BiasAudits

Bias audits are systematic evaluations that seek to identify, measure, and mitigate bias in data sets, machine learning models, and decision systems that affect outcomes for groups defined by protected characteristics. They aim to increase fairness, transparency, and accountability in automated or semi-automated decision making.

Audits examine data collection and labeling, feature engineering, and model performance across subgroups, as well as the deployment context that shapes outcomes. Methodologies often include problem framing, data quality checks, fairness metrics, and scenario testing, sometimes conducted by independent reviewers.

Reports summarize findings, severity, and recommended remediation, and specify any limitations. Remediation options may include data augmentation, feature removal, threshold adjustments, or the use of fairness-aware models, followed by ongoing monitoring and re-evaluation.

Bias audits operate within broader governance, risk management, and regulatory contexts. They are conducted internally or by third parties, and results may be shared with regulators, customers, or the public depending on policy and law.

Limitations and critiques note that no single metric captures all aspects of fairness, and that trade-offs with accuracy or other notions of equity can occur. Effective audits require clear scope, transparent methods, and ongoing oversight as systems evolve.