectivity
4 Grey Skin as Technofix: Failures to Lodge Located Complaints
6 Conclusions: Towards Humble Technical Practices
Acknowledgments
References

On the role of ethics and sustainability in business innovation / 2404.07678 / ISBN:https://doi.org/10.48550/arXiv.2404.07678 / Published by ArXiv / on (web) Publishing site

Abstract
Background
Ethical considera5ons
Sustainability considera5ons
Recommenda5ons
Conclusion
About the authors

PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models / 2404.08699 / ISBN:https://doi.org/10.48550/arXiv.2404.08699 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 Methodology
4 Evaluation
5 Conclusion
References
A Dataset Filtering Prompts

Detecting AI Generated Text Based on NLP and Machine Learning Approaches / 2404.10032 / ISBN:https://doi.org/10.48550/arXiv.2404.10032 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Literature Review
III. Proposed Methodology
V. Conclusion
References

Debunking Robot Rights Metaphysically, Ethically, and Legally / 2404.10072 / ISBN:https://doi.org/10.48550/arXiv.2404.10072 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
3 The Robots at Issue
4 The Machines Like us Argument: Mistaking the Map for the Territory
5 Embodied Enactive (Post-Cartesian) Perspectives on Cognition
6 Posthumanism
7 The Legal Perspective
8 The Troubling Implications of Legal Rationales for Robot Rights
9 The Enduring Irresponsibility of AI Rights Talk
Notes
References

Characterizing and modeling harms from interactions with design patterns in AI interfaces / 2404.11370 / ISBN:https://doi.org/10.48550/arXiv.2404.11370 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background & Related Work
3 Scoping Review of Design Patterns, Affordances, and Harms in AI Interfaces
4 DECAI: Design-Enhanced Control of AI Systems
5 Case Studies
6 Discussion
References

Taxonomy to Regulation: A (Geo)Political Taxonomy for AI Risks and Regulatory Measures in the EU AI Act / 2404.11476 / ISBN:https://doi.org/10.48550/arXiv.2404.11476 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 EU Public Policy Analysis
3 A Geo-Political AI Risk Taxonomy
4 European Union Artificial Intelligence Act
5 Conclusion
References

Just Like Me: The Role of Opinions and Personal Experiences in The Perception of Explanations in Subjective Decision-Making / 2404.12558 / ISBN:https://doi.org/10.48550/arXiv.2404.12558 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Method
3 Results
4 Discussin and Implications
6 Conclusion & Future Work
References

Large Language Model Supply Chain: A Research Agenda / 2404.12736 / ISBN:https://doi.org/10.48550/arXiv.2404.12736 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
3 LLM Infrastructure
4 LLM Lifecycle
5 Downstream Ecosystem
6 Conclusion
References

The Necessity of AI Audit Standards Boards / 2404.13060 / ISBN:https://doi.org/10.48550/arXiv.2404.13060 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Audit the process, not just the product
3 3 Governance for safety
4 4 Auditing standards body, not standard audits
5 Conclusion
References

Modeling Emotions and Ethics with Large Language Models / 2404.13071 / ISBN:https://doi.org/10.48550/arXiv.2404.13071 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Qualifying and Quantifying Emotions
3 Case Study #1: Linguistic Features of Emotion
4 Qualifying and Quantifying Ethics
5 Concluding Remarks
References

From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap / 2404.13131 / ISBN:https://doi.org/10.1145/3630106.3658951 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Disentangling Replicability of Model Performance Claiim and Replicability of Social Claim
3 How Claim Replicability Helps Bridge the Responsiblity Gap
4 Claim Replicability's Practical Implication
5 Concluding Remarks
References

A Practical Multilevel Governance Framework for Autonomous and Intelligent Systems / 2404.13719 / ISBN:https://doi.org/10.48550/arXiv.2404.13719 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Comprehensive Governance of Emerging Technologies
III. A Practical Multilevel Governance Framework for AIs
IV. Application of the Framework for the Development of AIs
V. Conclusion
References

Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis / 2404.13861 / ISBN:https://doi.org/10.48550/arXiv.2404.13861 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Mechanistic Agency: A Common View in AI Practice
3 Volitional Agency: an Alternative Approach
4 Alternatives to AI as Agent
5 A Final Note
References

Designing Safe and Engaging AI Experiences for Children: Towards the Definition of Best Practices in UI/UX Design / 2404.14218 / ISBN:https://doi.org/10.48550/arXiv.2404.14218 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Towards Ethical and Engaging AI Interfaces for Children: a Comprehensive Framework
References

AI Procurement Checklists: Revisiting Implementation in the Age of AI Governance / 2404.14660 / ISBN:https://doi.org/10.48550/arXiv.2404.14660 / Published by ArXiv / on (web) Publishing site

Abstract
1 Technical assessments require an AI expert to complete — and we don’t have enough experts
2 Procurement Loopholes Exist
3 Substantive and Procedural Transparency are Necessary for Deploying Effective and Ethical AI systems
4 Building Towards Better Governance of Government AI

Who Followed the Blueprint? Analyzing the Responses of U.S. Federal Agencies to the Blueprint for an AI Bill of Rights / 2404.19076 / ISBN:https://doi.org/10.48550/arXiv.2404.19076 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Findings
Acknowledgments
References

Fairness in AI: challenges in bridging the gap between algorithms and law / 2404.19371 / ISBN:https://doi.org/10.48550/arXiv.2404.19371 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Discrimination in Law
III. Prevalent Algorithmic Fairness Definitions
IV. Criteria for the Selection of Fairness Methods
V. Discussion
References

War Elephants: Rethinking Combat AI and Human Oversight / 2404.19573 / ISBN:https://doi.org/10.48550/arXiv.2404.19573 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Lessons from History: War Elephants
4 Discussion
5 Conclusions
References

Not a Swiss Army Knife: Academics' Perceptions of Trade-Offs Around Generative Artificial Intelligence Use / 2405.00995 / ISBN:https://doi.org/10.48550/arXiv.2405.00995 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Method
4 Findings
5 Discussion
6 Limitations and Future Research
7 Conclusion
References

Towards an Ethical and Inclusive Implementation of Artificial Intelligence in Organizations: A Multidimensional Framework / 2405.01697 / ISBN:https://doi.org/10.48550/arXiv.2405.01697 / Published by ArXiv / on (web) Publishing site

Abstract
1 Technocriticism and Key Actors in the Age of AI
2 How can organizations participate
3 Four Pillars for Implementing an Ethical Framework in Organizations
4 Conclusions

A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law / 2405.01769 / ISBN:https://doi.org/10.48550/arXiv.2405.01769 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Surveys
3 Finance
4 Medicine and Healthcar
5 Law
6 Ethics
7 Conclusion
References

AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research / 2405.01859 / ISBN:https://doi.org/10.48550/arXiv.2405.01859 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Current State of AWS
3. AWS Proliferation and Threats to Academic Research
4. Policy Recommendations
Impact Statement
References

Responsible AI: Portraits with Intelligent Bibliometrics / 2405.02846 / ISBN:https://doi.org/10.48550/arXiv.2405.02846 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Conceptualization: Responsible AI
III. Data and Methodology
IV. Bibliometric Portraits of Responsible AI
V. Discussion and Conclusions
References
Authors

Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines / 2405.03153 / ISBN:https://doi.org/10.48550/arXiv.2405.03153 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
4 Results
5 Discussion
6 Conclusion
References

Organizing a Society of Language Models: Structures and Mechanisms for Enhanced Collective Intelligence / 2405.03825 / ISBN:https://doi.org/10.48550/arXiv.2405.03825 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Motivation
3 Proposed Organizational Forms
4 Interaction Mechanisms
5 Governance and Organization
6 Unified Legal Framework
7 Conclusion
References

