course-lesson
Agentic AI: Autonomy Under Guardrails
Agentic systems plan, use tools, and act over multiple steps with limited supervision. This part covers why autonomy multiplies both value and risk, and how to bound an agent so it is useful inside a boundary you can define and defend.
Testing and Validation of AI Systems
Validation is the independent judgement that an AI system is fit for purpose. This part covers what to test beyond accuracy, the principle of independence, and why validation is continuous rather than a one-time gate.
Documentation and the Audit Trail
In regulated AI, a control you cannot evidence is a control that does not exist. This part covers what to document, the difference between documentation and a live audit trail, and how to generate evidence as a by-product of operation.
Human-in-the-Loop Design
Human oversight is one of the most relied-upon controls in regulated AI — and one of the most frequently hollow. This part covers how to design oversight that is genuine, where to place it, and how to avoid the traps that make it theatre.
Fairness and Bias: Measurement and Mitigation
Fairness is where AI risk becomes most visible and most contested. This part covers how bias enters systems, why fairness has competing mathematical definitions, how to measure disparate impact, and the trade-offs of mitigation.
Privacy, Lawful Basis, and Data Minimisation
AI runs on personal data, which pulls it into the heart of data-protection law. This part covers lawful basis, purpose limitation, minimisation, individual rights, and the special rules around automated decision-making.
Data Governance and Lineage
Trustworthy AI rests on trustworthy data. This part covers data quality, the discipline of end-to-end lineage, and why knowing exactly where every input came from is the foundation of both fairness and defensibility.
Designing for Explainability from Day One
Explainability is not a feature you add at the end; it is a property you design in or lose. This part covers the kinds of explanation different audiences need, the techniques available, and the architectural choices that keep decisions reconstructable.
Model Risk Management for AI
Model risk management is a mature discipline with decades of regulatory pedigree. This part shows how its core ideas — the model lifecycle, independent validation, and the model inventory — extend to AI, and where machine learning breaks its assumptions.
Governance Foundations: Roles and Accountability
Accountability for an AI decision must rest with a named human, not "the algorithm". This part lays out the roles, the three-lines-of-defence model, and how to make ownership real rather than a box on an org chart.
Risk Classification: Tiering AI by Impact
Governance effort should track the harm a system can do. This part covers how to classify AI systems by impact, the dimensions that drive a tier, and how to document and defend a classification under challenge.
The Regulatory Landscape
AI regulation is not one thing but several overlapping regimes. This part maps the layers — horizontal AI law, sectoral rules, data protection, and internal policy — and shows how to build a single obligation map for your systems.