Algorithmic Hiring Systems: Implications and Recommendations for Organisations and Policymakers

Algorithms are becoming increasingly prevalent in the hiring process, as they are used to source, screen, interview, and select job applicants. This chapter examines the perspective of both organisations and policymakers about algorithmic hiring systems, drawing examples from Japan and the United States. The focus is on discussing the drivers underlying the rising demand for algorithmic hiring systems and four risks associated with their implementation: the privacy of job candidate data; the privacy of current and former employees’ workplace data; the potential for algorithmic hiring bias; and concerns surrounding ongoing oversight of algorithmically-assisted decision-making throughout the hiring process. These risks serve as the foundation for developing a risk management framework based on management control principles to facilitate dialogue within organisations to address the governance and management of such risks. The framework also identifies areas policymakers can focus on to help balance (1) granting organisations unfettered access to the personal and potentially sensitive data of job applicants and employees to develop hiring algorithms and (2) implementing strict data protection laws that safeguard individuals’ rights yet may impede innovation, and emphasises the need to establish an intra-governmental AI oversight and coordination function that tracks, analyses, and reports on adverse algorithmic incidents. The chapter concludes by highlighting seven recommendations to mitigate the risks organisations and policymakers face in the development, use, and oversight of algorithmic hiring.