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You are now here: AI Ethics Primer - search within the bibliography - version 0.3 of 2023-08-13
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AI Ethics Issues in Real World: Evidence from AI Incident Database / 2206.07635 / on (web) Publishing site
4 Results
The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis / 2206.03225 / on (web) Publishing site
1 Introduction 2 Related Work 3 Study Methodology 4 Evaluation of Ethical AI Principles 5 Evaluation of Ethical Principle Implementations
A Framework for Ethical AI at the United Nations / 2104.12547 / on (web) Publishing site
2. Defining ethical AI
Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance / 2206.11922 / on (web) Publishing site
1 Introduction 3 Methodology 4 Results References
On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services / 2111.01306 / on (web) Publishing site
3 Practical Challengesof Ethical AI
A primer on AI ethics via arXiv- focus 2020-2023 / Kaggle / on (web) Publishing site
Section 1: Introduction and concept Section 2: History and prospective Section 3: Current trends 2020-2023 Appendix B: Data and charts from arXiv
From OECD to India: Exploring cross-cultural differences in perceived trust, responsibility and reliance of AI and human experts / 2307.15452 / on (web) Publishing site
1. Introduction References
Ethical Considerations and Policy Implications for Large Language Models: Guiding Responsible Development and Deployment / 2308.02678 / on (web) Publishing site
Bias and Discrimination of Training Data
Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI / 2308.04448 / on (web) Publishing site
4 Centralized regulation in the US
context
A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation / 2305.11391 / on (web) Publishing site
Reference
Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust / 2308.09979 / on (web) Publishing site
1 Introduction 2 Results 4 Conclusions
Targeted Data Augmentation for bias mitigation / 2308.11386 / on (web) Publishing site
3 Targeted data augmentation
Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection / 2308.12885 / on (web) Publishing site
5 Results 6 Discussion
Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph / 2308.13534 / on (web) Publishing site
I. Introduction III. Comprehensive review of state-of-the-art LLMs VI. Solution architecture for privacy-aware and trustworthy conversational AI