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Tag: quantifying
Bibliography items where occurs: 81
- The AI Index 2022 Annual Report / 2205.03468 / ISBN:https://doi.org/10.48550/arXiv.2205.03468 / Published by ArXiv / on (web) Publishing site
- Chapter 5 AI Policy and Governance
Appendix - 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
- 1 Introduction
- 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
- 10 Supplemental & additional details
- 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
- 4. Traceability - For Transparent and Dynamic AI in Medical Imaging
9. Discussion and Conclusion - 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
- 2 Definitions of Terms
- 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
- 5 Related Work
- 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
- 1 Introduction
- 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
- 1 Introduction
4 Related Work - 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
- 2. Chatbots Background and Scope of Research
- 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
- 4 Fairness and equity
- 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
- Discussion
- 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
- 4. State-of-the-art AI techniques in autonomous threat hunting
- 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
- 3 Bias on demand: a framework for generating synthetic data with bias
- 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
- METRIC-framework for medical training data
- 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
- 4 Safe, Secure and Trustworthy AI
- 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
- 3 Method
- 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
- Methodology
- 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
- 1 Context
- 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
- V. Discussion
- 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
- 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
- 4 Critical Survey
- AI Alignment: A Comprehensive Survey / 2310.19852 / ISBN:https://doi.org/10.48550/arXiv.2310.19852 / Published by ArXiv / on (web) Publishing site
- 5 Governance
- 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
- 6 Conclusions: Towards Humble Technical Practices
- 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
- 2 Background and Related Work
- 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
- 4 LLM Lifecycle
- 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
- 1 Introduction
2 Qualifying and Quantifying Emotions - 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
- 3 Results
- 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
- 1 Introduction
- 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
- 4 The Narrow Depth of Industry’s Responsible AI Research
- 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
- 3. Discussion
- 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
- Introduction
IV. Impact of Alignments on Corporate Investment Forecasts - 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 - MoralBench: Moral Evaluation of LLMs / 2406.04428 / Published by ArXiv / on (web) Publishing site
- 3 Benchmark and Method
- Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models / 2406.05602 / Published by ArXiv / on (web) Publishing site
- 2. Related Work
- A Blueprint for Auditing Generative AI / 2407.05338 / ISBN:https://doi.org/10.48550/arXiv.2407.05338 / Published by ArXiv / on (web) Publishing site
- 5 Model audits
- Operationalising AI governance through ethics-based auditing: An industry case study / 2407.06232 / Published by ArXiv / on (web) Publishing site
- 6. Lessons learned from AstraZeneca’s 2021 AI audit
- 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
- IV. Proposing an Alternative 3C Framework
- 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
- 1 Introduction
- 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
- I. The Why and How Behind LLMs
- 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
- 2 Operationalizable minimum requirements
9 Sustainability (SU) - 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
- 4 Explainable AI and causal inference
