• Responsible AI Focus: Master ethical AI use aligned with business and societal values

• Risk Mitigation: Learn to manage compliance, transparency, and AI decision-making

• Strategic Guidance: Integrate ethical practices into AI adoption and leadership

• Reputation Builder: Build organisational trust and credibility in AI deployments

 

Course Overview

1. Course Introduction Preview

Module 1: Foundations of AI Ethics and Responsible AI

1. 1.1 Understanding AI in a Modern Ethics Context
2. 1.2 The Societal Impact of AI Technologies
3. 1.3 Core Principles and Stakeholders
4. 1.4 Building AI Literacy for the Workplace
5. 1.5 Human Rights, Democracy, and AI Ethics
6. 1.6 Case Studies

Module 2: Bias, Fairness, and Inclusion in AI

1. 2.1 Where Bias Enters AI Systems
2. 2.2 Fairness Concepts and Practical Evaluation
3. 2.3 Mitigation and Inclusive Design
4. 2.4 Applied Fairness Cases
5. 2.5 Case Studies

Module 3: Transparency, Explainability, and Documentation

1. 3.1 Why Transparency Matters
2. 3.2 Explainability Methods and Documentation Standards
3. 3.3 Communicating AI Decisions Responsibly
4. 3.4 Transparency, Documentation, and Governance Practices
5. 3.5 Case Studies

Module 4: Privacy, Security, and AI Data Governance

1. 4.1 Privacy Principles in AI
2. 4.2 AI Data Governance and Data Quality
3. 4.3 Security Risks in AI Systems
4. 4.4 Privacy-Preserving AI Techniques
5. 4.5 Content Authenticity, Provenance, and Trust
6. 4.6 Real World Case Studies

Module 5: Accountability, Oversight, and AI Governance

1. 5.1 Accountability Across the AI Lifecycle
2. 5.2 Human Oversight and Control
3. 5.3 Risk Management and Assurance
4. 5.4 Red Teaming and Safety Testing
5. 5.5 Governance Operating Model
6. 5.6 Grievance and Remedy Processes
7. 5.7 System Retirement and Decommissioning
8. 5.8 Applied Case Studies

Module 6: Legal, Regulatory, and Standards Landscape

1. 6.1 International Principles and Treaties
2. 6.2 Management and Technical Standards
3. 6.3 Binding Regional Laws
4. 6.4 National Guidance and Voluntary Frameworks
5. 6.5 Sector-Specific and Cross-Border Compliance
6. 6.6 Case Studies

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

1. 7.1 How Modern Generative and Agentic AI Systems Work
2. 7.2 New Risks Introduced by Generative AI
3. 7.3 Agentic AI Risks and Governance
4. 7.4 Evaluation and Safe Deployment
5. 7.5 Responsible Use Cases and Boundaries

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

1. 8.1 Select an AI Use Case
2. 8.2 Perform an Ethics and Risk Assessment
3. 8.3 Develop an AI Governance Package Using the NIST AI RMF
4. 8.4 Final Capstone Deliverable
5. 8.5 Review and Reflection

Optional Module: AI Agents for Ethics

1. 1.1 What Are AI Agents?
2. 1.2 Applications and Trends of AI Agents for Ethics
3. 1.3 How Does an AI Agent Work?
4. 1.4 Core Characteristics of AI Agents
5. 1.5 Importance of AI Agents
6. 1.6 Types of AI Agents

Ethics Advisor

Advise on AI development and deployment ethical issues. Ensure justice, openness, and accountability in AI technology, supporting responsible innovation.

Policy Developer

Formulate and implement ethical and regulatory frameworks for AI adoption and governance to ensure responsible and equitable deployment.

Ethical Standards Advisor

Provide guidance on ethical considerations in AI development and deployment, ensuring adherence to ethical principles and societal values.

Audit Architect

Design and implement systems for ethical compliance and accountability in AI applications, conducting audits to detect and mitigate ethical risks.