Explainer: What is a Proxy Variable?

The sneaky reason AI stays biased even after you remove race from the data.

The One-Sentence Definition

A proxy variable is a data feature that correlates with a protected attribute (like race, gender, or class) even though it doesn't mention it directly - so removing the protected attribute from a model doesn't remove the bias it encodes.

Why This Matters

Most people assume that removing race from an AI model makes it race-neutral. This is wrong.

Machine learning models don't care what a feature is called. They care what it predicts. If a feature like custody_status or zip_code is correlated with race in the training data, the model will learn and use that correlation - whether or not the word "race" appears anywhere in the dataset.

This is called proxy discrimination, and it's one of the hardest problems in algorithmic fairness.

Common Proxy Variables

Feature	Protected Attribute It Encodes	Why
Zip code	Race	Historical redlining segregated neighborhoods by race. Zip codes still reflect this.
Custody status	Race	Over-policing of Black communities leads to disproportionate pretrial detention.
Name	Gender / Ethnicity	Names encode gender and ethnicity with high accuracy.
College attended	Socioeconomic background	Elite colleges remain heavily stratified by class and race.
Credit score	Race / Class	Credit histories reflect historical exclusion from banking.
Prior arrests	Race	Arrest records reflect over-policing, not actual crime rates.

Real-World Proof: COMPAS Analysis

We tested this directly using the ProPublica COMPAS dataset.

What We Did

Step 1 - Biased model (includes race + proxies):

X = pd.get_dummies(df[[
    'race',
    'sex',
    'CustodyStatus',        # proxy for race
    'MaritalStatus'
]])

Results:

Group	High-Risk Flag Rate
Black defendants	87.16%
White defendants	0.40%
Fairness gap	86.77%

Step 2 - Remove race only (naive approach):

Many developers stop here. They drop the race column and assume the model is now fair. It isn't.

The CustodyStatus feature was still acting as a racial proxy. The gap barely moved.

Step 3 - Remove race AND known proxies (our fix):

X = pd.get_dummies(df[[
    'Sex_Code_Text',
    'MaritalStatus'
    # Race removed ✓
    # CustodyStatus removed ✓ (proxy for race)
]])

Results:

Group	High-Risk Flag Rate
Black defendants	84.71%
White defendants	69.02%
Fairness gap	15.69%

Summary

Approach	Fairness Gap	Reduction
Biased model	86.77%	-
Remove race only	~80%+	Minimal
Remove race + proxy	15.69%	71%

Removing the protected attribute alone is not enough. You must audit every feature for correlation with protected attributes.

How to Detect Proxy Variables

import pandas as pd
from scipy.stats import chi2_contingency

def check_proxy(df, feature, protected_attr):
    """
    Check if a feature is a proxy for a protected attribute
    using a chi-squared test of independence.
    Returns p-value - if < 0.05, likely a proxy.
    """
    contingency = pd.crosstab(df[feature], df[protected_attr])
    chi2, p, dof, expected = chi2_contingency(contingency)
    return {
        'feature': feature,
        'protected_attr': protected_attr,
        'p_value': round(p, 4),
        'is_proxy': p < 0.05
    }

# Example usage
result = check_proxy(df, 'CustodyStatus', 'race')
print(result)
# {'feature': 'CustodyStatus', 'protected_attr': 'race', 'p_value': 0.0001, 'is_proxy': True}

Run this on every feature in your dataset before training. Any feature with is_proxy: True needs careful consideration - either remove it or apply fairness-aware techniques.

The Bigger Picture

Proxy variables exist because our social world is stratified. Zip codes encode race because of redlining. Custody records encode race because of over-policing. Credit scores encode class because of historical exclusion.

Data doesn't exist in a vacuum. It reflects the society that generated it. A model trained on that data will learn those reflections - unless you actively audit and intervene.

This is why algorithmic auditing is not optional. It is a prerequisite for deployment.

COMPAS/ - Full COMPAS analysis: biased model → fair model → 71% gap reduction
Ai Fair recrutment Dataset/ - AI recruitment bias: 97.3% gap reduction after feature audit
Coming soon: Facial recognition bias, HMDA loan bias, healthcare AI

Proxy Variables