AI+ Ethics Fundamentals™

Course structure

Course Overview

• Course Introduction Preview

Module 1: Foundations of AI Ethics and Responsible AI

• 1.1 Understanding AI in a Modern Ethics Context
• 1.2 The Societal Impact of AI Technologies
• 1.3 Core Principles and Stakeholders
• 1.4 Building AI Literacy for the Workplace
• 1.5 Human Rights, Democracy, and AI Ethics
• 1.6 Case Studies

Module 2: Bias, Fairness, and Inclusion in AI

• 2.1 Where Bias Enters AI Systems
• 2.2 Fairness Concepts and Practical Evaluation
• 2.3 Mitigation and Inclusive Design
• 2.4 Applied Fairness Cases
• 2.5 Case Studies

Module 3: Transparency, Explainability, and Documentation

• 3.1 Why Transparency Matters
• 3.2 Explainability Methods and Documentation Standards
• 3.3 Communicating AI Decisions Responsibly
• 3.4 Transparency, Documentation, and Governance Practices
• 3.5 Case Studies

Module 4: Privacy, Security, and AI Data Governance

• 4.1 Privacy Principles in AI
• 4.2 AI Data Governance and Data Quality
• 4.3 Security Risks in AI Systems
• 4.4 Privacy-Preserving AI Techniques
• 4.5 Content Authenticity, Provenance, and Trust
• 4.6 Real World Case Studies

Module 5: Accountability, Oversight, and AI Governance

• 5.1 Accountability Across the AI Lifecycle
• 5.2 Human Oversight and Control
• 5.3 Risk Management and Assurance
• 5.4 Red Teaming and Safety Testing
• 5.5 Governance Operating Model
• 5.6 Grievance and Remedy Processes
• 5.7 System Retirement and Decommissioning
• 5.8 Applied Case Studies

Module 6: Legal, Regulatory, and Standards Landscape

• 6.1 International Principles and Treaties
• 6.2 Management and Technical Standards
• 6.3 Binding Regional Laws
• 6.4 National Guidance and Voluntary Frameworks
• 6.5 Sector-Specific and Cross-Border Compliance
• 6.6 Case Studies

Module 7: Generative AI, Agentic AI, and Responsible Deployment

• 7.1 How Modern Generative and Agentic AI Systems Work
• 7.2 New Risks Introduced by Generative AI
• 7.3 Agentic AI Risks and Governance
• 7.4 Evaluation and Safe Deployment
• 7.5 Responsible Use Cases and Boundaries

Module 8: Capstone - AI Ethics Impact Assessment and Governance Plan

• 8.1 Select an AI Use Case
• 8.2 Perform an Ethics and Risk Assessment
• 8.3 Develop an AI Governance Package Using the NIST AI RMF
• 8.4 Final Capstone Deliverable
• 8.5 Review and Reflection

Optional Module: AI Agents for Ethics

• 1.1 What Are AI Agents?
• 1.2 Applications and Trends of AI Agents for Ethics
• 1.3 How Does an AI Agent Work?
• 1.4 Core Characteristics of AI Agents
• 1.5 Importance of AI Agents
• 1.6 Types of AI Agents