What is AI bias risk?

Sources of AI bias and how they manifest

AI bias arises at multiple points in an AI system’s development and deployment. Training data bias is the most fundamental: if the data used to train an AI system over-represents certain groups, under-represents others, or contains historical discrimination, the AI will learn and reproduce those patterns. A hiring AI trained predominantly on CVs of successful employees from one demographic group will tend to rate candidates from other groups lower; not because of explicit discrimination, but because of the patterns in the training data. Model design bias arises from choices made in how the AI is built: what features are included, what outcomes are optimised for, what trade-offs are made. Deployment bias arises from how the AI is used: applying an AI system in contexts different from those it was trained on, or using AI recommendations without human review, can amplify biases that would otherwise be moderated.

What bias testing requires under the EU AI Act

The high-risk provider regime includes requirements relevant to data quality and bias, with duties and dates determined by the applicable provisions. A deployer should examine the supplier's evidence and test whether it is relevant to the people and context involved. Article 27 fundamental-rights assessment applies to certain public bodies, private public-service entities and specified credit and insurance uses, with its stated exceptions; it is not universal for every employer or buyer of AI. Equality law and GDPR can require action independently of that classification.

Legal references reviewed 4 October 2026. High-risk provisions have phased application and transition rules. Sources: Commission timetable, deployer duties, impact-assessment scope and literacy guidance.