Disparate Impact Remover

Description

Disparate Impact Remover is a preprocessing technique that transforms feature values in a dataset to reduce statistical dependence between features and protected attributes (like race or gender). The method modifies non-protected features through mathematical transformations that preserve the utility of the data whilst reducing correlations that could lead to discriminatory outcomes. This approach specifically targets the '80% rule' disparate impact threshold by adjusting feature distributions to ensure more equitable treatment across demographic groups in downstream model predictions.

Example Use Cases

Fairness

Transforming features in a credit scoring dataset where variables like 'years of employment' and 'education level' are correlated with race, applying mathematical transformations to reduce these correlations whilst preserving the predictive value for creditworthiness assessment.

Preprocessing a recruitment dataset where features like 'previous job titles' and 'university attended' correlate with gender, modifying these features to ensure the '80% rule' is met whilst maintaining useful information for predicting job performance.

Transparency

Preprocessing financial lending data to provide transparent bias metrics showing the quantified reduction in correlation between protected attributes and creditworthiness features, enabling institutions to demonstrate compliance with the 80% rule and explain their fairness interventions to regulators.

Reliability

Ensuring consistent model performance across demographic groups in healthcare risk assessment by mathematically transforming features to reduce protected attribute correlations, improving reliability of predictions for minority populations who may have been systematically under-served.

Limitations

Feature transformations may reduce model accuracy by removing or distorting important predictive information during the debiasing process.
Only addresses measured protected attributes and cannot eliminate bias that operates through unmeasured proxy variables.
Effectiveness depends on the specific transformation method chosen and may not generalise well to different datasets or domains.
May create artificial feature distributions that don't reflect real-world data patterns, potentially causing issues in model deployment.

Resources

Software Packages

holisticai

Jan 26, 2023

This is an open-source tool to assess and improve the trustworthiness of AI systems.

AIF360

Aug 22, 2018

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Documentations

Disparate Impact Remover — holisticai documentation

Holisticai Developers•Jan 1, 2023

aif360.algorithms.preprocessing.DisparateImpactRemover — aif360 ...

Aif360 Developers•Jan 1, 2018

Related Techniques

Name	Description	Assurance Goals
Reweighing	Reweighing is a pre-processing technique that mitigates bias by assigning different weights to training examples based on their group membership and class label. The weights are calculated to ensure that privileged and unprivileged groups have equal influence on the model's training process, effectively balancing the dataset without altering the feature values themselves. This helps to train fairer models by correcting for historical imbalances in how different groups are represented in the data.	Fairness Transparency Reliability
Relabelling	A preprocessing fairness technique that modifies class labels in training data to achieve equal positive outcome rates across protected groups. Also known as 'data massaging', this method identifies instances that contribute to discriminatory patterns and flips their labels (from positive to negative or vice versa) to balance the proportion of positive outcomes between demographic groups. The technique aims to remove historical bias from training data whilst preserving the overall class distribution, enabling standard classifiers to learn from discrimination-free datasets.	Fairness Transparency Reliability
Preferential Sampling	A preprocessing fairness technique developed by Kamiran and Calders that addresses dataset imbalances by re-sampling training data with preference for underrepresented groups to achieve discrimination-free classification. This method modifies the training distribution by prioritising borderline objects (instances near decision boundaries) from underrepresented groups for duplication whilst potentially removing instances from overrepresented groups. Unlike relabelling approaches, preferential sampling maintains original class labels whilst creating a more balanced dataset that prevents models from learning biased patterns due to skewed group representation.	Fairness Reliability Transparency
Attribute Removal (Fairness Through Unawareness)	Attribute Removal (Fairness Through Unawareness) ensures fairness by completely excluding protected attributes such as race, gender, or age from the model's input features. While this approach prevents direct discrimination, it may not eliminate bias if other features are correlated with protected attributes (proxy discrimination). This technique represents the most basic fairness intervention but often needs to be combined with other approaches to address indirect bias through seemingly neutral features.	Fairness Transparency