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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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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. |
format | Online Article Text |
id | pubmed-9764302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
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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