A Fourth Wave of Open Data? Exploring the Spectrum of Scenarios for Open Data and Generative AI / 2405.04333 / ISBN:https://doi.org/10.48550/arXiv.2405.04333 / Published by ArXiv / on (web) Publishing site

Glossary of Terms
Executive Summary
1. Introduction
2. Methodology
3. A Spectrum of Scenarios of Open Data for Generative AI
4. Open Data Requirements And Diagnostic
5. Recommendations for Advancing Open Data in Generative AI
Appendix

Guiding the Way: A Comprehensive Examination of AI Guidelines in Global Media / 2405.04706 / ISBN:https://doi.org/10.48550/arXiv.2405.04706 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Theoretical Framework
3 Data and Methods
4 Results
5 Discussion and conclusions
References

Trustworthy AI-Generative Content in Intelligent 6G Network: Adversarial, Privacy, and Fairness / 2405.05930 / ISBN:https://doi.org/10.48550/arXiv.2405.05930 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Trustworthy AIGC in 6G Network
IV. Privacy of AIGC in 6G Network
V. Fairness of AIGC in 6G Network
VIII. Conclusion

Towards ethical multimodal systems / 2304.13765 / ISBN:https://doi.org/10.48550/arXiv.2304.13765 / Published by ArXiv / on (web) Publishing site

References

RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Roles / 2307.15158 / ISBN:https://doi.org/10.48550/arXiv.2307.15158 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
4 Method for Generating Responsible AI Guidelines
5 Evaluation of the 22 Responsible AI Guidelines
6 Discussion
7 Conclusion
References
B Mapping Guidelines with EU AI Act Articles

Redefining Qualitative Analysis in the AI Era: Utilizing ChatGPT for Efficient Thematic Analysis / 2309.10771 / ISBN:https://doi.org/10.48550/arXiv.2309.10771 / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methods
4 Users’ Experiences and Challenges with ChatGPT
5 Analyses of the Design Process
7 Discussion
8 Limitations and Future Work
9 Conclusion
References

XXAI: Towards eXplicitly eXplainable Artificial Intelligence / 2401.03093 / ISBN:https://doi.org/10.48550/arXiv.2401.03093 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Main hypothesis
3. Overcoming the barriers to widespread use of symbolic AI
4. Discussion of the problems of symbolic AI and ways to overcome them
5. Conclusions and prospects
References

Should agentic conversational AI change how we think about ethics? Characterising an interactional ethics centred on respect / 2401.09082 / ISBN:https://doi.org/10.48550/arXiv.2401.09082 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Evaluating a system as a social actor
Social-interactional harms
Design implications for LLM agents
Informing existing HCI approaches
Conclusion
References

Unsocial Intelligence: an Investigation of the Assumptions of AGI Discourse / 2401.13142 / ISBN:https://doi.org/10.48550/arXiv.2401.13142 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Between Human Intelligence and Technology: AGI’s Dual Value-Laden Pedigrees
3 The Motley Choices of AGI Discourse
4 Towards Contextualized, Politically Legitimate, and Social Intelligence
5 Conclusion: Politically Legitimate Intelligence
Acknowledgments
References
A Dimensions of AGI: a Summary

Not My Voice! A Taxonomy of Ethical and Safety Harms of Speech Generators / 2402.01708 / ISBN:https://doi.org/10.48550/arXiv.2402.01708 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Overview of Speech Generation
6 Taxonomy of Harms
7 Discussion
8 Conclusion
Acknowledgments
References
A Appendix

The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative / 2402.14859 / ISBN:https://doi.org/10.48550/arXiv.2402.14859 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related Work
3. Methodology
4. Experiments
References

Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback / 2404.10271 / ISBN:https://doi.org/10.48550/arXiv.2404.10271 / Published by ArXiv / on (web) Publishing site

1. Introduction
2. Background
3. What Are the Collective Decision Problems and their Alternatives in this Context?
4. Who Provides the Human Feedback?
5. What Is the Format of Human Feedback?
6. How Do We Incorporate Diverse Individual Feedback?
7. Which Traditional Social-Choice-Theoretic Concepts Are Most Relevant?
10. Conclusion
Impact Statement
References

A scoping review of using Large Language Models (LLMs) to investigate Electronic Health Records (EHRs) / 2405.03066 / ISBN:https://doi.org/10.48550/arXiv.2405.03066 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Materials
3 Results
4 Discussion
5 Conclusions
Appendix
References

Integrating Emotional and Linguistic Models for Ethical Compliance in Large Language Models / 2405.07076 / ISBN:https://doi.org/10.48550/arXiv.2405.07076 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Quantitative Models of Emotions, Behaviors, and Ethics
4 Pilot Studies
5 Conclusion
Limitations
References
Appendix S: Multiple Adversarial LLMs
Appendix A: Wheels of Emotions
Appendix B: Polarized Emotions in One Article
Appendix D: Complex Emotions
Appendix E: “To My Sister” of Different Linguistic Behaviors

Using ChatGPT for Thematic Analysis / 2405.08828 / ISBN:https://doi.org/10.48550/arXiv.2405.08828 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Coding in Thematic Analysis: Manual vs GPT-driven Approaches
3 Pilot-testing: UN Policy Documents Thematic Analysis Supported by GPT
4 Validation Using Topic Modeling
5 Discussion and Limitations
6 OpenAI Updates on Policies and Model Capabilities: Implications for Thematic Analysis
7 Conclusion
8 Acknowledgments
9 Appendix
References

When AI Eats Itself: On the Caveats of Data Pollution in the Era of Generative AI / 2405.09597 / ISBN:https://doi.org/10.48550/arXiv.2405.09597 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 RQ1: What Happens When AI Eats Itself ?
3 RQ2: What Technical Strategies Can Be Employed to Mitigate the Negative Consequences of AI Autophagy?
4 RQ3: Which Regulatory Strategies Can Be Employed to Address These Negative Consequences?
5 Conclusions and Outlook
7 References

Cyber Risks of Machine Translation Critical Errors : Arabic Mental Health Tweets as a Case Study / 2405.11668 / ISBN:https://doi.org/10.48550/arXiv.2405.11668 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2.MT Critical Errors
3. Data Compiling and Annotation
4.Error Analysis
5.Quality Metrics Performance
6. Conclusion
7. Bibliographical References

The Narrow Depth and Breadth of Corporate Responsible AI Research / 2405.12193 / ISBN:https://doi.org/10.48550/arXiv.2405.12193 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Literature on Industry’s Engagement in Responsible AI Research
3 Motivations for Industry to Engage in Responsible AI Research
4 The Narrow Depth of Industry’s Responsible AI Research
5 The Narrow Breadth of Industry’s Responsible AI Research
6 Limited Adoption of Responsible AI Research in Commercialization: Patent Citation Analysis
7 Discussion
8 Conclusion
References
S1 Additional Analyses on Engagement Analysis
S2 Additional Analyses on Linguistic Analysis

Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems / 2405.13191 / ISBN:https://doi.org/10.48550/arXiv.2405.13191 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 The Audit Procedure
4 Conducting the Pilots
6 Conclusion and Outlook
References
B The Different Types of Auditing
C The Risk Assessment Database
D Lifecycle Mapping of Pilot 1
E Lifecycle Mapping of Pilot 2: The GARMI Vision Module

A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions / 2405.14487 / ISBN:https://doi.org/10.48550/arXiv.2405.14487 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Threat Intelligence
III. Vulnerability Assessment
IV. Network Security
V. Privacy Preservation
VI. Awareness
VII. Cyber Security Operations Automation
VIII. Ethical LLMs
IX. Challenges and Open Problems
X. Conclusions
References

