Mitigating cognitive biases in developing AI-assisted recruitment systems: A knowledge-sharing approach
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Date
2022
Open Access Location
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
IGI Global
Rights
(c) The author/s
CC BY
CC BY
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.
Description
Keywords
AI Developers, Algorithms, Artificial Intelligence, Cognitive Biases, Datasets, HR Managers, Knowledge Sharing, Recruitment
Citation
Soleimani M, Intezari A, Pauleen DJ. (2022). Mitigating cognitive biases in developing ai-assisted recruitment systems: A knowledge-sharing approach. International Journal of Knowledge Management. 18. 1. (pp. 1-18).