Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/307017 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Business Ethics [ISSN:] 1573-0697 [Volume:] 183 [Issue:] 3 [Publisher:] Springer Netherlands [Place:] Dordrecht [Year:] 2022 [Pages:] 637-652
Publisher: 
Springer Netherlands, Dordrecht
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.
Subjects: 
Algorithms
Gender bias
Stigma
Objectivity
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.