fairlearn-fairness

fairlearn-fairness

Fairlearn provides "Metrics - Tools to assess which groups are negatively impacted and compare models across fairness and accuracy dimensions" and "Algorithms - Techniques to mitigate unfairness" per the Fairlearn quickstart. Two primitives: MetricFrame (group disaggregation) + Reductions (ExponentiatedGradient, ThresholdOptimizer).

When to use

• Pre-deployment: assert per-group accuracy / selection rate disparities are within budget.
• Bias incident triage: a stakeholder reports the model is unfair to group X; produce evidence + a mitigated comparison.
• Compliance evidence (ECOA, GDPR Art. 22, EU AI Act high-risk systems): group-disaggregated metrics + mitigation provenance.

Step 1 - Install

pip install fairlearn
# OR
conda install -c conda-forge fairlearn

Per the Fairlearn quickstart.

Step 2 - Compute disaggregated accuracy

from fairlearn.metrics import MetricFrame
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier

classifier = DecisionTreeClassifier(min_samples_leaf=10, max_depth=4)
classifier.fit(X, y_true)
y_pred = classifier.predict(X)

mf = MetricFrame(
    metrics=accuracy_score,
    y_true=y_true,
    y_pred=y_pred,
    sensitive_features=sex,
)
print(mf.by_group)
print(f"Disparity (max-min): {mf.difference()}")

Per the Fairlearn quickstart. sensitive_features can be a Series or a 2-D array for intersectional analysis (sex × race).

Step 3 - Compute selection-rate disparity

from fairlearn.metrics import selection_rate

sr = MetricFrame(
    metrics=selection_rate,
    y_true=y_true,
    y_pred=y_pred,
    sensitive_features=sex,
)
print(sr.by_group)
# Demographic Parity Difference (DPD)
print(f"DPD: {sr.difference()}")

DPD = max group selection rate − min group selection rate. Industry guidance often cites the 80% rule (selection rate ratio ≥ 0.8 between groups) as a soft threshold; consult legal counsel for binding thresholds in your jurisdiction.

Step 4 - Equalized odds (TPR + FPR per group)

from fairlearn.metrics import (
    true_positive_rate,
    false_positive_rate,
    MetricFrame,
)

mf = MetricFrame(
    metrics={
        "TPR": true_positive_rate,
        "FPR": false_positive_rate,
        "selection_rate": selection_rate,
    },
    y_true=y_true,
    y_pred=y_pred,
    sensitive_features=sex,
)
print(mf.by_group)

Equalized Odds requires both TPR and FPR to be equal across groups - stricter than Demographic Parity.

Step 5 - Mitigation via Reductions

from fairlearn.reductions import DemographicParity, ExponentiatedGradient

constraint = DemographicParity()
mitigator = ExponentiatedGradient(classifier, constraint)
mitigator.fit(X, y_true, sensitive_features=sex)
y_pred_mitigated = mitigator.predict(X)

Per the Fairlearn quickstart: this approach significantly reduces selection-rate differences while maintaining accuracy. Other constraints: EqualizedOdds, TruePositiveRateParity, FalsePositiveRateParity.

Step 6 - Threshold post-processing

from fairlearn.postprocessing import ThresholdOptimizer

postprocess = ThresholdOptimizer(
    estimator=classifier,
    constraints="demographic_parity",
    prefit=True,
)
postprocess.fit(X, y_true, sensitive_features=sex)
y_pred_pp = postprocess.predict(X, sensitive_features=sex)

Cheaper than retraining; trades model output for per-group threshold adjustment.

Step 7 - CI assertion

def assert_fairness(y_true, y_pred, sensitive, max_dpd=0.10):
    sr = MetricFrame(
        metrics=selection_rate,
        y_true=y_true,
        y_pred=y_pred,
        sensitive_features=sensitive,
    )
    dpd = sr.difference()
    if dpd > max_dpd:
        raise AssertionError(
            f"Demographic Parity Difference {dpd:.3f} exceeds budget {max_dpd}"
        )

assert_fairness(y_true, y_pred, sex, max_dpd=0.10)

Anti-patterns

Anti-pattern	Why it fails	Fix
Compute aggregate accuracy only	Hides group disparities	Always use MetricFrame (Step 2)
Choose Demographic Parity for all problems	DP can be inappropriate when base rates legitimately differ across groups	Match constraint to legal/ethical context: DP, EO, EOD, EOP
Mitigate via training data resampling alone	Doesn't generalize to new data; brittle	Use Reductions (Step 5) or post-processing (Step 6)
Single sensitive attribute (e.g., sex only)	Misses intersectional disparities (Black women)	Pass 2-D sensitive_features for intersection (Step 2)
Hard-code 80% rule globally	Not legally binding everywhere; not appropriate for all metrics	Tune max_dpd per use case + legal counsel; use waiver template if scope-exclusion needed

Limitations

• Fairlearn does not detect proxy discrimination (zip code as proxy for race). Pair with feature-correlation analysis and domain expertise.
• Mitigation often comes at accuracy cost; document the tradeoff with stakeholders, not unilaterally.

References

• Fairlearn quickstart - MetricFrame, selection_rate, ExponentiatedGradient, ThresholdOptimizer