Towards Clinical AI Fairness: Filling Gaps in the Puzzle / 2405.17921 / ISBN:https://doi.org/10.48550/arXiv.2405.17921 / Published by ArXiv / on (web) Publishing site

Abstract
Main
Results
Methods in clinical AI fairness research
Discussion
Conclusion
Methods
Author Contributions
Reference
Additional material

The ethical situation of DALL-E 2 / 2405.19176 / ISBN:https://doi.org/10.48550/arXiv.2405.19176 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Understanding what can DALL-E 2 actually do
3 Current and potential future use of it
4 Following the RRI, (Responsible research innovation) principles
5 Technology and society, a complex relationship
6 Technological mediation
7 Conclusion
References

The Future of Child Development in the AI Era. Cross-Disciplinary Perspectives Between AI and Child Development Experts / 2405.19275 / ISBN:https://doi.org/10.48550/arXiv.2405.19275 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Anticipated AI Use for Children
3. Discussion
4. Conclusion
Bibliography
Appendix 1: Experts Consulted & Acknowledgements

Using Large Language Models for Humanitarian Frontline Negotiation: Opportunities and Considerations / 2405.20195 / ISBN:https://doi.org/10.48550/arXiv.2405.20195 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related Work
5. Interview Results: Opportunities and Concerns of Using LLMs in the Frontline
6. Discussion
References
A. Appendix

There and Back Again: The AI Alignment Paradox / 2405.20806 / ISBN:https://doi.org/10.48550/arXiv.2405.20806 / Published by ArXiv / on (web) Publishing site

Abstract
Paper
References

Responsible AI for Earth Observation / 2405.20868 / ISBN:https://doi.org/10.48550/arXiv.2405.20868 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Mitigating (Unfair) Bias
3 Secure AI in EO: Focusing on Defense Mechanisms, Uncertainty Modeling and Explainability
4 Geo-Privacy and Privacy-preserving Measures
5 Maintaining Scientific Excellence, Open Data, and Guiding AI Usage Based on Ethical Principles in EO
6 AI&EO for Social Good
7 Responsible AI Integration in Business Innovation and Sustainability
8 Conclusions, Remarks and Future Directions
References

Gender Bias Detection in Court Decisions: A Brazilian Case Study / 2406.00393 / ISBN:https://doi.org/10.48550/arXiv.2406.00393 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Framework
4 Discussion
5 Final Remarks
References
A DVC Dataset: Domestic Violence Cases
B PAC Dataset: Parental Alienation Cases
C Biases

Transforming Computer Security and Public Trust Through the Exploration of Fine-Tuning Large Language Models / 2406.00628 / ISBN:https://doi.org/10.48550/arXiv.2406.00628 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background, Foundational Studies, and Discussion:
3 Experimental Design, Overview, and Discussion
4 Comparative Analysis of Pre-Trained Models.
5 Discussion and further research

How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences of LLMs / 2406.01168 / ISBN:https://doi.org/10.48550/arXiv.2406.01168 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
I. Description of Method/Empirical Design
II. Risk Characteristics of LLMs
III. Impact of Alignment on LLMs’ Risk Preferences
IV. Impact of Alignments on Corporate Investment Forecasts
V. Robustness: Transcript Readability and Investment Score Predictability
VI. Conclusions
References
Figures and tables

Evaluating AI fairness in credit scoring with the BRIO tool / 2406.03292 / ISBN:https://doi.org/10.48550/arXiv.2406.03292 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Preliminary Analysis
3 ML model construction
4 Fairness violation analysis in BRIO
5 Risk assessment in BRIO
6 Risk analysis via BRIO for the German Credit Dataset
7 Revenue analysis
References

Promoting Fairness and Diversity in Speech Datasets for Mental Health and Neurological Disorders Research / 2406.04116 / ISBN:https://doi.org/10.48550/arXiv.2406.04116 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. A Case Study on DAIC-WoZ Depression Research
3. Related Work
4. Desiderata
5. Methodology
6. Discussion
7. Conclusions
References
Appendix A. Terminology
Appendix B. Retrieval Methodology

MoralBench: Moral Evaluation of LLMs / 2406.04428 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Benchmark and Method
4 Experiments
5 Conclusion
References
Appendix

Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models / 2406.05602 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related Work
3. Bias Evaluation
4. Methodology
5. Results
6. Discussion
7. Conclusion
References

Deception Analysis with Artificial Intelligence: An Interdisciplinary Perspective / 2406.05724 / ISBN:https://doi.org/10.48550/arXiv.2406.05724 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 Theories and Components of Deception
3 Reductionism & Previous Research in Deceptive AI
4 DAMAS: A MAS Framework for Deception Analysis
5 Conclusion
References

The Impact of AI on Academic Research and Publishing / 2406.06009 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Ethics of AI for Writing Papers
AI in Editorial Processes
Conclusion

An Empirical Design Justice Approach to Identifying Ethical Considerations in the Intersection of Large Language Models and Social Robotics / 2406.06400 / ISBN:https://doi.org/10.48550/arXiv.2406.06400 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Theoretical Background
3 Methodology
4 Findings
5 Discussion
6 Conclusions and Recommendations
References
Appendix A: Interview questions
Appendix B: Collected data summary

The Ethics of Interaction: Mitigating Security Threats in LLMs / 2401.12273 / ISBN:https://doi.org/10.48550/arXiv.2401.12273 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Why Ethics Matter in LLM Attacks?
3 Potential Misuse and Security Concerns
4 Towards Ethical Mitigation: A Proposed Methodology
5 Preemptive Ethical Measures
6 Ethical Response to LLM Attacks
References

Global AI Governance in Healthcare: A Cross-Jurisdictional Regulatory Analysis / 2406.08695 / ISBN:https://doi.org/10.48550/arXiv.2406.08695 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Material and Methods
4 Global Regulatory Landscape of AI
5 Generative AI: The New Frontier
6 Results and Conclusion
7 Future Directions
Ethical Statement
Acknowledgments
References
A Supplemental Tables

Fair by design: A sociotechnical approach to justifying the fairness of AI-enabled systems across the lifecycle / 2406.09029 / ISBN:https://doi.org/10.48550/arXiv.2406.09029 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Fairness and AI
3 Assuring fairness across the AI lifecycle
4 Assuring AI fairness in healthcare
5 Conclusion
References

Some things never change: how far generative AI can really change software engineering practice / 2406.09725 / ISBN:https://doi.org/10.48550/arXiv.2406.09725 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and related work
3 Methodology
4 Results
6 Conclusions and future work
REFERENCES

Federated Learning driven Large Language Models for Swarm Intelligence: A Survey / 2406.09831 / ISBN:https://doi.org/10.48550/arXiv.2406.09831 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Foundations and Integration of SI and LLM
III. Federated LLMs for Smarm Intelligence
IV. Learned Lessons and Open Challenges
References

Applications of Generative AI in Healthcare: algorithmic, ethical, legal and societal considerations / 2406.10632 / ISBN:https://doi.org/10.48550/arXiv.2406.10632 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Selection of application
III. Analysis
IV. Conclusion
References
Aappendix A Societal aspects
Appendix B Legal aspects
Appendix C Algorithmic / technical aspects

Justice in Healthcare Artificial Intelligence in Africa / 2406.10653 / ISBN:https://doi.org/10.48550/arXiv.2406.10653 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
1. Beyond Bias and Fairness
2. Bridging the Justice Gap
3. Ensuring Equitable Access to AI Technologies
4. Prioritizing the Common Good Over Corporate Greed
5. Promoting Global Solidarity
6. Ensuring Sustainable AI Development
7. Addressing Bias and Enforcing Fairness
Conclusion
References

