Traditional recruiting techniques are often characterized by discrimination as human recruiters make biased decisions. To increase fairness in human resource management (HRM), organizations are increasingly adopting AI-based methods. Especially recruiting processes are restructured in order to find promising talents for vacant job positions. However, use of AI in recruiting is a two-edged sword as the neutrality of AI-based decisions highly depends on the quality of the underlying data. In this research-in-progress, we develop a research model explaining AI adoption in recruiting by defining and considering fairness as a determinant. Based on 21 semi-structured interviews we identified dimensions of perceived fairness (diversity, ethics, discrimination and bias, explainable AI) thereby affecting AI adoption. The proposed model addresses research gaps in AI recruiting research in general and arising ethical questions concerning the use of AI in people management in general and recruiting process in particular. We also discuss implications for further research and next steps of this research in progress work.
Ochmann, Jessica and Laumer, Sven, "Fairness as a Determinant of AI Adoption in Recruiting: An Interview-based Study" (2019). DIGIT 2019 Proceedings. 16.