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AI & Machine Learning · Foundational

Algorithmic Bias

Systematic, unfair errors in AI outputs that disadvantage certain groups, often inherited from data.

Definition

Sources include unrepresentative training data, historical bias, label bias, proxy variables and feedback loops. Mitigation involves diverse data, bias metrics, reweighting, fairness constraints and ongoing monitoring.

Example

A hiring model penalizing resumes from women because past hires were mostly men.

Exam tip

Fixing biased outcomes starts with examining training data representativeness.

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