Canadian Journal of Law and Technology


Hiring algorithms, issue idenfitication, hiring discrimination, systemic discrimination


Private-sector employers are increasingly using hiring algorithms as a tool for screening job applicants, comparing qualifications, and ultimately determining which candidates should be selected. Within this context, hiring algorithms make no small promise: a hiring process that is not only more efficient and effective, but also more supportive of workplace equality. This promise rests largely on the notion that traditional human-driven models of hiring are beset by subjective biases and prejudices, whereas hiring algorithms, which are driven by hard data and objective evidence, can eliminate certain human biases and prejudices, thereby promoting workplace equality. But can hiring algorithms deliver on this promise? This article, which focuses on issue identification, argues that while hiring algorithms may, when used carefully, assist in mitigating certain hiring discrimination risks, their capacity to do so is not without limits, and they may in fact introduce certain concerns over systemic discrimination.