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AI+ Ethics Fundamentals™

AI+ Ethics Fundamentals™
  • 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

 

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Certificate Code

AC-120

Exam Format

AI-Driven Remote Exam Proctoring

Course Overview

Important details and certification information

Instructor-led OR Self-paced course + Official exam + Digital badge
Instructor-Led: 3 Days (live or virtual)
Basic knowledge of artificial intelligence, machine learning concepts, Python familiarity, fundamental AI/ML concepts
50 questions, 70% passing, 90 minutes, online proctored exam

Certification Modules

  1. Course Introduction Preview

  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

  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

  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

  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

  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

  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

  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

  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

  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

AI Tools Covered

AI4People (Atomium - European Institute for Science, Media, and Democracy)
IBM - AI Fairness 360
IBM - AI Explainability 360
European Commission High-Level Expert Group on AI
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