Why_this_webapp Bibliography

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

Part 3 - 4 Demonstration of the Proposed Framework

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

2. Fairness - For Equitable AI in Medical Imaging

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

VI. IMPROVING FAIRNESS , ACCOUNTABILITY, TRANSPARENCY, AND ETHICS

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

Ethics of GEnerating Native Ads

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

4. Technical Risks

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

FUTURE-AI GUIDELINE

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
4 Deontological AI Alignment

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
IV. Mitigation strategies for bias in AI
VI. Mitigation strategies for fairness in AI

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

4. ChatGPT

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

4. Post-market surveillance phase

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

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

V. Discussion and suggestions

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

Materials and methods
Results
Discussion

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

Contents / List of figures / List of tables / Acronyms
1 Introduction
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
9 Towards fairness through time
Bibliography

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

3 Detection

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

Part 1. Study design
Part 3. Updates to baseline selection
Table 1. Updated MI-CLAIM checklist for generative AI clinical studies.

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

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

5 Ways to mitigate bias and promote Fairness
7 Challenges and Limitations
References
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

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

6 Ethics

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

I. Introduction
II. Trustworthy AIGC in 6G Network
V. Fairness of AIGC in 6G Network

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

3 RQ2: What Technical Strategies Can Be Employed to Mitigate the Negative Consequences of AI Autophagy?

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

Methods in clinical AI fairness research
Reference
Additional material

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

Paper

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

2 RELATED WORK

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:

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

7. Conclusion and Future Directions

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

I Introduction
5 Limitation and Future Work

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

3. Methodology

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

Appendix

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

4 Biases
5 Trustworthy and Responsible AI in Human-centric Applications