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A technical report describing techniques for measuring, assessing, and mitigating bias across AI system lifecycles and decision-making processes.

Mitigating unwanted bias is vital for reducing regulatory penalties, protecting brand reputation, and ensuring fair outcomes in consumer-facing and hiring algorithms.

A hiring software vendor integrates bias evaluation metrics described in ISO/IEC TR 24027 into its validation pipeline, assessing disparate impact across demographic cohorts before releasing candidate ranking models.

Common fairness criteria, such as calibration and equal error rates across groups, generally cannot all be met at once when base rates differ; technical bias reduction cannot resolve underlying societal inequalities or flawed policy objectives.

The report analyzes sources of bias across the AI lifecycle, including historical prejudice, sample representation errors, labeling flaws, and proxy variables. It defines mathematical fairness criteria, measurement techniques, and mitigation approaches spanning pre-processing data adjustments, in-processing model constraints, and post-processing threshold calibration.