AI ethics
Definition
AI ethics covers principles (fairness, transparency, accountability, privacy) and governance for designing and deploying AI. It includes codes of conduct, impact assessments, and regulation.
It connects to AI safety (risks, alignment), bias in AI (fairness), and explainable AI (transparency). Regulation (e.g. EU AI Act) is making ethics and impact assessments mandatory for high-risk applications. Organizations and practitioners need to operationalize principles into design, evaluation, and deployment practices.
How it works
Organizations adopt guidelines (e.g. responsible AI principles) and review processes (e.g. ethics boards, impact assessments). Regulators set requirements (e.g. EU AI Act: risk tiers, transparency, human oversight). Practitioners use checklists (e.g. data provenance, fairness metrics), audits (e.g. bias, safety), and stakeholder input to align systems with ethical norms. Flow: identify use case and risk level → assess impact (who is affected, what could go wrong) → implement safeguards (data, model, explainability, human-in-the-loop) → monitor and iterate. Documentation and accountability (who is responsible for what) are part of governance.
Use cases
AI ethics and governance apply when you need to align systems with norms, regulation, and accountability (impact, fairness, privacy).
- Impact assessments and governance for new AI products
- Aligning with regulation (e.g. EU AI Act) and sector codes
- Privacy, fairness, and accountability in design and deployment