Conversational Agents as Catalysts for Critical Thinking: Challenging Design Fixation in Group Design / 2406.11125 / ISBN:https://doi.org/10.48550/arXiv.2406.11125 / Published by ArXiv / on (web) Publishing site

Abstract
1 INTRODUCTION
2 BEYOND RECOMMENDATIONS: ENHANCING CRITICAL THINKING WITH GENERATIVE AI
3 CHALLENGES AND OPPORTUNITIES OF USING CONVERSATIONAL AGENTS IN GROUP DESIGN
4 POTENTIAL SCENARIO AND APPLICATIONS OF CONVERSATIONAL AGENTS IN GROUP DESIGN PROCESS
5 BALANCING CRITICAL THINKING WITH DESIGNER SATISFACTION AND MOTIVATION
6 POTENTIAL DESIGN CONSIDERATIONS
7 CONCLUSION
REFERENCES

Current state of LLM Risks and AI Guardrails / 2406.12934 / ISBN:https://doi.org/10.48550/arXiv.2406.12934 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Large Language Model Risks
4 Challenges in Implementing Guardrails
5 Open Source Tools
7 Conclusion
References

Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health / 2406.13659 / ISBN:https://doi.org/10.48550/arXiv.2406.13659 / Published by ArXiv / on (web) Publishing site

Abstract
I. INTRODUCTION
II. RECENT ADVANCEMENTS IN LARGE LANGUAGE MODELS
III. CASE STUDIES : APPLICATIONS OF LLM S IN PATIENT ENGAGEMENT
IV. DISCUSSION AND F UTURE D IRECTIONS
V. CONCLUSION
REFERENCES

Documenting Ethical Considerations in Open Source AI Models / 2406.18071 / ISBN:https://doi.org/10.48550/arXiv.2406.18071 / Published by ArXiv / on (web) Publishing site

Abstract
1 INTRODUCTION
2 RELATED WORK
3 METHODOLOGY AND STUDY DESIGN
4 RESULTS
5 DISCUSSION AND IMPLICATIONS
6 THREATS TO VALIDITY
7 CONCLUSIONS
8 ACKNOWLEDGEMENTS
REFERENCES

AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations / 2406.18346 / ISBN:https://doi.org/10.48550/arXiv.2406.18346 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Limitations of RLxF
4 The Internal Tensions and Ethical Issues in RLxF
5 Rebooting Safety and Alignment: Integrating AI Ethics and System Safety
6 Conclusion
References

A Survey on Privacy Attacks Against Digital Twin Systems in AI-Robotics / 2406.18812 / ISBN:https://doi.org/10.48550/arXiv.2406.18812 / Published by ArXiv / on (web) Publishing site

Abstract
I. INTRODUCTION AND MOTIVATION
II. BACKGROUND
III. ATTACKS ON DT-INTEGRATED AI ROBOTS
IV. DT-INTEGRATED ROBOTICS DESIGN CONSIDERATIONS AND DISCUSSION
V. CONCLUSION
ACKNOWLEDGEMENTS
REFERENCES

Staying vigilant in the Age of AI: From content generation to content authentication / 2407.00922 / ISBN:https://doi.org/10.48550/arXiv.2407.00922 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Art Practice: Human Reactions to Synthetic Fake Content
Emphasizing Reasoning Over Detection
Conclusions and Future Works
References

SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest / 2407.01110 / ISBN:https://doi.org/10.48550/arXiv.2407.01110 / Published by ArXiv / on (web) Publishing site

Abstract
I. INTRODUCTION
II. UNDERSTANDING GENAI SECURITY
III. CRITICAL ANALYSIS
IV. SECGENAI FRAMEWORK REQUIREMENTS SPECIFICATIONS
V. DISCUSSIONS AND RECOMMENDATIONS
REFERENCES

Artificial intelligence, rationalization, and the limits of control in the public sector: the case of tax policy optimization / 2407.05336 / ISBN:https://doi.org/10.48550/arXiv.2407.05336 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Artificial intelligence as Weberian rationalization
3. Bureaucratization, tax policy, and equality
4. AI-driven tax policy to reduce economic inequality: a thought experiment
5. Freedom, equality, and self-determination in the iron cage
6. Conclusion
References

A Blueprint for Auditing Generative AI / 2407.05338 / ISBN:https://doi.org/10.48550/arXiv.2407.05338 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Why audit generative AI systems?
3 How to audit generative AI systems?
4 Governance audits
5 Model audits
6 Application audits
7 Clarifications and limitations
8 Conclusion
Bibliography

Challenges and Best Practices in Corporate AI Governance:Lessons from the Biopharmaceutical Industry / 2407.05339 / ISBN:https://doi.org/10.48550/arXiv.2407.05339 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction | The need for corporate AI governance
2 Case study | AstraZeneca’s AI governance journey
3 Practical implementation challenges | What to be prepared for?
4 Discussion | Best practices and lessons learned
5 Concluding remarks | Upfront investments vs. long-term benefits
6 References

Operationalising AI governance through ethics-based auditing: An industry case study / 2407.06232 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. The need to operationalise AI governance
3. AstraZeneca and AI governance
4. An ‘ethics-based’ AI audit
5. Methodology: An industry case study
6. Lessons learned from AstraZeneca’s 2021 AI audit
7. Limitations
8. Conclusions
REFERENCES
APPENDIX 1

Auditing of AI: Legal, Ethical and Technical Approaches / 2407.06235 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The evolution of auditing as a governance mechanism
3 The need to audit AI systems – a confluence of top-down and bottom-up pressures
4 Auditing of AI’s multidisciplinary foundations
5 In this topical collection
6 Concluding remarks
References

Why should we ever automate moral decision making? / 2407.07671 / ISBN:https://doi.org/10.48550/arXiv.2407.07671 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Reasons for automated moral decision making
3 Conclusion
References

Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation / 2402.12590 / ISBN:https://doi.org/10.48550/arXiv.2402.12590 / Published by ArXiv / on (web) Publishing site

D. Results for Claude 3

Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models / 2310.19917 / ISBN:https://doi.org/10.48550/arXiv.2310.19917 / Published by ArXiv / on (web) Publishing site

References

Potential Societal Biases of ChatGPT in Higher Education: A Scoping Review / 2311.14381 / ISBN:https://doi.org/10.48550/arXiv.2311.14381 / Published by ArXiv / on (web) Publishing site

REFERENCES

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare / 2309.12325 / ISBN:https://doi.org/10.48550/arXiv.2309.12325 / Published by ArXiv / on (web) Publishing site

REFERENCES:
Table 1
Table 2

A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics / 2310.05694 / ISBN:https://doi.org/10.48550/arXiv.2310.05694 / Published by ArXiv / on (web) Publishing site

AUTHORS

Rethinking Machine Ethics -- Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? / 2308.15399 / ISBN:https://doi.org/10.48550/arXiv.2308.15399 / Published by ArXiv / on (web) Publishing site

A Details of Datasets
B Details of Instructions
C Experimental Details

PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models / 2404.08699 / ISBN:https://doi.org/10.48550/arXiv.2404.08699 / Published by ArXiv / on (web) Publishing site

F Additional Figures

Bridging the Global Divide in AI Regulation: A Proposal for a Contextual, Coherent, and Commensurable Framework / 2303.11196 / ISBN:https://doi.org/10.48550/arXiv.2303.11196 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Global Divide in AI Regulation: Horizontally. Context-Specific
III. Striking a Balance Betweeen the Two Approaches
IV. Proposing an Alternative 3C Framework
V. Conclusion

CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomics / 2407.02885 / ISBN:https://doi.org/10.48550/arXiv.2407.02885 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 Conceptual Foundations
4 Design Framework
5 Case Studies
6 Discussion
7 Conclusion
Notes
References

