Convex Formulations for Fair Principal Component Analysis

Matt Olfat · Anil J. Aswani

37 citations0/0 files storedAAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))

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Ethics and Social Impacts of AI Statistical Methods and Applications Privacy-Preserving Technologies in Data

Abstract

Though there is a growing literature on fairness for supervised learning, incorporating fairness into unsupervised learning has been less well-studied. This paper studies fairness in the context of principal component analysis (PCA). We first define fairness for dimensionality reduction, and our definition can be interpreted as saying a reduction is fair if information about a protected class (e.g., race or gender) cannot be inferred from the dimensionality-reduced data points. Next, we develop convex optimization formulations that can improve the fairness (with respect to our definition) of PCA and kernel PCA. These formulations are semidefinite programs, and we demonstrate their effectiveness using several datasets. We conclude by showing how our approach can be used to perform a fair (with respect to age) clustering of health data that may be used to set health insurance rates.

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References

2012Fairness through awareness 2016Equality of Opportunity in Supervised Learning 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2015Certifying and Removing Disparate Impact 2018Mitigating Unwanted Biases with Adversarial Learning 2013Learning Fair Representations 2018Fairness in Criminal Justice Risk Assessments: The State of the Art 2008The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

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2022Efficient fair principal component analysis 2021FairOD: Fairness-aware Outlier Detection 2019A Distributed Fair Machine Learning Framework with Private Demographic Data Protection 2019Fairness in Clustering with Multiple Sensitive Attributes 2022Fairness-Aware Range Queries for Selecting Unbiased Data 2023A Novel Approach for Fair Principal Component Analysis Based on Eigendecomposition

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