bias-audit

name	bias-audit
description	Audit dataset bias across protected attributes — demographic parity, equalized odds, representation gaps, and intersectional bias. Reports actionable gaps with per-group metrics.
tags	["bias-audit","fairness","demographics","representation","equalized-odds","dataset-curation"]

Bias Audit

Overview

Bias in training data produces biased models, full stop. This audit measures representation, outcome disparities, and intersectional gaps so you can fix problems before training.

When to Use

Use when: building models that make decisions about people, deploying in regulated domains, or when protected attributes (gender, race, age, etc.) are available.

Core Metrics

Representation Audit

import pandas as pd
import numpy as np

def representation_audit(df, protected_cols, population_benchmark=None):
    """Check if dataset representation matches population."""
    n = len(df)
    results = {}
    
    for col in protected_cols:
        dist = df[col].value_counts(normalize=True).to_dict()
        results[col] = {
            "distribution": dist,
            "n_groups": len(dist),
            "min_group_pct": min(dist.values()),
            "max_group_pct": max(dist.values()),
            "imbalance_ratio": max(dist.values()) / (min(dist.values()) + 1e-10),
        }
    
    # Intersectional audit
    if len(protected_cols) >= 2:
        intersectional = df.groupby(protected_cols).size() / n
        min_intersection = intersectional.min()
        results["intersectional"] = {
            "n_intersections": len(intersectional),
            "min_pct": min_intersection,
            "empty_groups": (intersectional == 0).sum(),
        }
    
    return results

Outcome Parity Audit

def outcome_audit(df, label_col, protected_col, positive_label=1):
    """Check if outcomes differ across protected groups."""
    groups = df.groupby(protected_col)
    
    metrics = {}
    for group, data in groups:
        metrics[group] = {
            "n": len(data),
            "positive_rate": (data[label_col] == positive_label).mean(),
            "label_distribution": data[label_col].value_counts().to_dict(),
        }
    
    # Disparity metrics
    pos_rates = [m["positive_rate"] for m in metrics.values()]
    disparity = max(pos_rates) - min(pos_rates)
    
    return {
        "per_group": metrics,
        "max_disparity": disparity,
        "disparity_ratio": max(pos_rates) / (min(pos_rates) + 1e-10),
    }

Thresholds That Matter

Metric	Green	Yellow	Red
Group size ratio (max/min)	< 3:1	3:1-10:1	> 10:1
Outcome disparity	< 5pp	5-15pp	> 15pp
Min intersection group	> 1%	0.1-1%	< 0.1%

Remediation Plan

1. Under-represented groups: Oversample, collect more data, or use synthetic augmentation.
2. Outcome disparity: Check if label quality differs across groups. Check if label definition is biased.
3. Intersectional gaps: Report even if you can't fix — don't hide zero-count cells.
4. Document: What you measured, what you found, what you did about it. Transparency is the minimum bar.