AIM 104 - AI Ethics Foundation

1. Introduction to AI ethics
2. Historical and cultural perspectives on ethics
3. Ethical frameworks in AI
4. Bias, fairness, and accountability
5. Privacy, surveillance, and data ethics
6. Transparency, explainability, and trust
7. Regulation, governance, and global policy
8. Ethical leadership and the future of AI

1. Explain the role of responsible AI and its contribution to societal well-being. (1, 2, 7, 8)
2. Analyze the role of ethical principles in developing responsible AI solutions. (1, 3, 5, 7, 8)
3. Differentiate the core principles of ethical AI, including human-centered design, transparency, fairness, autonomy, beneficence, non-maleficence, and privacy. (1, 3, 4, 5, 6, 8)
4. Evaluate common ethical pitfalls in AI systems such as bias, unintended consequences, non-compliance, and privacy violations. (4, 5, 6, 7, 8)
5. Apply an ethics-first approach within the AI project cycle to support responsible design and implementation. (1, 3, 4, 7, 8)
6. Compare and contrast different design approaches for ethical AI, including rule-based, top-down, and bottom-up models. (1, 3, 7)
7. Explain ethical frameworks for analyzing and resolving dilemmas in AI contexts. (3, 7)
8. Apply ethical theories to analyze real-world AI dilemmas. (2, 3, 7)
9. Assess the societal and organizational impacts of AI solutions. (4, 5, 6, 7, 8)
10. Evaluate the importance of interpretability in AI models and its influence on human trust and decision-making. (4, 6, 7, 8)
11. Assess the risks and consequences of data breaches and privacy violations in AI applications. (5, 6, 7)
12. Design an ethical post-deployment plan that ensures responsible and transparent AI operations. (5, 7, 8)
13. Develop a code of AI ethics for individuals and organizations to guide responsible practice. (1, 3, 7, 8)
14. Refine existing AI ethics codes using established ethical frameworks and best practices. (2, 3, 7, 8)