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Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring

Assuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for (e.g., by providing high quality training data), employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection pr...

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Autores principales: Hofeditz, Lennart, Clausen, Sünje, Rieß, Alexander, Mirbabaie, Milad, Stieglitz, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764302/
https://www.ncbi.nlm.nih.gov/pubmed/36568961
http://dx.doi.org/10.1007/s12525-022-00600-9
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author Hofeditz, Lennart
Clausen, Sünje
Rieß, Alexander
Mirbabaie, Milad
Stieglitz, Stefan
author_facet Hofeditz, Lennart
Clausen, Sünje
Rieß, Alexander
Mirbabaie, Milad
Stieglitz, Stefan
author_sort Hofeditz, Lennart
collection PubMed
description Assuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for (e.g., by providing high quality training data), employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection processes more objective. However, as the final hiring decision is likely to remain with humans, prevalent human biases could still cause discrimination. This work investigates the impact of an AI-based system’s candidate recommendations on humans’ hiring decisions and how this relation could be moderated by an Explainable AI (XAI) approach. We used a self-developed platform and conducted an online experiment with 194 participants. Our quantitative and qualitative findings suggest that the recommendations of an AI-based system can reduce discrimination against older and female candidates but appear to cause fewer selections of foreign-race candidates. Contrary to our expectations, the same XAI approach moderated these effects differently depending on the context. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12525-022-00600-9.
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spelling pubmed-97643022022-12-20 Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring Hofeditz, Lennart Clausen, Sünje Rieß, Alexander Mirbabaie, Milad Stieglitz, Stefan Electron Mark Research Paper Assuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for (e.g., by providing high quality training data), employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection processes more objective. However, as the final hiring decision is likely to remain with humans, prevalent human biases could still cause discrimination. This work investigates the impact of an AI-based system’s candidate recommendations on humans’ hiring decisions and how this relation could be moderated by an Explainable AI (XAI) approach. We used a self-developed platform and conducted an online experiment with 194 participants. Our quantitative and qualitative findings suggest that the recommendations of an AI-based system can reduce discrimination against older and female candidates but appear to cause fewer selections of foreign-race candidates. Contrary to our expectations, the same XAI approach moderated these effects differently depending on the context. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12525-022-00600-9. Springer Berlin Heidelberg 2022-12-20 2022 /pmc/articles/PMC9764302/ /pubmed/36568961 http://dx.doi.org/10.1007/s12525-022-00600-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Paper
Hofeditz, Lennart
Clausen, Sünje
Rieß, Alexander
Mirbabaie, Milad
Stieglitz, Stefan
Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring
title Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring
title_full Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring
title_fullStr Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring
title_full_unstemmed Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring
title_short Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring
title_sort applying xai to an ai-based system for candidate management to mitigate bias and discrimination in hiring
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9764302/
https://www.ncbi.nlm.nih.gov/pubmed/36568961
http://dx.doi.org/10.1007/s12525-022-00600-9
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