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AI Ethics & Governance

The societal side of AI — fairness, accountability, and the laws and norms now shaping how AI can be built and used.

Artificial intelligence now helps decide who gets a loan, whose job application is read, and which faces a camera unlocks. AI ethics and governance is the field that asks whether those decisions are fair, who is responsible when they go wrong, and what rules society should set.

Think of a brand-new sport that everyone suddenly starts playing at once. At first there is no referee, no rulebook, and no agreement on what counts as cheating. AI is at that stage: powerful, but racing ahead of the rules meant to keep it fair and safe. Governance is the slow work of writing that rulebook — through laws, company policies, and shared habits — while the game is still being played.

It covers hidden bias, honesty about how systems work, copyright, fake media, and how automation reshapes everyday jobs.

The main ideas

  • Bias & fairness — Where discrimination enters ML systems, and formal notions of fairness (and their trade-offs).
  • Accountability & transparency — Who is responsible when AI causes harm; documentation like model and data cards.
  • Regulation — The EU AI Act, US executive actions, and emerging global rules for high-risk AI.
  • Copyright & data rights — Training data, ownership, consent, and the law around generative outputs.
  • Misinformation & deepfakes — Synthetic media, provenance, watermarking, and trust.
  • Economic & labor impact — Automation, the future of work, and who benefits from AI.

AI Safety, Alignment & Ethics · Privacy & Security in AI · Interpretability & Explainability

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