- 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
- 5 Open Challenges and Future Research
Directions (RQ5)
- ValueCompass: A Framework for Measuring Contextual Value Alignment Between Human and LLMs / 2409.09586 / ISBN:https://doi.org/10.48550/arXiv.2409.09586 / Published by ArXiv / on (web) Publishing site
- 3 ValueCompass Framework
- 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
- Mutual Impacts: Technology and Democracy
- 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
- 2. Literature Review
3. Methodology
4. Framework Development - 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
- 6 Methodologies & Capabilities (RQ2)
- Investigating Labeler Bias in Face Annotation for Machine Learning / 2301.09902 / ISBN:https://doi.org/10.48550/arXiv.2301.09902 / Published by ArXiv / on (web) Publishing site
- 5. Discussion
- Jailbreaking and Mitigation of Vulnerabilities in Large Language Models / 2410.15236 / ISBN:https://doi.org/10.48550/arXiv.2410.15236 / Published by ArXiv / on (web) Publishing site
- V. Evaluation and Benchmarking
- The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methods / 2410.18866 / ISBN:https://doi.org/10.48550/arXiv.2410.18866 / Published by ArXiv / on (web) Publishing site
- VI. Evaluation Metrics
- Democratizing Reward Design for Personal and Representative Value-Alignment / 2410.22203 / ISBN:https://doi.org/10.48550/arXiv.2410.22203 / Published by ArXiv / on (web) Publishing site
- 4 Study Design & Methodology
- Using Large Language Models for a standard assessment mapping for sustainable communities / 2411.00208 / ISBN:https://doi.org/10.48550/arXiv.2411.00208 / Published by ArXiv / on (web) Publishing site
- 1 Introduction
2 Literature Review - How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law / 2404.12762 / ISBN:https://doi.org/10.48550/arXiv.2404.12762 / Published by ArXiv / on (web) Publishing site
- 1 Introduction
- A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions / 2406.03712 / ISBN:https://doi.org/10.48550/arXiv.2406.03712 / Published by ArXiv / on (web) Publishing site
- VI. Trustworthiness and Safety
- Persuasion with Large Language Models: a Survey / 2411.06837 / ISBN:https://doi.org/10.48550/arXiv.2411.06837 / Published by ArXiv / on (web) Publishing site
- 4 Experimental Design Patterns
- Bias in Large Language Models: Origin, Evaluation, and Mitigation / 2411.10915 / ISBN:https://doi.org/10.48550/arXiv.2411.10915 / Published by ArXiv / on (web) Publishing site
- 4. Bias Evaluation
- Political-LLM: Large Language Models in Political Science / 2412.06864 / ISBN:https://doi.org/10.48550/arXiv.2412.06864 / Published by ArXiv / on (web) Publishing site
- 4 Classical Political Science Functions and Modern Transformations
- Ethics and Technical Aspects of Generative AI Models in Digital Content Creation / 2412.16389 / ISBN:https://doi.org/10.48550/arXiv.2412.16389 / Published by ArXiv / on (web) Publishing site
- 3 Methodology
- Large Language Model Safety: A Holistic Survey / 2412.17686 / ISBN:https://doi.org/10.48550/arXiv.2412.17686 / Published by ArXiv / on (web) Publishing site
- 6 Autonomous AI Risks
- Generative AI and LLMs in Industry: A text-mining Analysis and Critical Evaluation of Guidelines and Policy Statements Across Fourteen Industrial Sectors / 2501.00957 / ISBN:https://doi.org/10.48550/arXiv.2501.00957 / Published by ArXiv / on (web) Publishing site
- V. Discussion and Synthesis
- Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifesto / 2312.01818 / ISBN:https://doi.org/10.48550/arXiv.2312.01818 / Published by ArXiv / on (web) Publishing site
- 5. Outlook & Implications
- Uncovering Bias in Foundation Models: Impact, Testing, Harm, and Mitigation / 2501.10453 / ISBN:https://doi.org/10.48550/arXiv.2501.10453 / Published by ArXiv / on (web) Publishing site
- Supplementary
- Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare / 2501.18632 / ISBN:https://doi.org/10.48550/arXiv.2501.18632 / Published by ArXiv / on (web) Publishing site
- Introduction
- Meursault as a Data Point / 2502.01364 / ISBN:https://doi.org/10.48550/arXiv.2502.01364 / Published by ArXiv / on (web) Publishing site
- III. Conceptual Framework
