{"keyword":["Management of Technology and Innovation","Marketing","Computer Science Applications","Economics and Econometrics","Business and International Management"],"year":"2022","status":"public","author":[{"first_name":"Lennart","last_name":"Hofeditz","full_name":"Hofeditz, Lennart"},{"first_name":"Sünje","full_name":"Clausen, Sünje","last_name":"Clausen"},{"full_name":"Rieß, Alexander","last_name":"Rieß","first_name":"Alexander"},{"id":"88691","full_name":"Mirbabaie, Milad","last_name":"Mirbabaie","first_name":"Milad"},{"first_name":"Stefan","last_name":"Stieglitz","full_name":"Stieglitz, Stefan"}],"language":[{"iso":"eng"}],"publisher":"Springer Science and Business Media LLC","date_created":"2023-01-17T15:17:03Z","publication_status":"published","type":"journal_article","user_id":"80546","doi":"10.1007/s12525-022-00600-9","date_updated":"2023-01-18T07:56:16Z","_id":"37138","abstract":[{"text":"AbstractAssuming 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.","lang":"eng"}],"publication_identifier":{"issn":["1019-6781","1422-8890"]},"citation":{"chicago":"Hofeditz, Lennart, Sünje Clausen, Alexander Rieß, Milad Mirbabaie, and Stefan Stieglitz. “Applying XAI to an AI-Based System for Candidate Management to Mitigate Bias and Discrimination in Hiring.” Electronic Markets (ELMA), 2022. https://doi.org/10.1007/s12525-022-00600-9.","mla":"Hofeditz, Lennart, et al. “Applying XAI to an AI-Based System for Candidate Management to Mitigate Bias and Discrimination in Hiring.” Electronic Markets (ELMA), Springer Science and Business Media LLC, 2022, doi:10.1007/s12525-022-00600-9.","apa":"Hofeditz, L., Clausen, S., Rieß, A., Mirbabaie, M., & Stieglitz, S. (2022). Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring. Electronic Markets (ELMA). https://doi.org/10.1007/s12525-022-00600-9","short":"L. Hofeditz, S. Clausen, A. Rieß, M. Mirbabaie, S. Stieglitz, Electronic Markets (ELMA) (2022).","ieee":"L. Hofeditz, S. Clausen, A. Rieß, M. Mirbabaie, and S. Stieglitz, “Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring,” Electronic Markets (ELMA), 2022, doi: 10.1007/s12525-022-00600-9.","ama":"Hofeditz L, Clausen S, Rieß A, Mirbabaie M, Stieglitz S. Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring. Electronic Markets (ELMA). Published online 2022. doi:10.1007/s12525-022-00600-9","bibtex":"@article{Hofeditz_Clausen_Rieß_Mirbabaie_Stieglitz_2022, title={Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring}, DOI={10.1007/s12525-022-00600-9}, journal={Electronic Markets (ELMA)}, publisher={Springer Science and Business Media LLC}, author={Hofeditz, Lennart and Clausen, Sünje and Rieß, Alexander and Mirbabaie, Milad and Stieglitz, Stefan}, year={2022} }"},"title":"Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring","publication":"Electronic Markets (ELMA)"}