Related papers: Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables

Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables

URL: http://arxiv.org/abs/2202.08977v1
Date: Wed, 16 Feb 2022 15:34:07 GMT
Title: Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables
Authors: Samuele Centorrino and Jean-Pierre Florens and Jean-Michel Loubes
Abstract summary: A supervised machine learning algorithm determines a model from a learning sample that will be used to predict new observations. This information aggregation does not consider any potential selection on unobservables and any status-quo biases which may be contained in the training sample. The latter bias has raised concerns around the so-called textitfairness of machine learning algorithms, especially towards disadvantaged groups.
Score: 3.265773263570237
License: http://creativecommons.org/licenses/by/4.0/
Abstract: A supervised machine learning algorithm determines a model from a learning sample that will be used to predict new observations. To this end, it aggregates individual characteristics of the observations of the learning sample. But this information aggregation does not consider any potential selection on unobservables and any status-quo biases which may be contained in the training sample. The latter bias has raised concerns around the so-called \textit{fairness} of machine learning algorithms, especially towards disadvantaged groups. In this chapter, we review the issue of fairness in machine learning through the lenses of structural econometrics models in which the unknown index is the solution of a functional equation and issues of endogeneity are explicitly accounted for. We model fairness as a linear operator whose null space contains the set of strictly {\it fair} indexes. A {\it fair} solution is obtained by projecting the unconstrained index into the null space of this operator or by directly finding the closest solution of the functional equation into this null space. We also acknowledge that policymakers may incur a cost when moving away from the status quo. Achieving \textit{approximate fairness} is obtained by introducing a fairness penalty in the learning procedure and balancing more or less heavily the influence between the status quo and a full fair solution.

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