Algorithmic Bias

Systematic and repeatable errors in an AI system that produce unfair outcomes, such as privileging one group of users over another, often arising from biased training data or flawed model design.

When AI consistently favours one group over another, often unintentionally. For example, if an AI trained mostly on men's resumes learns to prefer male candidates, that's algorithmic bias.

Bias is one of the most significant and common AI risks. Your governance framework must include processes to detect, measure and address bias throughout the AI lifecycle or your organisation faces regulatory, legal and reputational consequences.