Paper Number
1279
Paper Type
Complete
Abstract
Recruiting is one of the most controversial and sensitive use cases of AI due to the potential impact on the life of job seekers. Thus, on the one hand, regulators introduce new regulations on AI classifying recruiting as high-risk use case. On the other hand, technology research shows AI could reduce discrimination. One perspective is still unknown: How do discriminated people perceive the application of AI in recruiting? The paper investigates how discriminated people perceive the use of AI in recruiting and how transparency of AI decisions influences this perception. We conducted a lab-in-the-field experiment using an online questionnaire yielding a random sample of (N = 1335) unemployed registered with the Austrian Public Unemployment Service. We show that perceived everyday discrimination increases the positive overall evaluation of AI decisions in recruiting. Evaluating regulatory requirements and technical features should also explicitly take discriminated groups into account.
Recommended Citation
Fleiß, Jürgen; Kubicek, Bettina; and Thalmann, Stefan, "Better the Devil You Don’t Know. Perceived Discrimination Increases Positive Evaluation of AI Recruiting Decisions." (2024). ICIS 2024 Proceedings. 17.
https://aisel.aisnet.org/icis2024/soc_impactIS/soc_impactIS/17
Better the Devil You Don’t Know. Perceived Discrimination Increases Positive Evaluation of AI Recruiting Decisions.
Recruiting is one of the most controversial and sensitive use cases of AI due to the potential impact on the life of job seekers. Thus, on the one hand, regulators introduce new regulations on AI classifying recruiting as high-risk use case. On the other hand, technology research shows AI could reduce discrimination. One perspective is still unknown: How do discriminated people perceive the application of AI in recruiting? The paper investigates how discriminated people perceive the use of AI in recruiting and how transparency of AI decisions influences this perception. We conducted a lab-in-the-field experiment using an online questionnaire yielding a random sample of (N = 1335) unemployed registered with the Austrian Public Unemployment Service. We show that perceived everyday discrimination increases the positive overall evaluation of AI decisions in recruiting. Evaluating regulatory requirements and technical features should also explicitly take discriminated groups into account.
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05-SocImpact