Also known as: AI regulation · AI policy · AI Act
AI governance refers to the policies, regulations, and internal controls that determine how AI systems may be developed and deployed, including risk assessment, transparency obligations, and accountability for harms.
Regulatory approaches differ structurally. Risk-tiered frameworks such as the EU AI Act impose obligations proportional to application risk; others focus on model capability thresholds, or on sector-specific rules layered onto existing regulation.
For organisations deploying rather than building models, the practical obligations usually centre on documentation, human oversight of consequential decisions, and disclosure to affected individuals — requirements that arrive well before any question of frontier model capability.
Training Data Provenance
Training data provenance is documentation of where the data used to train a model came from, including its sources, licensing status, and any known rights disputes.
AI Alignment
AI alignment is the field concerned with ensuring AI systems reliably pursue the goals their operators actually intend, rather than the literal specification given or some proxy the system learned during training.
AI Evaluation
AI evaluation is the practice of systematically measuring model performance against defined tasks and criteria, using benchmarks, human review, or automated graders to quantify capability and detect regressions.
GDPR
The GDPR (General Data Protection Regulation) is the European Union's data protection law, which governs how personal data of people in the EU may be collected and processed, and grants individuals rights over their data regardless of where the processing organisation is based.