Mitigation

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Introduction

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Note

Bias mitigation can be approached through pre-processing, in-processing, and post-processing methods.

Bias in AI systems can lead to systematic disadvantages for certain individuals or groups. To address and reduce these biases, several mitigation methods can be employed throughout the development process, from data generation to model deployment. This document outlines various techniques and strategies for mitigating bias in AI systems.

Pre-processing Methods

Pre-processing techniques aim to mitigate bias by transforming the data before it is used to train a model. This can include:

• Reweighting: Adjusting the weights of data points to ensure balanced representation across groups.

• Resampling: Over-sampling underrepresented groups or under-sampling overrepresented groups to achieve a balanced dataset.

• Data Augmentation: Generating synthetic data to bolster the representation of minority groups.

• Fair Representation Learning: Learning new representations of data that remove sensitive information while retaining useful information for prediction.

In-processing Methods

In-processing techniques modify the learning algorithm itself to reduce bias during the model training phase. This can include:

• Adversarial Debiasing: Training the model in conjunction with an adversary that penalizes the model for producing biased predictions.

• Fairness Constraints: Incorporating constraints that enforce fairness criteria directly into the optimization objective of the learning algorithm.

• Regularization Techniques: Adding regularization terms to the loss function that penalize bias.

Post-processing Methods

Post-processing techniques adjust the predictions of the trained model to mitigate bias. This can include:

• Calibrated Equalized Odds: Adjusting the decision thresholds for different groups to achieve equalized odds across groups.

• Reject Option Classification: Allowing uncertain predictions to be reconsidered, particularly for disadvantaged groups, to improve fairness.

• Output Adjustment: Modifying the output probabilities to ensure fair treatment across groups.

• Pre-processing Methods
• In-processing Methods
• Post-processing Methods

Summary Table

The following table summarizes the bias mitigation methods and their applicability at different Machine Learning (ML) tasks.

Table 4 Bias Mitigation Methods

Type Strategy	Method name	Binary Classification	Multi Classification	Regression	Clustering	Recommender Systems
Preprocessing	Correlation Remover	X	X	X
Preprocessing	Disparate Impact Remover	X	X	X	X
Preprocessing	Learning Fair Representation	X
Preprocessing	Reweighing	X	X
Preprocessing	Fair Clustering				X
Inprocessing	Adversarial Debiasing	X
Inprocessing	Exponentiated Gradient	X		X
Inprocessing	Fair K Center Clustering				X
Inprocessing	Fair K Median Clustering				X
Inprocessing	Fair Scoring Classifier		X
Inprocessing	Fairlet Clustering				X
Inprocessing	Grid Search	X		X
Inprocessing	Debiasing Learning					X
Inprocessing	Blind Spot Aware					X
Inprocessing	Popularity Propensity					X
Inprocessing	Meta Fair Classifier	X
Inprocessing	Prejudice Remover	X
Inprocessing	Two Sided Fairness					X
Inprocessing	Variational Fair Clustering				X
Postprocessing	Debiasing Exposure					X
Postprocessing	Fair Top-K					X
Postprocessing	LP Debiaser	X	X
Postprocessing	MCMF Clustering				X
Postprocessing	ML Debiaser	X	X
Postprocessing	Plugin Estimator and Recalibration			X
Postprocessing	Wasserstein Barycenters			X
Postprocessing	Calibrated Equalized Odds	X
Postprocessing	Equalized Odds	X
Postprocessing	Reject Option Classification	X
Postprocessing	Disparate Impact Remover for RS					X