Past, Present, and Future: A Survey of The Evolution of Affective Robotics For Well-being / 2407.02957 / ISBN:https://doi.org/10.48550/arXiv.2407.02957 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background and Definitions
III. Method
IV. Evolution of Affective Robots for Well-Being
V. 10 Years of Affectivbe Robotics
VI. Future Opportunities in Affective Robotivs for Well-Being
VII. Conclusion
References
Authors Bios

With Great Power Comes Great Responsibility: The Role of Software Engineers / 2407.08823 / ISBN:https://doi.org/10.48550/arXiv.2407.08823 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 Future Research Challenges
4 Conclusion
References

Have We Reached AGI? Comparing ChatGPT, Claude, and Gemini to Human Literacy and Education Benchmarks / 2407.09573 / ISBN:https://doi.org/10.48550/arXiv.2407.09573 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Literature Review
3 Methodology
4 Data Analysis and Results
5 Discussion
6 Conclusion
References

Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations / 2407.11054 / ISBN:https://doi.org/10.48550/arXiv.2407.11054 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
A brief history of AI and generative AI
Applications of generative AI in literature reviews and evidence synthesis
Applications of generative AI to real-world evidence (RWE):
Applications of generative AI to health economic modeling
Limitations of generative AI in HTA applications
Policy landscape
Conclusion
Glossary
Appendices
References

Thorns and Algorithms: Navigating Generative AI Challenges Inspired by Giraffes and Acacias / 2407.11360 / ISBN:https://doi.org/10.48550/arXiv.2407.11360 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Study Methodology: Narrative Review
3 Giraffe and Acacia: Reciprocal Adaptations and Shaping
4 Generative AI and Humans: Risks and Mitigation
5 Meta Analysis: Limits of the Analogy
6 Discussion
7 Recommendations: Fixing Gen AI’s Value Alignment
8 Conclusion
References

Prioritizing High-Consequence Biological Capabilities in Evaluations of Artificial Intelligence Models / 2407.13059 / ISBN:https://doi.org/10.48550/arXiv.2407.13059 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Proposed Approach to Determining High-Consequence Biological Capabilities of Concern
Next Steps for AI Biosecurity Evaluations
References

Report on the Conference on Ethical and Responsible Design in the National AI Institutes: A Summary of Challenges / 2407.13926 / ISBN:https://doi.org/10.48550/arXiv.2407.13926 / Published by ArXiv / on (web) Publishing site

Introduction
1. Organizing the National AI Institutes for Ethical and Responsible Design
2. Ethics Frameworks
3. AI Institutes and Society
4. Coordination between AI Institutes
Acknowledgements

Assurance of AI Systems From a Dependability Perspective / 2407.13948 / ISBN:https://doi.org/10.48550/arXiv.2407.13948 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction: Assurance for Traditional Systems
2 Assurance for Systems Extended with AI and ML
3 Assurance of AI Systems for Specific Functions
4 Assurance for General-Purpose AI
5 Assurance and Alignment for AGI
6 Summary and Conclusion
References

Open Artificial Knowledge / 2407.14371 / ISBN:https://doi.org/10.48550/arXiv.2407.14371 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Key Challenges of Artificial Data
3. OAK Dataset
5. Use Considerations
References
Appendices

Honest Computing: Achieving demonstrable data lineage and provenance for driving data and process-sensitive policies / 2407.14390 / ISBN:https://doi.org/10.48550/arXiv.2407.14390 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Threat Model for Honest Computing
3. Honest Computing reference specifications
4. Discussion
5. Conclusion
References

RogueGPT: dis-ethical tuning transforms ChatGPT4 into a Rogue AI in 158 Words / 2407.15009 / ISBN:https://doi.org/10.48550/arXiv.2407.15009 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background
III. Methodology
IV. Results
V. Benchmarking with Chat GPT4 Default Interface
VI. Discussion
VII. Conclusion
References

Nudging Using Autonomous Agents: Risks and Ethical Considerations / 2407.16362 / ISBN:https://doi.org/10.48550/arXiv.2407.16362 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Technology Mediated Nudging
3 Examples of Biases
4 Ethical Considerations
5 Principles for the Nudge Lifecycle
6 Conclusion
Acknowledgements
References

Mapping the individual, social, and biospheric impacts of Foundation Models / 2407.17129 / ISBN:https://doi.org/10.48550/arXiv.2407.17129 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Theoretical Lens: Expanding Views on Algorithmic Risks and Harms
3 Methods: Snowball and Structured Search
4 Mapping Individual, Social, and Biospheric Impacts of Foundation Models
5 Discussion: Grappling with the Scale and Interconnectedness of Foundation Models
6 Conclusion
Impact Statement
References
A Appendix

Navigating the United States Legislative Landscape on Voice Privacy: Existing Laws, Proposed Bills, Protection for Children, and Synthetic Data for AI / 2407.19677 / ISBN:https://doi.org/10.48550/arXiv.2407.19677 / Published by ArXiv / on (web) Publishing site

Abstract
2. American Privacy Rights Act of 2024
3. Children’s Privacy in the US
5. Regulations on Synthetic Data for AI

Interactive embodied evolution for socially adept Artificial General Creatures / 2407.21357 / ISBN:https://doi.org/10.48550/arXiv.2407.21357 / Published by ArXiv / on (web) Publishing site

Introduction
Artificial companions
Expected impacts

Exploring the Role of Social Support when Integrating Generative AI into Small Business Workflows / 2407.21404 / ISBN:https://doi.org/10.48550/arXiv.2407.21404 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methodology
4 Findings
5 Limitations
6 Discussion and Future Work
References
A Example Storyboards

Deepfake Media Forensics: State of the Art and Challenges Ahead / 2408.00388 / ISBN:https://doi.org/10.48550/arXiv.2408.00388 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Deepfake Detection
5. Deepfakes Detection Method on Realistic Scenarios
6. Active Authentication
VII. Conclusion
References

Integrating ESG and AI: A Comprehensive Responsible AI Assessment Framework / 2408.00965 / ISBN:https://doi.org/10.48550/arXiv.2408.00965 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Literature Review
3 Methodology
4 ESG-AI framework
5 Discussion
6 Conclusion
References

AI for All: Identifying AI incidents Related to Diversity and Inclusion / 2408.01438 / ISBN:https://doi.org/10.48550/arXiv.2408.01438 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 Methodology
4 Results
5 Discussion and Implications
7 Conclusions and Future Work
References

Surveys Considered Harmful? Reflecting on the Use of Surveys in AI Research, Development, and Governance / 2408.01458 / ISBN:https://doi.org/10.48550/arXiv.2408.01458 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methods
4 Large-Scale Surveys of AI in the Literature
5 Discussion
6 Conclusion
7 Research Ethics and Social Impact
References
A Known Limitations of Surveys
B Additional Materials for Pilot Survey

Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity / 2408.04023 / ISBN:https://doi.org/10.48550/arXiv.2408.04023 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Related Work
3. Proposed framework
4. Model architecture and training parameters
5. Model Training
6. Results
7. Conclusion and Future Directions
References

AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent / 2408.04281 / ISBN:https://doi.org/10.48550/arXiv.2408.04281 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related Work
III. Methodology
IV. Graphical User Interface (GUI)
VI. Conclusion
References

Criticizing Ethics According to Artificial Intelligence / 2408.04609 / ISBN:https://doi.org/10.48550/arXiv.2408.04609 / Published by ArXiv / on (web) Publishing site

Abstract
1 Preliminary notes
2 Clarifying conceptual ambiguities
3 Critical Reflection on AI Risks
4 Exploring epistemic challenges
5 Investigating fundamental normative issues
6 Outlook
Bibliography

