Academy / Responsible AI & Governance

Path 4 · Course 2 · For everyone who builds, buys or uses AI

Responsible AI Principles & Ethics

Fairness, transparency, accountability, privacy, safety and human agency — what they mean, where they conflict, and how to work through ethical dilemmas in real AI projects.

📘 6 modules⏱ About 3.5 hours✅ Knowledge check per module🎓 Final assessment & certificate🎯 No prerequisites

0 of 6 modules complete

Start Module 1 Final assessment

Modules

  1. 1

    Why responsible AI matters

    Definitions, landmark failures in hiring, justice, education and public services, common threads and generative AI concerns.

    25 min
  2. 2

    The principles landscape

    OECD, UNESCO and UK principles; what Microsoft, Google, AWS, IBM, Anthropic and OpenAI commit to; the common core and ethics washing.

    25 min
  3. 3

    Fairness and bias

    Six sources of bias, proxies, competing fairness definitions, measuring and mitigating bias, and fairness in generative AI.

    35 min
  4. 4

    Transparency, explainability and accountability

    Levels of transparency, model cards and system cards, explainability methods, the ICO's six explanation types and accountability.

    30 min
  5. 5

    Privacy, safety and human agency

    Privacy risks across the AI lifecycle, safety and robustness, frontier safety frameworks, human oversight, inclusiveness and societal impact.

    30 min
  6. 6

    Ethics in practice

    Principles in tension, a seven-step dilemma framework, a worked case, responsible AI across the lifecycle, roles and stakeholder engagement.

    30 min