Systematic, unfair skew in an AI system's outputs that can disadvantage certain groups or individuals.
AI bias arises when a system produces results that are systematically unfair, often reflecting bias in its training data, its design, or how it is used. It can lead to discriminatory outcomes in areas such as hiring, lending, or healthcare.
Managing bias involves representative data, testing across groups, ongoing monitoring, and documentation. Fairness and non-discrimination are central concerns of both ISO/IEC 42001 and the EU AI Act.
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