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abstract: 'Recently there has been sustained interest in modifying prediction algorithms to satisfy fairness constraints. These constraints are typically complex nonlinear functionals of the observed data distribution. Focusing on the causal constraints proposed by [@NabiShpitser18Fair], we introduce new theoretical results and optimization techniques to make model training easier and more accurate. Specifically, we show how to reparameterize the observed data likelihood such that fairness constraints correspond directly to parameters that appear in the likelihood, transforming a complex constrained optimization objective into a simple optimization problem with box constraints. We also exploit methods from empirical likelihood theory in statistics to improve predictive performance, without requiring parametric models for high-dimensional feature vectors.'
bibliography:
- 'references.bib'
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INTRODUCTION {#sec:intro}
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Predictive models trained on imperfect data are increasingly being used in socially-impactful settings. Predictions (such as risk scores) have been used to inform high-stakes decisions in criminal justice [@Perry2013policing], healthcare [@Kappen2018Clinical], and finance [@Khandani2010Finance]. While automation may bring many potential benefits – such as speed and accuracy – it is also fraught with risks. Predictive models introduce two dangers in particular: the illusion of objectivity and violation of fairness norms. Predictive models may appear to be “neutral,” since humans are less involved 