AI Ethics for Professionals

Unit 1: GenAI Promises and Risks

The promise and peril of generative AI

• Distinctions between machine learning and generative AI
• AI alignment: challenges in aligning AI with human values, interests, and rights
• Transparency in AI systems: importance and challengesAI security: types of attacks and vulnerabilities
• Bias in AI: sources, implications, and case studies
• The problem of responsibility in AI-driven decisions

Unit 2: How Can We Trust AI?

• Key elements of trustworthy AI systems
• Safety and harm mitigation strategies
• Privacy and data security considerations in AI
• Explainability and interpretability in AI systems
• Addressing potential biases in AI models
• Case studies on AI trustworthiness (e.g., ChatGPT, facial recognition technologies)
• Human-in-the-loop approaches to AI oversight

Unit 3: AI Governance and Regulation

• Overview of AI governance approaches
• The EU AI Act: risk-based classification and governance structures
• U.S. federal efforts in AI governance: The Blueprint for an AI Bill of Rights
• Executive Order on Safe, Secure, and Trustworthy AI
• NIST AI Risk Management Framework
• Comparing EU and U.S. approaches to AI regulation
• AI governance in enterprises: case study of IBM's AI ethics structure
• Implementing AI ethics in small and medium-sized businesses
• Balancing top-down and bottom-up approaches to AI ethics
• Challenges and considerations in organizational AI governance

Unit 4: Communicating AI Ethics

• Purpose and importance of AI ethical statements
• Analyzing and comparing organizational AI principles (e.g., Google, Anthropic)
• Elements of effective ethical statements:
• Communicating values and commitments
• Specifying actionable policies and processes
• Grounding ethical statements in broader ethical frameworks
• Addressing specific ethical concerns (e.g., bias, privacy, safety)
• Translating ethical principles into governance structures
• Communicating AI ethics to various stakeholders (e.g., users, developers, policymakers)Practical exercise: Crafting and critiquing AI ethics statements