Governance
AI Bias
AI bias is systematic error in an AI system's outputs that produces unfair treatment of individuals or groups, typically arising from biased training data, biased labels, or model architecture choices that proxy for protected attributes. Common types include historical bias, measurement bias, aggregation bias, and deployment-context mismatch.
Bias detection requires disaggregated evaluation across demographic subgroups — aggregate accuracy metrics mask disparate impact. Mitigation strategies include data rebalancing, fairness constraints during training, post-hoc calibration, and ongoing monitoring after deployment. Regulators in lending, employment, and healthcare require documented bias testing as a condition of AI system approval.
Related terms
- AI Ethics — AI ethics is the practice of evaluating AI systems against principles like fairness, transparency, accountability, and harm reduction before deployment.
- Responsible AI — Responsible AI is an umbrella term for the operational practices that make AI deployments safe, fair, transparent, accountable, and aligned with human values — covering ethics, governance, security, privacy, and reliability across the full lifecycle.
- Explainable AI — Explainable AI (XAI) is the set of methods and practices that make an AI system's predictions understandable to humans — identifying which inputs drove which outputs.
- AI Governance — AI governance is the system of policies, controls, and accountabilities that determines what AI is allowed to do inside an organization, who approves AI deployments, how AI decisions are audited, and how risk is managed.