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

J. Rieskamp, L. Hofeditz, M. Mirbabaie, S. Stieglitz, in: Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023.

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Conference Paper | English
Author
Rieskamp, JonasLibreCat; Hofeditz, Lennart; Mirbabaie, MiladLibreCat; Stieglitz, Stefan
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.
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Proceedings Title
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS)
Conference
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS)
Conference Date
2023-01-03 – 2023-01-06
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Rieskamp J, Hofeditz L, Mirbabaie M, Stieglitz S. Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research. In: Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS). ; 2023.
Rieskamp, J., Hofeditz, L., Mirbabaie, M., & Stieglitz, S. (2023). Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research. Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS). Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS).
@inproceedings{Rieskamp_Hofeditz_Mirbabaie_Stieglitz_2023, title={Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research}, booktitle={Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS)}, author={Rieskamp, Jonas and Hofeditz, Lennart and Mirbabaie, Milad and Stieglitz, Stefan}, year={2023} }
Rieskamp, Jonas, Lennart Hofeditz, Milad Mirbabaie, and Stefan Stieglitz. “Approaches to Improve Fairness When Deploying AI-Based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research.” In Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023.
J. Rieskamp, L. Hofeditz, M. Mirbabaie, and S. Stieglitz, “Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research,” presented at the Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023.
Rieskamp, Jonas, et al. “Approaches to Improve Fairness When Deploying AI-Based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research.” Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), 2023.

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