Interactive embodied evolution for socially adept Artificial General Creatures / 2407.21357 / ISBN:https://doi.org/10.48550/arXiv.2407.21357 / Published by ArXiv / on (web) Publishing site

References

Between Copyright and Computer Science: The Law and Ethics of Generative AI / 2403.14653 / ISBN:https://doi.org/10.48550/arXiv.2403.14653 / Published by ArXiv / on (web) Publishing site

Introduction
I. The Why and How Behind LLMs
II. The Difference Between Academic and Commercial Research
III. A Guide for Data in LLM Research
IV. The Path Ahead

The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources / 2406.16746 / ISBN:https://doi.org/10.48550/arXiv.2406.16746 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
3 Data Sources
4 Data Preparation
6 Model Training
7 Environmental Impact
8 Model Evaluation
9 Model Release & Monitoring
References
A Contributions

Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives / 2407.14962 / ISBN:https://doi.org/10.48550/arXiv.2407.14962 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Generative AI
III. Language Modeling
IV. Challenges of Generative AI and LLMs
V. Bridging Research Gaps and Future Directions
References
Authors

VersusDebias: Universal Zero-Shot Debiasing for Text-to-Image Models via SLM-Based Prompt Engineering and Generative Adversary / 2407.19524 / ISBN:https://doi.org/10.48550/arXiv.2407.19524 / Published by ArXiv / on (web) Publishing site

Abstract
I Introduction
2 Related Works
3 Method
4 Experiment
5 Limitation and Future Work
References
Appendices

Speculations on Uncertainty and Humane Algorithms / 2408.06736 / ISBN:https://doi.org/10.48550/arXiv.2408.06736 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 The Numbers of the Future
3 Uncertainty Ex Machina
4 Conclusions
References

Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration / 2408.07483 / ISBN:https://doi.org/10.48550/arXiv.2408.07483 / Published by ArXiv / on (web) Publishing site

1 Introduction
2 Visualization Atlases : Examples and Collection
3 Visualization Atlas Design Patterns
4 Interviews with Visualization Atlas Creators
5 Visualization Atlases Genres
6 Key Characteristics of Visualization Atlases
7 Discussion
8 Conclusion
Acknowledgments
References

Neuro-Symbolic AI for Military Applications / 2408.09224 / ISBN:https://doi.org/10.48550/arXiv.2408.09224 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Neuro-Symbolic AI
III. Autonomy in Military Weapons Systems
IV. Military Applications of Neuro-Symbolic AI
V. Challenges and Risks
VI. Interpretability and Explainability
VII. Conclusion
References

Conference Submission and Review Policies to Foster Responsible Computing Research / 2408.09678 / ISBN:https://doi.org/10.48550/arXiv.2408.09678 / Published by ArXiv / on (web) Publishing site

Executive Summary
Introduction
Avoiding harm
Responsible disclosure of vulnerabilities
Ethics Board Review
Obtaining Consent
Accurate Reporting and Reproducibility
Financial Conflicts of Interest
Use of Generative AI in CS Conference Publications
Ways to Incorporate Ethics Review into Publication Review Processes
Reporting and Retraction Policies and Procedures
Tracking and Transparency
References

Don't Kill the Baby: The Case for AI in Arbitration / 2408.11608 / ISBN:https://doi.org/10.48550/arXiv.2408.11608 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
1. What is AI
2. Designating AI as an Arbitrator is Consistent with FAA
3. Practical and Strategic Benefits of Using AI in Arbitration
Part II. The Critics are Killing the Baby
1. Resistance Against AI Does Not Offer Conclusive Reasons for Outright Rejection
2. Let AI Grow Under Favorable Conditions: Avoiding Overly Moralistic Views
3. Arbitration Should Allow Flexible, Contract-Based Experimentation in a Fast- Evolving Regulatory Landscape

CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher / 2408.11650 / ISBN:https://doi.org/10.48550/arXiv.2408.11650 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Background and Related Works
3. Methodology
4. Experiment Results
5. Discussion and Future Works
References

The Problems with Proxies: Making Data Work Visible through Requester Practices / 2408.11667 / ISBN:https://doi.org/10.48550/arXiv.2408.11667 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Related Work
Methods
Findings
Discussion
Conclusion
Research Ethics and Social Impact
References
Appendix

Promises and challenges of generative artificial intelligence for human learning / 2408.12143 / ISBN:https://doi.org/10.48550/arXiv.2408.12143 / Published by ArXiv / on (web) Publishing site

Abstract
1 Main
2 Promises
3 Challenges
4 Needs
5 Conclusion and Future Directions
References
Tables

Catalog of General Ethical Requirements for AI Certification / 2408.12289 / ISBN:https://doi.org/10.48550/arXiv.2408.12289 / Published by ArXiv / on (web) Publishing site

Summary
1 Introduction
2 Operationalizable minimum requirements
3 European Union AI Act: a brief overview
4 Compliance and implementation of the suggested assessments
5 Overall Ethical Requirements (O)
6 Fairness (F)
7 Privacy and Data Protection (P)
8 Safety and Robustness (SR)
9 Sustainability (SU)
10 Transparency and Explainability (T)
11 Truthfulness (TR)
12 Conclucing Remarks

Dataset | Mindset = Explainable AI | Interpretable AI / 2408.12420 / ISBN:https://doi.org/10.48550/arXiv.2408.12420 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Literature Review
3. Database and Experimental Setup
4. Experiment Implementation, Results and Analysis
6. Conclusion and Future Works
References

Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks / 2408.12806 / ISBN:https://doi.org/10.48550/arXiv.2408.12806 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related Work
III. Generative AI
IV. Attack Methodology
V. Conclusion
References

Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey / 2408.12880 / ISBN:https://doi.org/10.48550/arXiv.2408.12880 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Preliminaries
3 Multimodal Medical Studies
4 Contrastice Foundation Models (CFMs)
5 Multimodal LLMs (MLLMs)
6 Discussions of Current Studies
7 Challenges and Future Directions
References
Appendix

Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis / 2408.15121 / ISBN:https://doi.org/10.48550/arXiv.2408.15121 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Methodology
4 Background
5 Explanation Requirements and Legal Explanatory Goals
6 A Categorisation of XAI in Terms of Explanatory Goals
7 Case Studies: Closed-Loop and Semi-Closed-Loop Control
8 Instructions for Use & Discussion of Findings
9 Threats to Validity
10 Conclusion
References

What Is Required for Empathic AI? It Depends, and Why That Matters for AI Developers and Users / 2408.15354 / ISBN:https://doi.org/10.48550/arXiv.2408.15354 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Three Empathic AI Use Cases in Medicine
“Fine cuts” of Empathy: Capabilities and Distinctions under the Empathy Umbrella
What Empathic Capabilities Do AIs Need?
Implications for AI Creators and Users
Conclusion
References

Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems / 2408.15550 / ISBN:https://doi.org/10.48550/arXiv.2408.15550 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Trustworthy and Responsible AI Definition
3 Governance for Human-Centric Intelligence Systems
4 Biases
5 Trustworthy and Responsible AI in Human-centric Applications
6 Open Challenges
7 Guidelines and Recommendations
8 Conclusion and Final Remarks
References

A Survey for Large Language Models in Biomedicine / 2409.00133 / ISBN:https://doi.org/10.48550/arXiv.2409.00133 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background
3 LLMs in Zero-Shot Biomedical Applications
4 Adapting General LLMs to the Biomedical Field
5 Discussion
6 Conclusion
References

Digital Homunculi: Reimagining Democracy Research with Generative Agents / 2409.00826 / ISBN:https://doi.org/10.48550/arXiv.2409.00826 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. The Experimentation Bottleneck
3. How GenAI Could Make a Difference
4. Risks and Caveats
5. Annoyances or Dealbreakers?
6. Conclusion
References

