Algorithmic Bias
Systematic and repeatable errors in an AI system that produce unfair outcomes, often caused by biased training data, flawed model design or unexamined assumptions built into the system.
In plain language
When AI consistently favours one group over another, usually unintentionally. For example, if an AI trained mostly on resumes of men learns to prefer male candidates, that's algorithmic bias embedded in the system.
Why this matters
Bias is one of the most common and consequential AI risks. Your governance framework must include processes to detect, measure and remediate bias throughout the AI lifecycle or your organisation faces regulatory enforcement action, legal liability and reputational damage.
Relevance
GovernanceBias detection and mitigation are governance imperatives. Frameworks should mandate fairness testing, impact monitoring and remediation processes proportionate to the consequences of biased decisions.
Related terms
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