Phase 2: Bias Detection & Fairness Metrics · 50 min · Python · Fairlearn · AIF360

Bias Mitigation — Pre-processing, In-processing, Post-processing

Detecting bias is diagnosis. Mitigating it is treatment. You need both.

Hiring signal: Being able to apply bias mitigation techniques and measure the accuracy-fairness tradeoff is the skill that separates RAI auditors from RAI engineers. Interviewers ask 'you found bias, now what?' — and the answer needs to include specific mitigation algorithms and their measured tradeoffs.

What you will learn

Apply reweighing (pre-processing) to balance the dataset
Apply exponentiated gradient reduction (in-processing) during model training
Apply threshold optimization (post-processing) to adjust decision boundaries
Measure and plot the accuracy-fairness tradeoff across all three techniques

The Problem

You've run the bias audit. The numbers show disparate impact below 0.80 and equalized odds difference above 0.05. The model is biased. Now what?

Bias mitigation is where RAI engineering gets real. You have three intervention points — before training (pre-processing), during training (in-processing), and after training (post-processing). Each has tradeoffs:

Intervention	When	What It Changes	Pros	Cons
Pre-processing	Before training	The training data	Simple, model-agnostic, preserves the original model	Doesn't fix model-internal bias; limited effectiveness
In-processing	During training	The training objective	Most effective; directly optimizes for fairness	Model-specific; harder to implement; may significantly reduce accuracy
Post-processing	After training	The decision threshold	Simple, model-agnostic, no retraining needed	Doesn't fix the model; may be legally problematic (explicitly using protected attribute at decision time)

The 3 Techniques You'll Build

1. Reweighing (Pre-processing)

Reweighing assigns weights to training examples so that the joint distribution of the protected attribute and the label matches what it would be if they were independent. In simpler terms: it upweights under-represented combinations (e.g., Group_B with positive outcome) and downweights over-represented ones.

from fairlearn.preprocessing import CorrelationRemover
# Or use AIF360's Reweighing algorithm:
from aif360.algorithms.preprocessing import Reweighing

2. Exponentiated Gradient Reduction (In-processing)

This technique wraps your model in a fairness-constrained optimization. It trains the model while penalizing fairness violations, finding the best accuracy-fairness tradeoff on the Pareto frontier.

from fairlearn.reductions import ExponentiatedGradient, DemographicParity, EqualizedOdds

mitigator = ExponentiatedGradient(
    estimator=RandomForestClassifier(),
    constraints=EqualizedOdds(),  # or DemographicParity()
)
mitigator.fit(X_train, y_train, sensitive_features=sensitive_train)
y_pred_mitigated = mitigator.predict(X_test)

3. Threshold Optimization (Post-processing)

This technique finds different decision thresholds for each group to equalize a fairness metric. It's the simplest to implement but the most legally sensitive — it explicitly uses the protected attribute at decision time.

from fairlearn.postprocessing import ThresholdOptimizer

postprocess = ThresholdOptimizer(
    estimator=model,  # pre-trained model
    constraints="equalized_odds",
)
postprocess.fit(X_train, y_train, sensitive_features=sensitive_train)
y_pred_post = postprocess.predict(X_test, sensitive_features=sensitive_test)

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Building the Mitigation Comparison Module, What's Next — plus a hands-on lab, quiz, and project artifact.

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