The overlooked need for Ethics in Complexity Science: Why it matters / 2409.02002 / ISBN:https://doi.org/10.48550/arXiv.2409.02002 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Mapping ethical challenges in complexity science
Limited research on ethics in complexity science
Practical considerations for ethical actions in complexity science
Conclusion
Author contributions
Funding statement
References

AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities / 2409.02017 / ISBN:https://doi.org/10.48550/arXiv.2409.02017 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Background
Methods
Results
Discussion
Limitation
Conclusion
References

Preliminary Insights on Industry Practices for Addressing Fairness Debt / 2409.02432 / ISBN:https://doi.org/10.48550/arXiv.2409.02432 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Fairness Debt
3 Method
4 Findings
5 Discussions
6 Conclusions
References

DetoxBench: Benchmarking Large Language Models for Multitask Fraud & Abuse Detection / 2409.06072 / ISBN:https://doi.org/10.48550/arXiv.2409.06072 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Prior Benchmarks
3 Data Details
8 Dataset Disclaimers and Terms
9 Conclusion & Future Work
References
10 Appendix

Exploring AI Futures Through Fictional News Articles / 2409.06354 / ISBN:https://doi.org/10.48550/arXiv.2409.06354 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Reflections from two workshop participants
Discussion and conclusion

Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik's Cubeà / 2402.01760 / ISBN:https://doi.org/10.48550/arXiv.2402.01760 / Published by ArXiv / on (web) Publishing site

D. CausalRating: A Tool To Rate Sentiments Analysis Systems for Bias

Face Recognition: to Deploy or not to Deploy? A Framework for Assessing the Proportional Use of Face Recognition Systems in Real-World Scenarios / 2402.05731 / ISBN:https://doi.org/10.48550/arXiv.2402.05731 / Published by ArXiv / on (web) Publishing site

References

The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources / 2406.16746 / ISBN:https://doi.org/10.48550/arXiv.2406.16746 / Published by ArXiv / on (web) Publishing site

B Cheatsheet Samples

Don't Kill the Baby: The Case for AI in Arbitration / 2408.11608 / ISBN:https://doi.org/10.48550/arXiv.2408.11608 / Published by ArXiv / on (web) Publishing site

Part IV. Future Direction and Conclusion

Catalog of General Ethical Requirements for AI Certification / 2408.12289 / ISBN:https://doi.org/10.48550/arXiv.2408.12289 / Published by ArXiv / on (web) Publishing site

References

The overlooked need for Ethics in Complexity Science: Why it matters / 2409.02002 / ISBN:https://doi.org/10.48550/arXiv.2409.02002 / Published by ArXiv / on (web) Publishing site

Annexus

On the Creativity of Large Language Models / 2304.00008 / ISBN:https://doi.org/10.48550/arXiv.2304.00008 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 A Creative Journey from Ada Lovelace to Foundation Models
3 Large Language Models and Boden’s Three Criteria
4 Easy and Hard Problems in Machine Creativity
5 Practical Implications
6 Conclusion
References

Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward / 2305.08413 / ISBN:https://doi.org/10.48550/arXiv.2305.08413 / Published by ArXiv / on (web) Publishing site

Introduction
Part I Modelling - Machine learning, computer vision and processing 1 Machine learning and computer vision for Earth observation
2 Advanced processing and computing
Part II Understanding - Physics-machine learning interplay, causality and ontologies 3 Knowledge-based AI and Earth observation
4 Explainable AI and causal inference
5 Physics-aware machine learning
Part III Communicating - Machine-user interaction, trustworthiness & ethics 6 User-centric Earth observation
7 Earth observation and society: the growing relevance of ethics
Conclusions
References

LLM generated responses to mitigate the impact of hate speech / 2311.16905 / ISBN:https://doi.org/10.48550/arXiv.2311.16905 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Dataset
4 Hate Classifier Model
6 Experiment
7 Results
8 Discussion
9 Limitations
10 Ethical Considerations
A Reproducibility
B Experiment Setup Details
C Verified Articles
G Model Answers Analysis

Why business adoption of quantum and AI technology must be ethical / 2312.10081 / ISBN:https://doi.org/10.48550/arXiv.2312.10081 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Argument from a holistic and humanistic perspective
Argument from Authority: Ethics by committee
Argument by regulatory relevance
Argument for acknowledging complexity: the case for individual ethos, regulation is not enough
Argument by analogy: The case of sustainability
Reductio ad absurdum: Argument by assuming the opposite scenario leading to unacceptable consequences
Summary and action areas
Notes
References

Views on AI aren't binary -- they're plural / 2312.14230 / ISBN:https://doi.org/10.48550/arXiv.2312.14230 / Published by ArXiv / on (web) Publishing site

Abstract
The false binary: A note on language
The false binary: Ethics (the stereotype
The false binary: Alignment (the stereotype)
The false binary: Ethics’s discontents with Alignment
The complex reality: Where Ethics and Alignment (actually) differ
The complex reality: Where Ethics and Alignment (actually) are similar
The complex reality: Complication: There are more than two camps
The complex reality: Complication: The existential risk narrative has corporate valu
The complex reality: Complication: Good and bad ideas can be knotted together
Overcoming the dichotomy: How to build bridges
Conclusion
References

Data-Centric Foundation Models in Computational Healthcare: A Survey / 2401.02458 / ISBN:https://doi.org/10.48550/arXiv.2401.02458 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Foundation Models
3 Foundation Models in Healthcare
4 Multi-Modal Data Fusion
5 Data Quantity
6 Data Annotation
7 Data Privacy
8 Performance Evaluation
9 Challenges and Opportunities
10 Conclusions
References
A Healthcare Data Modalities
B Healthcare Foundation Models

Ethical Artificial Intelligence Principles and Guidelines for the Governance and Utilization of Highly Advanced Large Language Models / 2401.10745 / ISBN:https://doi.org/10.48550/arXiv.2401.10745 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Background
Comprehending Advanced Large Language Models and its Capabilities
Advanced Large Language Models Governance Using AI Ethics
Considerations for Advanced Large Language Models and Policy-Making
Discussion
Conclusion
References

Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models / 2401.16727 / ISBN:https://doi.org/10.48550/arXiv.2401.16727 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Hate Speech
3 Methodology
4 Challenges
5 Future Directions
6 Conclusion
References

Integrating Generative AI in Hackathons: Opportunities, Challenges, and Educational Implications / 2401.17434 / ISBN:https://doi.org/10.48550/arXiv.2401.17434 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Methodology
3. Results
4. Discussion
5. Conclusion
References

Large language models as linguistic simulators and cognitive models in human research / 2402.04470 / ISBN:https://doi.org/10.48550/arXiv.2402.04470 / Published by ArXiv / on (web) Publishing site

Abstract
Language models as human participants
Six fallacies that misinterpret language models
Using language models to simulate roles and model cognitive processes
Concluding remarks
References

Navigating LLM Ethics: Advancements, Challenges, and Future Directions / 2406.18841 / ISBN:https://doi.org/10.48550/arXiv.2406.18841 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Conceptualization and frameworks
III. Methodology
IV. Findings and Resultant Themes
V. Discussion
VI. Conclusion and Future directions
References

How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions / 2409.07192 / ISBN:https://doi.org/10.48550/arXiv.2409.07192 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Research Design
4 Results
5 Open Challenges and Future Research Directions (RQ5)
6 Discussions
References

Synthetic Human Memories: AI-Edited Images and Videos Can Implant False Memories and Distort Recollection / 2409.08895 / ISBN:https://doi.org/10.48550/arXiv.2409.08895 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
1 Related Work
2 Methodology
3 Result of Primary Analysis
4 Results of Additional Analysis
5 Discussion
6 Conclusion
References

