Biased Humans, (Un)Biased Algorithms?

Journal of Business Ethics

36 Pages Posted: 19 May 2022 Last revised: 1 Sep 2022

See all articles by Florian Pethig

Florian Pethig

Tilburg University - Tilburg University School of Economics and Management

Julia Kroenung

University of Mannheim; FernUniversitat in Hagen

Date Written: February 6, 2022

Abstract

Previous research has shown that algorithmic decisions can reflect gender bias. The increasingly widespread utilization of algorithms in critical decision-making domains (e.g., healthcare or hiring) can thus lead to broad and structural disadvantages for women. However, women often experience bias and discrimination through human decisions and may turn to algorithms in the hope of receiving neutral and objective evaluations. Across three studies (N = 1107), we examine whether women’s receptivity to algorithms is affected by situations in which they believe that their gender identity might disadvantage them in an evaluation process. In Study 1, we establish, in an incentive-compatible online setting, that unemployed women are more likely to choose to have their employment chances evaluated by an algorithm if the alternative is an evaluation by a man rather than a woman. Study 2 generalizes this effect by placing it in a hypothetical hiring context, and Study 3 proposes that relative algorithmic objectivity, i.e., the perceived objectivity of an algorithmic evaluator over and against a human evaluator, is a driver of women’s preferences for evaluations by algorithms as opposed to men. Our work sheds light on how women make sense of algorithms in stereotype-relevant domains and exemplifies the need to provide education for those at risk of being adversely affected by algorithmic decisions. Our results have implications for the ethical management of algorithms in evaluation settings. We advocate for improving algorithmic literacy so that evaluators and evaluatees (e.g., hiring managers and job applicants) can acquire the abilities required to reflect critically on algorithmic decisions.

Keywords: algorithms, gender bias, stigma, objectivity

Suggested Citation

Pethig, Florian and Kroenung, Julia, Biased Humans, (Un)Biased Algorithms? (February 6, 2022). Journal of Business Ethics, Available at SSRN: https://ssrn.com/abstract=4110128

Florian Pethig (Contact Author)

Tilburg University - Tilburg University School of Economics and Management ( email )

P.O. Box 90153
Tilburg, 5000 LE
Netherlands

Julia Kroenung

University of Mannheim ( email )

Universitaetsbibliothek Mannheim
Zeitschriftenabteilung
Mannheim, 68131
Germany

FernUniversitat in Hagen ( email )

Universit¨atsstraße 41
Hagen, 58097
Germany

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