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Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems: A Knowledge-Sharing Approach

Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems: A Knowledge-Sharing Approach

Melika Soleimani, Ali Intezari, David J. Pauleen
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 18
ISSN: 1548-0666|EISSN: 1548-0658|EISBN13: 9781799893608|DOI: 10.4018/IJKM.290022
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MLA

Soleimani, Melika, et al. "Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems: A Knowledge-Sharing Approach." IJKM vol.18, no.1 2022: pp.1-18. http://doi.org/10.4018/IJKM.290022

APA

Soleimani, M., Intezari, A., & Pauleen, D. J. (2022). Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems: A Knowledge-Sharing Approach. International Journal of Knowledge Management (IJKM), 18(1), 1-18. http://doi.org/10.4018/IJKM.290022

Chicago

Soleimani, Melika, Ali Intezari, and David J. Pauleen. "Mitigating Cognitive Biases in Developing AI-Assisted Recruitment Systems: A Knowledge-Sharing Approach," International Journal of Knowledge Management (IJKM) 18, no.1: 1-18. http://doi.org/10.4018/IJKM.290022

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Abstract

Artificial Intelligence (AI) is increasingly embedded in business processes, including the Human Resource (HR) recruitment process. While AI can expedite the recruitment process, evidence from the industry, however, shows that AI-recruitment systems (AIRS) may fail to achieve unbiased decisions about applicants. There are risks of encoding biases in the datasets and algorithms of AI which lead AIRS to replicate and amplify human biases. To develop less biased AIRS, collaboration between HR managers and AI developers for training algorithms and exploring algorithmic biases is vital. Using an exploratory research design, 35 HR managers and AI developers globally were interviewed to understand the role of knowledge sharing during their collaboration in mitigating biases in AIRS. The findings show that knowledge sharing can help to mitigate biases in AIRS by informing data labeling, understanding job functions, and improving the machine learning model. Theoretical contributions and practical implications are suggested.