Auditing Algorithmic Bias in Practice
A practical toolkit for identifying and mitigating bias in deployed AI systems — scoping, measurement, testing, remediation, and governance.
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Auditing Algorithmic Bias in Practice: A Framework for Accountability
A ~11-minute audio interview about the report with Dr. Krzysztof Pietroszek, President of FAIR Labs. Produced with www.allais.com.
Summary
The bottom line for auditors
You cannot audit bias by running one metric and comparing it to a threshold. A credible audit fixes a scope, chooses a fairness definition that fits the decision and its harms, checks whether the data can even support the measurement, computes a small battery of complementary tests rather than a single number, and reports disparities with the uncertainty and the assumptions attached. Where it finds harm, it reaches first for the cheapest durable fix—often a change to the data, the threshold, or the human workflow, not a new model—and it writes the whole thing down so an independent party can check it and repeat it later. This toolkit supplies each step as a ready-to-run instrument.
A practical toolkit for identifying and mitigating bias in deployed AI systems — scoping, measurement, testing, remediation, and governance.
Key findings
- Choose the fairness definition before you measure. Group parity, calibration, and individual fairness cannot generally be satisfied at once. Decide which harm the decision most needs to avoid, pick the metric that speaks to it, and record why—so the choice is a governed judgment rather than a convenient default.
- Audit the data before the model. Proxies for protected attributes, undersampled groups, and biased labels manufacture disparity that no downstream metric can distinguish from the model's own behavior. Interrogate provenance and representativeness first; a clean metric on dirty data is a false negative.
How to cite this report
Auditing Algorithmic Bias in Practice. FAIR Labs Toolkit. Fair Artificial Intelligence Research Labs, 2026. Available at https://fairlabs.ai/research/bias-audit-toolkit
FAIR Labs (Fair Artificial Intelligence Research Labs) is a nonpartisan 501(c)(3) research institute in Washington, DC. Its reports may be reproduced with attribution for non-commercial purposes. For interviews, briefings or data requests, use the contact form.