Fairness-Aware ALE Plots for Auditing Bias in Subgroups

Core Concepts

FALE (Fairness-Aware Accumulated Local Effects) plots are a method for measuring and visualizing the change in fairness for different subgroups of a population, with respect to the values of a selected feature.

Abstract

The content presents FALE, a novel method for auditing the fairness of machine learning models in subgroups of the population.
Key highlights:

Fairness in AI is an important issue, and bias can often be identified in subgroups defined by multiple attributes, rather than just individual sensitive attributes.
Existing methods for auditing subgroup fairness can be computationally challenging and lack intuitive ways to present the findings to end-users.
FALE builds on the Accumulated Local Effects (ALE) explainability method to measure and visualize the impact of feature values on the fairness of the model, as defined by a chosen fairness metric.
FALE plots show how the fairness metric changes for different subgroups defined by the values of a selected feature, along with the population sizes of those subgroups.
This provides a user-friendly, first-step tool to help identify subgroups with potential fairness issues that require further investigation.
The authors demonstrate the FALE method using the Adult dataset and the statistical parity fairness definition on the sensitive attribute of sex.

Stats

The model predictions on the test set are biased against Females, by a value of 0.177, according to the statistical parity fairness definition.

Quotes

"Fairness in AI is an open problem that has attracted the attention of the research community the last years. Although there exist a plethora of methods for auditing bias in AI, there does not exist a one-size-fits-all solution to the problem [8,20]."
"A common issue with all aforementioned works is that they hardly focus on presenting their findings in a user intuitive and explainable way. In this paper, we propose a method that addresses this issue."

Key Insights Distilled From

FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups

by Giorgos Gian... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18685.pdf

Deeper Inquiries

How can the FALE method be extended to handle more than two protected groups

To extend the FALE method to handle more than two protected groups, we can modify the calculation of the FALE estimates to incorporate multiple protected groups. Instead of comparing just two groups (protected and non-protected), we can calculate the unfairness measure for each subgroup defined by the protected attribute. By iterating through each subgroup, we can compute the FALE estimates for the influence of a particular feature value on the unfairness measure across all protected groups. This approach allows for a more comprehensive analysis of bias in subgroups with multiple protected attributes.

How can the FALE method be used to not only audit for fairness, but also guide the development of fairer machine learning models

The FALE method can not only be used to audit for fairness but also guide the development of fairer machine learning models by providing actionable insights into where bias exists and how it impacts different subgroups. By visualizing the changes in fairness for various feature values, developers can identify which attributes contribute most to unfair outcomes. This information can guide feature selection, model training, and mitigation strategies to address bias in specific subgroups. By leveraging FALE plots during the model development process, developers can iteratively improve the fairness of their models and ensure equitable outcomes across diverse subgroups.

What other types of explainability methods could be adapted to the task of fairness auditing in a similar way to FALE

Other types of explainability methods that could be adapted to the task of fairness auditing in a similar way to FALE include Partial Dependence Plots (PDP) and M-plots. PDP plots showcase the marginal effect of a feature on the model's predictions, making them suitable for analyzing how different features impact fairness in subgroups. By extending PDP plots to incorporate fairness metrics, similar to FALE, developers can visualize the influence of features on fairness outcomes. M-plots, which provide a global explanation of feature importance, can also be adapted to assess the impact of features on fairness across subgroups. By integrating fairness metrics into these explainability methods, developers can gain a deeper understanding of how model decisions are influenced by different attributes and ensure fairness in their machine learning systems.