AI+ Ethics Fundamentals™
Formerly known as AI+ Ethics™ <br> <br> Navigate the Intersection of AI and Ethics in Business Landscape
Certificate Code:
AC-120
About This Course
- 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
Certificate Overview
Included
Instructor-led OR Self-paced course + Official exam + Digital badge
Duration
- Instructor-Led: 1 day (live or virtual)
- Self-Paced: 8 hours of content
Prerequisites
Basic knowledge of artificial intelligence, machine learning concepts, Python familiarity, fundamental AI/ML concepts
Exam Format
50 questions, 70% passing, 90 minutes, online proctored exam
Course Modules
1
Course Overview
- Course Introduction Preview
2
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
3
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
4
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
5
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
6
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
7
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
8
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
9
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
10
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
AI Tools You'll Learn
AI4People (Atomium - European Institute for Science, Media, and Democracy)
IBM - AI Fairness 360
IBM - AI Explainability 360
