Audit score fairness

Wealth and capacity features (estimated net worth, real-estate value, geography) can stand in for protected characteristics. A model that never sees a protected attribute can still produce disparate outcomes through those proxies. philanthropy.metrics gives you the two diagnostics for checking.

A diagnostic, not a clearance

A passing ratio does not certify a model as non-discriminatory. The choice of protected groups and of decision threshold materially changes the result. Involve your equity and compliance stakeholders before acting on scores.

Threshold first, then audit

Both functions take binary decisions, not continuous scores. The threshold is part of what you are auditing: the same model can pass at one cut-off and fail at another.

import numpy as np
import pandas as pd

from philanthropy.datasets import generate_synthetic_donor_data
from philanthropy.metrics import disparate_impact_ratio, selection_rate_by_group
from philanthropy.models import DonorPropensityModel

rng = np.random.default_rng(0)
df = generate_synthetic_donor_data(n_samples=1200, random_state=42)
cols = ["total_gift_amount", "years_active", "event_attendance_count"]

model = DonorPropensityModel(n_estimators=50, random_state=0).fit(
    df[cols].to_numpy(), df["is_major_donor"].to_numpy()
)
scores = model.predict_affinity_score(df[cols].to_numpy())

# A protected attribute the model never saw. In production this is a real CRM
# field, joined in only for the audit, not a feature.
region = rng.choice(["north", "south", "east", "west"], size=len(df))

flagged = (scores >= 70).astype(int)
rates = selection_rate_by_group(flagged, region)
print({g: round(r, 3) for g, r in sorted(rates.items())})

selection_rate_by_group returns the fraction selected within each group. Read it before the ratio: a ratio of 0.5 means something very different at rates of 0.80/0.40 than at 0.002/0.001.

The four-fifths rule

disparate_impact_ratio is min(selection_rate) / max(selection_rate). The US EEOC's four-fifths rule flags anything below 0.8 as evidence of adverse impact warranting investigation.

ratio = disparate_impact_ratio(flagged, region)
print(f"disparate impact ratio: {ratio:.3f}")

if ratio < 0.8:
    print("below the four-fifths threshold; investigate before acting")

assert 0.0 <= ratio <= 1.0
# The ratio is exactly min/max over the per-group selection rates.
assert ratio == min(rates.values()) / max(rates.values())

The EEOC's own worked example: 80 of 100 selected in one group and 40 of 100 in another gives 0.50.

y_pred = [1] * 80 + [0] * 20 + [1] * 40 + [0] * 60
groups = ["A"] * 100 + ["B"] * 100
assert disparate_impact_ratio(y_pred, groups) == 0.5
assert selection_rate_by_group(y_pred, groups) == {"A": 0.8, "B": 0.4}

Sweep the threshold

The ratio is a function of where you cut. Audit the cut-off you actually plan to use, and look at the curve around it.

sweep = pd.DataFrame([
    {
        "threshold": t,
        "flagged": int((scores >= t).sum()),
        "ratio": round(disparate_impact_ratio((scores >= t).astype(int), region), 3),
    }
    for t in (50, 60, 70, 80, 90)
])
print(sweep.to_string(index=False))

A ratio that swings across the sweep is telling you the disparity is threshold-driven, not structural. One that is flat and low is telling you the model has learned a proxy.

Missing group labels are refused, not imputed

Silently dropping donors with an unknown protected attribute biases the audit toward whoever your CRM records completely. Both functions raise instead.

import pytest

with pytest.raises(ValueError, match="missing"):
    disparate_impact_ratio([1, 0, 1], [np.nan, 1.0, 1.0])

with pytest.raises(ValueError, match="missing"):
    selection_rate_by_group([1, 0, 1], [np.nan, 1.0, 1.0])

Decide what an unknown group means for your audit and encode it explicitly, as its own "unknown" category, or by scoping the audit to the recorded population and saying so.

What to do with a failing ratio

Not "drop the feature". Removing estimated_net_worth does not remove the proxy if real_estate_value and zip code carry the same signal. Use philanthropy.inspection.donor_feature_importance to find which features drive the gap, then decide with your stakeholders whether the disparity reflects genuine capacity differences or an artifact of your data.

from philanthropy.inspection import donor_feature_importance

importance = donor_feature_importance(
    model, df[cols].to_numpy(), df["is_major_donor"].to_numpy(),
    feature_names=cols, random_state=0,
)
print(importance)

See Compliance considerations before this reaches production.