Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research

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dc.contributor.author Rieskamp, Jonas
dc.contributor.author Hofeditz, Lennart
dc.contributor.author Mirbabaie, Milad
dc.contributor.author Stieglitz, Stefan
dc.date.accessioned 2022-12-27T18:49:22Z
dc.date.available 2022-12-27T18:49:22Z
dc.date.issued 2023-01-03
dc.description.abstract Algorithmic fairness in Information Systems (IS) is a concept that aims to mitigate systematic discrimination and bias in automated decision making. However, previous research argued that different fairness criteria are often incompatible. In hiring, AI is used to assess and rank applicants according to their fit for vacant positions. However, various types of bias also exist for AI-based algorithms (e.g., using biased historical data). To reduce AI’s bias and thereby unfair treatment, we conducted a systematic literature review to identify suitable strategies for the context of hiring. We identified nine fundamental articles in this context and extracted four types of approaches to address unfairness in AI, namely pre-process, in-process, post-process, and feature selection. Based on our findings, we (a) derived a research agenda for future studies and (b) proposed strategies for practitioners who design and develop AIs for hiring purposes.
dc.format.extent 10
dc.identifier.doi 10.24251/HICSS.2023.026
dc.identifier.isbn 978-0-9981331-6-4
dc.identifier.uri https://hdl.handle.net/10125/102654
dc.language.iso eng
dc.relation.ispartof Proceedings of the 56th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject AI and the Future of Work
dc.subject ai-based algorithms
dc.subject ai implementation
dc.subject fairness in ai
dc.subject hiring
dc.subject slr
dc.title Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research
dc.type.dcmi text
prism.startingpage 216
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