Éthique de l'IA
Définition
L'éthique de l'IA couvre les principes (équité, transparence, responsabilité, vie privée) et la gouvernance for conceptioning 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 (par ex. EU AI Act) is making ethics and impact assessments mandatory for high-risk applications. Organizations and practitioners need to operationalize principles into conception, evaluation, and deployment practices.
Comment ça fonctionne
Organizations adoptent des directives (par ex. principes d'IA responsable) and review processes (par ex. ethics boards, impact assessments). Regulators set requirements (par ex. EU AI Act: risk tiers, transparency, human oversight). Practitioners use checklists (par ex. data provenance, fairness metrics), audits (par ex. 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.
Cas d'utilisation
L'éthique et la gouvernance de l'IA s'appliquent lorsqu'il faut aligner les systèmes avec les normes, la réglementation et la responsabilité (impact, équité, vie privée).
- Impact assessments and governance for new AI products
- Aligning with regulation (par ex. EU AI Act) and sector codes
- Privacy, fairness, and accountability in conception and deployment