Convex Formulations for Fair Principal Component Analysis

By Matt Olfat, Anil Jayanti Aswani. 2018

55 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Sociology and Political Science, Statistics and Probability

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Citations in the corpus

The Price of Fair PCA: One Extra Dimension (2018, background): “The second piece is the work by Olfat and Aswani [2018], who also look to design PCA-like maps which reduce the projected data’s dependence on a sensitive attribute.”
Fairness in representation: quantifying stereotyping as a representational harm (2019, background): “In the context of unsupervised learning, recent work on fair clustering[12, 31, 3] and PCA[30, 28] seeks to generate a modiﬁed representation of the input points so that the new representation (clustering or reduced dimension) satisﬁes a notion of “balance” with respect to groups.”