The Terminator of Social Welfare? The Economic Consequences of Algorithmic Discrimination

64 Pages Posted: 8 Sep 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 experimental data from a comprehensive field study, we explore the causal effects of algorithmic discrimination on economic efficiency and social welfare. We harness economic, game-theoretic, and state-of-the-art machine learning concepts allowing us to overcome the central challenge of missing counterfactuals, which generally impedes assessing economic downstream consequences of algorithmic discrimination. This way, we are able to precisely quantify downstream efficiency and welfare ramifications, which provides us a unique opportunity to assess whether the introduction of an AI system is actually desirable. Our results highlight that AI systems’ capabilities in enhancing welfare critically depends on the degree of inherent algorithmic biases. While an unbiased system in our setting outperforms humans and creates substantial welfare gains, the positive impact steadily decreases and ultimately reverses the more biased an AI system becomes. We show that this relation is particularly concerning in selective-labels environments, i.e., settings where outcomes are only observed if decision-makers take a particular action so that the data is selectively labeled, because commonly used technical performance metrics like the precision measure are prone to be deceptive. Finally, our results depict that continued learning, by creating feedback loops, can remedy algorithmic discrimination and associated negative effects over time.

Keywords: Algorithmic Discrimination, Artificial Intelligence, Game Theory, Economics, Batch Learning

JEL Classification: M20

Suggested Citation

Bauer, Kevin and Pfeuffer, Nicolas and Abdel-Karim, Benjamin and Hinz, Oliver and Kosfeld, Michael, The Terminator of Social Welfare? The Economic Consequences of Algorithmic Discrimination (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 )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Benjamin Abdel-Karim

Goethe University Frankfurt ( email )

Grüneburgplatz 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

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
46
Abstract Views
276
PlumX Metrics