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Free Guide · Risk Management

Bias Testing and Fairness Guide

Fairness testing is a control. This guide gives security, risk, and compliance teams a repeatable process to evaluate AI systems for bias, document results, and demonstrate governance to auditors.

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A practical checklist

Define the fairness objective
  • •What decision is the model influencing (hiring, lending, support triage, fraud flags)?
  • •What harm are you trying to prevent (denials, delays, false accusations, differential quality of service)?
  • •What populations are in scope (customers, employees, applicants)?
Choose metrics
  • •Group parity metrics (selection rate, false positive/negative rates)
  • •Calibration/score reliability across groups
  • •Distribution shift monitoring
  • •For GenAI: toxicity, stereotyping, and refusal consistency
Design test datasets
  • •Collect representative samples for each group (or use controlled synthetic data where appropriate)
  • •Define ground truth labels and reviewer guidance
  • •Log dataset provenance and consent/collection constraints
Run evaluations
  • •Evaluate metrics overall and per group
  • •Test on edge cases and adversarial prompts (for GenAI)
  • •Repeat across model versions and configuration changes
Document results
  • •Record metrics, thresholds, and pass/fail criteria
  • •Attach evidence (dashboards, notebooks, test logs)
  • •Summarise limitations and known failure modes
Remediate and retest
  • •Apply mitigations (data balance, post-processing, policy controls, guardrails)
  • •Rerun the full evaluation suite
  • •Implement monitoring to detect regression

What to include in a fairness report

•Model/version and configuration
•Intended use + out-of-scope use
•Groups evaluated and data sources
•Metrics and thresholds
•Results and identified gaps
•Mitigations applied
•Residual risk acceptance
•Monitoring plan and alert thresholds
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  • Updated June 2026
  • NIST AI RMF · ISO 42001 · EU AI Act aligned

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