V. Results - Safety at Scale: A Comprehensive Survey of Large Model Safety / 2502.05206 / ISBN:https://doi.org/10.48550/arXiv.2502.05206 / Published by ArXiv / on (web) Publishing site
- 8 Open Challenges
- On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective / 2502.14296 / ISBN:https://doi.org/10.48550/arXiv.2502.14296 / Published by ArXiv / on (web) Publishing site
- 4 Designing TrustGen, a Dynamic Benchmark Platform for Evaluating the
Trustworthiness of GenFMs
7 Benchmarking Vision-Language Models
10 Further Discussion - Evaluating Large Language Models on the Spanish Medical Intern Resident (MIR) Examination 2024/2025:A Comparative Analysis of Clinical Reasoning and Knowledge Application / 2503.00025 / ISBN:https://doi.org/10.48550/arXiv.2503.00025 / Published by ArXiv / on (web) Publishing site
- 5. Conclusion
- Vision Language Models in Medicine / 2503.01863 / ISBN:https://doi.org/10.48550/arXiv.2503.01863 / Published by ArXiv / on (web) Publishing site
- I. Introduction
- Medical Hallucinations in Foundation Models and Their Impact on Healthcare / 2503.05777 / ISBN:https://doi.org/10.48550/arXiv.2503.05777 / Published by ArXiv / on (web) Publishing site
- 2 LLM Hallucinations in Medicine
5 Mitigation Strategies
7 Annotations of Medical Hallucination with Clinical Case Records - Ethical Implications of AI in Data Collection: Balancing Innovation with Privacy / 2503.14539 / ISBN:https://doi.org/10.48550/arXiv.2503.14539 / Published by ArXiv / on (web) Publishing site
- Introduction
- BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models
/ 2503.24310 / ISBN:https://doi.org/10.48550/arXiv.2503.24310 / Published by ArXiv / on (web) Publishing site
- 2 Proposed Framework - BEATS
- Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice / 2504.00797 / ISBN:https://doi.org/10.48550/arXiv.2504.00797 / Published by ArXiv / on (web) Publishing site
- 2 Key Concepts and Definitions
- Towards interactive evaluations for interaction harms in human-AI systems / 2405.10632 / ISBN:https://doi.org/10.48550/arXiv.2405.10632 / Published by ArXiv / on (web) Publishing site
- 3 Why current evaluations approaches are insufficient for assessing
interaction harms
- Who is Responsible? The Data, Models, Users or Regulations? A Comprehensive Survey on Responsible Generative AI for a Sustainable Future / 2502.08650 / ISBN:https://doi.org/10.48550/arXiv.2502.08650 / Published by ArXiv / on (web) Publishing site
- 3 Explainable AI
- >Publishing site
- How Long is the Evaluation Relevant?
- From Texts to Shields: Convergence of Large Language Models and Cybersecurity / 2505.00841 / ISBN:https://doi.org/10.48550/arXiv.2505.00841 / Published by ArXiv / on (web) Publishing site
- 3 LLM Agent and Applications
- LLM Ethics Benchmark: A Three-Dimensional Assessment System for Evaluating Moral Reasoning in Large Language Models / 2505.00853 / ISBN:https://doi.org/10.48550/arXiv.2505.00853 / Published by ArXiv / on (web) Publishing site
- Abstract
- Sentience Quest: Towards Embodied, Emotionally Adaptive, Self-Evolving, Ethically Aligned Artificial General Intelligence / 2505.12229 / ISBN:https://doi.org/10.48550/arXiv.2505.12229 / Published by ArXiv / on (web) Publishing site
- Abstract
- Simulating Ethics: Using LLM Debate Panels to Model Deliberation on Medical Dilemmas / 2505.21112 / ISBN:https://doi.org/10.48550/arXiv.2505.21112 / Published by ArXiv / on (web) Publishing site
- 6. Future Directions
- Human-Centered Human-AI Collaboration (HCHAC) / 2505.22477 / ISBN:https://doi.org/10.48550/arXiv.2505.22477 / Published by ArXiv / on (web) Publishing site
- 3. Human Factor Research Methods of HAC
- Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment / 2506.02046 / ISBN:https://doi.org/10.48550/arXiv.2506.02046 / Published by ArXiv / on (web) Publishing site
- 3. The Eight Elements of Static Analysis: Theoretical Justification
4. Theoretical Framework for Vulnerability Scoring - Explainability in Context: A Multilevel Framework Aligning AI Explanations with Stakeholder with LLMs / 2506.05887 / ISBN:https://doi.org/10.48550/arXiv.2506.05887 / Published by ArXiv / on (web) Publishing site
- 2 Background on Explainable AI and Audience-Centered Explanations