Ethical_AI_and_ML_Principles with ML classifications

AI

1. Ethical AI and Machine Learning Principles to Practice Ensuring Responsible and Trustworthy AI Systems

2. Fairness and Non-Discrimination Avoid bias, ensure equitable treatment, perform fairness audits.

3. Transparency and Explainability Make systems understandable, use interpretable models, document processes.

4. Privacy and Data Governance Protect data, anonymize sensitive info, follow data protection laws.

5. Human-Centered Design and Oversight Keep humans in the loop, ensure inclusivity, design for user needs.

6. Accountability and Responsibility Define accountability, maintain audit logs, establish governance.

7. Reliability and Robustness Test thoroughly, detect adversarial attacks, ensure model generalization.

8. Sustainability Use efficient models, reduce carbon footprint, adopt green AI practices.

9. Ethical Use and Purpose Alignment Align AI with values, avoid harmful uses, conduct impact assessments.

10. Continuous Monitoring and Evaluation Monitor post-deployment, retrain as needed, set feedback loops.

11. Inclusive Stakeholder Engagement Involve diverse teams, gather feedback, promote open ethical dialogues.