GLOSSARY

An international standard providing guidance on 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 ISO/IEC 24027 bias evaluation metrics into its validation pipeline, assessing disparate impact across demographic cohorts before releasing candidate ranking models.

Eliminating statistical disparity across all attributes simultaneously is mathematically impossible; technical bias reduction cannot resolve underlying societal inequalities or flawed policy objectives.

The standard 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.