Fairness in Machine Learning and Econometrics

Samuele Centorrino, Jean-Pierre Florens and Jean-Michel Loubès
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Jean-Pierre Florens: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Jean-Michel Loubès: IMT - Institut de Mathématiques de Toulouse UMR5219 - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INSA Toulouse - Institut National des Sciences Appliquées - Toulouse - INSA - Institut National des Sciences Appliquées - UT - Université de Toulouse - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique
Authors registered in the RePEc Author Service: Laszlo Matyas ()

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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 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 taken into account. We model fairness as a linear operator whose null space contains the set of strictly fair indexes. A 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 costs when moving away from the status quo. Approximate fairness is thus introduced as an intermediate set-up between the status quo and a fully fair solution via a fairness-specific penalty in the objective function of the learning model.

Date: 2022
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Published in Econometrics with Machine Learning, Springer, pp.217-250, 2022, 978-3-031-15151-4

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04040498

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