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.
Highlights
According to Upadhyay and Khandelwal (2018), the recruitment and selection process is one area in which many human resource (HR) managers and hiring professionals are considering increasing the adoption of Artificial intelligence (AI)
The findings show that AI developers and HR managers can share their knowledge at three stages during the design and development of AI-recruitment systems (AIRS) − pre-development process, development process, and post-development process − to mitigate AIRS biases
The statements below show that AI developers believe that HR managers should share their knowledge and articulate what they need before developing AI for recruiting: I think HR managers should understand that AI is not a magic thing, it is something developed by someone who is prone to errors
Summary
According to Upadhyay and Khandelwal (2018), the recruitment and selection process is one area in which many HR managers and hiring professionals are considering increasing the adoption of AI. AI can be a breakthrough technology to improve the recruitment and selection process, evidence from the industry shows that there are concerns about AI being biased due to the way algorithms are developed and datasets used to train them (Manyika et al, 2018). In AI-assisted decision-making is one of the challenges of developing AI (Martin, 2018; Shrestha et al, 2019; Tambe et al, 2019) as datasets and algorithms are significantly influenced by human biases (Varshney, 2018). AI systems can be used in different areas to support the recruitment and selection process such as reviewing and extracting information from résumés and ranking them, analyzing video interviews to evaluate person-organization and person-job fit, and scanning through multiple databases for candidate sourcing (Albert, 2019). The role of knowledge sharing in mitigating biases that are embedded in AI is, understudied
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