Improving governance outcomes through AI documentation: Bridging theory and practice / 2409.08960 / ISBN:https://doi.org/10.48550/arXiv.2409.08960 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
3 Methods
4 Results
References

ValueCompass: A Framework of Fundamental Values for Human-AI Alignment / 2409.09586 / ISBN:https://doi.org/10.48550/arXiv.2409.09586 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Work
3 Designing ValueCompass: A Comprehensive Framework for Defining Fundamental Values in Alignment
4 Operationalizing ValueCompass: Methods to Measure Value Alignment of Humans and AI
5 Findings with ValueCompass: The Status Quo of Human-AI Value Alignment
6 Discussion
7 Conclusion
References

Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools / 2409.11489 / ISBN:https://doi.org/10.48550/arXiv.2409.11489 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Ethical Considerations in AI-Enabled Optimization
3 Case Studies in AI-Enabled Optimization
4 Lessons Learned from the Case Studies
5 Conclusion
References
Appendix A Technical and Contextual Details for Collaborative Decentralized Cold Supply Chains
Appendix B Technical and Conceptual Details for the Power Systems Case Study

Reporting Non-Consensual Intimate Media: An Audit Study of Deepfakes / 2409.12138 / ISBN:https://doi.org/10.48550/arXiv.2409.12138 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Research
3 Method
4 Findings
5 Discussion
References
Appendices

Generative AI Carries Non-Democratic Biases and Stereotypes: Representation of Women, Black Individuals, Age Groups, and People with Disability in AI-Generated Images across Occupations / 2409.13869 / ISBN:https://doi.org/10.48550/arXiv.2409.13869 / Published by ArXiv / on (web) Publishing site

Abstract
Mutual Impacts: Technology and Democracy
Data and Results
Middle-aged and elders’ representation
References
Supplementary Data (August 2024)

GenAI Advertising: Risks of Personalizing Ads with LLMs / 2409.15436 / ISBN:https://doi.org/10.48550/arXiv.2409.15436 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Background and Related Work
3 Chatbot Ad Engine Design
5 User Study Methodology
6 User Study Results
7 Discussion
References
A Appendix

XTRUST: On the Multilingual Trustworthiness of Large Language Models / 2409.15762 / ISBN:https://doi.org/10.48550/arXiv.2409.15762 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Works
3 XTRUST Construction
4 Experiments
5 Conclusion
Limitations
References
Appendices

Artificial Human Intelligence: The role of Humans in the Development of Next Generation AI / 2409.16001 / ISBN:https://doi.org/10.48550/arXiv.2409.16001 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Views on Intelligence
3 Origins and the Path leading to AHI
4 Brain-inspired Information processing
5 Challenges and Perspectives in Human-Level AI Development
6 Final Thoughts and Discussions
7 Conclusion
8 Acknowledgement
References

Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applications / 2409.16872 / ISBN:https://doi.org/10.48550/arXiv.2409.16872 / Published by ArXiv / on (web) Publishing site

Abstract
1. Introduction
2. Literature Review
3. Methodology
4. Framework Development
5. Analysis and Discussion
6. Conclusion
7. References

Decoding Large-Language Models: A Systematic Overview of Socio-Technical Impacts, Constraints, and Emerging Questions / 2409.16974 / ISBN:https://doi.org/10.48550/arXiv.2409.16974 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Systematic Reviews
3 Systematic Review Methodology
5 Aims & Objectives (RQ1)
6 Methodologies & Capabilities (RQ2)
7 Limitations & Considerations (RQ3)
8 Discussion
9 Conclusion
References

Social Media Bot Policies: Evaluating Passive and Active Enforcement / 2409.18931 / ISBN:https://doi.org/10.48550/arXiv.2409.18931 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Related Work
III. Current Platform Measures
IV. Methodology
V. Results
VI. Conclusion
References

Safety challenges of AI in medicine / 2409.18968 / ISBN:https://doi.org/10.48550/arXiv.2409.18968 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Inherent problems of AI related to medicine
3 Risks of using AI in medicine
4 AI safety issues related to large language models in medicine
5 Conclusion
7 Author contributions
References

Responsible AI in Open Ecosystems: Reconciling Innovation with Risk Assessment and Disclosure / 2409.19104 / ISBN:https://doi.org/10.48550/arXiv.2409.19104 / Published by ArXiv / on (web) Publishing site

Abstract
I Introduction
2 Related Work
3 Methods
5 Discussion
6 Conclusion
References
A Benchmarks in Open LLM Leaderboard

The Gradient of Health Data Privacy / 2410.00897 / ISBN:https://doi.org/10.48550/arXiv.2410.00897 / Published by ArXiv / on (web) Publishing site

Abstract
2 Background and Related Work
3 The Health Data Privacy Gradient
4 Technical Implementation of a Privacy Gradient Model
5 Legal and Ethical Implications
6 Case Studies
References

Enhancing transparency in AI-powered customer engagement / 2410.01809 / ISBN:https://doi.org/10.48550/arXiv.2410.01809 / Published by ArXiv / on (web) Publishing site

Abstract
Explaining AI-Powered Decision Making in Customer Engagement
Challenges to Achieving Transparency
Go Beyond Algorithms to Enhance Transparency
References

Ethical software requirements from user reviews: A systematic literature review / 2410.01833 / ISBN:https://doi.org/10.48550/arXiv.2410.01833 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Background
III. Research Methodology
IV. Results
V. Discussion
VI. Threats to Validity
VII. Conclusion
References
APPENDIX A SELECTED STUDIES
APPENDIX B DATA EXTRACTED FROM PRIMARY STUDIES
APPENDIX C PUBLICATION TRENDS
APPENDIX D ADDITIONAL INFORMATION

Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border Collaboration / 2410.02443 / ISBN:https://doi.org/10.48550/arXiv.2410.02443 / Published by ArXiv / on (web) Publishing site

Abstract
I. Introduction
II. Motivation
III. FL System Elements
IV. Proof of Concept I
V. Proof of Concepts 2
VI. Collaborative Network
VII. Evaluations and Experiments
VIII. Discussion and Conclusions
References

DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life / 2410.02683 / ISBN:https://doi.org/10.48550/arXiv.2410.02683 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Value-Based Framework on Moral Dilemmas
3 Daily Dilemmas: Dataset Construction
4 Daily Dilemmas: Dataset Analysis
5 Model Preference and Steerability on Daily Dilemmas
References

Application of AI in Credit Risk Scoring for Small Business Loans: A case study on how AI-based random forest model improves a Delphi model outcome in the case of Azerbaijani SMEs / 2410.05330 / ISBN:https://doi.org/10.48550/arXiv.2410.05330 / Published by ArXiv / on (web) Publishing site

Abstract
Introduction
Literature Review
Methodology
Discussion
Conclusion
Ethical considerations
References

AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models / 2410.07561 / ISBN:https://doi.org/10.48550/arXiv.2410.07561 / Published by ArXiv / on (web) Publishing site

Abstract
1 Introduction
2 Related Works
3 AI Press System
4 Experimental Setup
5 Results
Ethics Statement
References
C Prompts for Agents on Press Drafting Module
D Prompts for Agents on Press Polishing Module
E Prompts for Simulation Module
F User Profile Pool Generating
G Evaluation Experiment

DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life / 2410.02683 / ISBN:https://doi.org/10.48550/arXiv.2410.02683 / Published by ArXiv / on (web) Publishing site

Appendices

Human participants in AI research: Ethics and transparency in practice / 2311.01254 / ISBN:https://doi.org/10.48550/arXiv.2311.01254 / Published by ArXiv / on (web) Publishing site

Appendix D A Note on Terminology