Fair Inference for Discrete Latent Variable Models
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2022-09-15
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Abstract
It is now well understood that machine learning models, trained on data without due care, often
exhibit unfair and discriminatory behavior against certain populations. Traditional algorithmic
fairness research has mainly focused on supervised learning tasks, particularly classification. While
fairness in unsupervised learning has received some attention, the literature has primarily addressed
fair representation learning of continuous embeddings. In this paper, we conversely focus on
unsupervised learning using probabilistic graphical models with discrete latent variables. We develop
a fair stochastic variational inference technique for the discrete latent variables, which is accomplished
by including a fairness penalty on the variational distribution that aims to respect the principles of
intersectionality, a critical lens on fairness from the legal, social science, and humanities literature,
and then optimizing the variational parameters under this penalty. We first show the utility of our
method in improving equity and fairness for clustering using naïve Bayes and Gaussian mixture
models on benchmark datasets. To demonstrate the generality of our approach and its potential
for real-world impact, we then develop a special-purpose graphical model for criminal justice risk
assessments, and use our fairness approach to prevent the inferences from encoding unfair societal
biases.