The Economic Consequences of Algorithmic Discrimination: Theory and Empirical Evidence

54 Pages Posted: 8 Sep 2020 Last revised: 9 Dec 2020

See all articles by Kevin Bauer

Kevin Bauer

Leibniz Institute for Financial Research SAFE

Nicolas Pfeuffer

Goethe University Frankfurt

Benjamin Abdel-Karim

Goethe University Frankfurt

Oliver Hinz

Goethe University Frankfurt - Faculty of Economics and Business Administration

Michael Kosfeld

Goethe University Frankfurt; IZA Institute of Labor Economics; Centre for Economic Policy Research (CEPR); CESifo (Center for Economic Studies and Ifo Institute); University of Bonn - Center for Development Research (ZEF)

Date Written: August 17, 2020

Abstract

Using a novel theoretical framework and data from a comprehensive field study we conducted over a period of three years, we outline the causal effects of algorithmic discrimination on economic efficiency and social welfare in a strategic setting under uncertainty. We combine economic, game-theoretic, and applied machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes showcasing economic downstream consequences of algorithmic discrimination. Using our framework and unique data, we provide both theoretical and empirical evidence on the consequences of algorithmic discrimination. Our unique empirical setting allows us to precisely quantify efficiency and welfare ramifications relative to an ideal world where there are no information asymmetries. Our results emphasize that Artificial Intelligence systems' capabilities in overcoming information asymmetries and thereby enhancing welfare negatively depend on the degree of inherent algorithmic discrimination against specific groups in the population. This relation is particularly concerning in selective-labels environments where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled. The reason is that commonly used technical performance metrics like the precision measure can be highly deceptive and lead to wrong conclusions. Finally, our results depict that continued learning, by creating feedback loops, can help remedy algorithmic discrimination and associated negative effects over time.

Keywords: algorithmic discrimination, social welfare, economics, game theory, feedback loops, artificial intelligence, machine learning

JEL Classification: M20

Suggested Citation

Bauer, Kevin and Pfeuffer, Nicolas and Abdel-Karim, Benjamin and Hinz, Oliver and Kosfeld, Michael, The Economic Consequences of Algorithmic Discrimination: Theory and Empirical Evidence (August 17, 2020). SAFE Working Paper No. 287, Available at SSRN: https://ssrn.com/abstract=3675313 or http://dx.doi.org/10.2139/ssrn.3675313

Kevin Bauer

Leibniz Institute for Financial Research SAFE ( email )

(http://www.safe-frankfurt.de)
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany

Nicolas Pfeuffer

Goethe University Frankfurt ( email )

Gr├╝neburgplatz 1
Frankfurt am Main, 60323
Germany

Benjamin Abdel-Karim

Goethe University Frankfurt ( email )

Gr├╝neburgplatz 1
Frankfurt am Main, 60323
Germany

Oliver Hinz (Contact Author)

Goethe University Frankfurt - Faculty of Economics and Business Administration ( email )

Mertonstrasse 17-25
Frankfurt am Main, D-60325
Germany

Michael Kosfeld

Goethe University Frankfurt ( email )

Faculty of Economics and Business Administration
Theodor-W.-Adorno-Platz 4
Frankfurt am Main, 60323
Germany

IZA Institute of Labor Economics

Schaumburg-Lippe-Str. 7 / 9
Bonn, D-53072
Germany

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

CESifo (Center for Economic Studies and Ifo Institute) ( email )

Poschinger Str. 5
Munich, DE-81679
Germany

University of Bonn - Center for Development Research (ZEF) ( email )

Walter-Flex-Str. 3
Bonn, NRW 53113
Germany

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