Measuring and mitigating bias

Mental model: mitigation is an experiment on a harm, not a knob that makes a dashboard green. Name the affected decision, people, error consequence, and baseline; then change the earliest mechanism that plausibly creates the harm and test both the intended benefit and what the change breaks.

Mechanism: audit → hypothesis → intervention → release gate

Start with a versioned dataset and decision policy. Slice outcomes by relevant group or context, inspect examples and denominators, trace the gap to data, labels, threshold, workflow, or interface, and choose a control at that layer. Re-run the same slice suite against a holdout before a release decision.

def fnr(rows):
    positives = [r for r in rows if r[0]]
    return sum(not r[1] for r in positives) / len(positives)
before = [(1,1), (1,0), (1,0), (0,0)]
after  = [(1,1), (1,1), (1,0), (0,1)]
print("before", fnr(before), "after", fnr(after))
assert fnr(after) < fnr(before)

Run with python3; expected output shows the false-negative rate improves from about 0.67 to 0.33. It also introduces a false positive, so this is evidence to weigh, not an automatic approval.

Intervention map

Track sample size, intervals, missing-group rate, decision threshold, and the exact model/prompt/data version. Mitigations can improve a selected metric while worsening calibration, privacy, utility, or an unmeasured subgroup; report the comparison rather than optimizing in secret.

Production lens, failures, and decision rule

Monitor slice metrics, complaints, appeals, overrides, and data drift after launch. Roll back or route to review when a predeclared disparity or harm threshold is crossed. Do not collect sensitive attributes without a justified governance and privacy basis; do not call a group gap proof of cause without investigating the data-generating process. Release only when an accountable owner accepts the documented residual risk and recourse path.

Exercises

1. Add false-positive rate and a minimum sample-size guard to the artifact.
2. Design a non-model intervention for a biased support-routing workflow.

Connects to: metric tradeoffs · task evals · monitoring · accountability

Sources

• NIST SP 1270 — bias identification and management across the lifecycle.
• Fairness and Machine Learning — metrics, interventions, and technical limits.
• NIST AI RMF Playbook: Measure — evaluation and governance actions.