The AI knowledge map
Understand the systems.
Follow the evidence.
Durable explanations of how AI systems work and how organisations should reason about them. Topics group the concepts; articles work through one question at a time.
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5 published topicsAI Governance
Frameworks, regulation, and organisational practice that determine who is accountable for an AI system's behaviour and how that accountability is evidenced.
- Published work
- 1 analysis
- Cited sources
- 2 sources
Evidence & Trust
Source standards, provenance and transparency practices that make claims about AI systems inspectable, attributable and open to review.
- Published work
- 0 analyses
- Cited sources
- 2 sources
Model Evaluation
Methods and benchmarks used to measure model capabilities, reliability, limitations and the conditions under which results apply.
- Published work
- 0 analyses
- Cited sources
- 2 sources
Safety & Alignment
Techniques and operational practices for reducing AI risk, checking behaviour against intended use, and intervening when systems behave unexpectedly.
- Published work
- 0 analyses
- Cited sources
- 2 sources
Systems & Methods
Architectures, training approaches and operational methods that shape how AI systems are built, evaluated, deployed and maintained.
- Published work
- 0 analyses
- Cited sources
- 2 sources
Analyses from the publication
View the archiveWhat Is AI Governance?
A working definition of AI governance, how it differs from safety, ethics and compliance, and what separates a governance policy from a governance control.
Our evidence standard
A map you can inspect.
Sources, publication dates and explicit uncertainty remain attached to the material. Topic hubs explain the scope; the published work supplies the detail.
How claims are sourced