The ability to get and keep a job is a key aspect of participating in society sustaining livelihoods. Yet the way decisions are made on who is eligible jobs, and why, are rapidly changing with the advent and growth in uptake of hiring systems (AHSs) powered by data-driven tools. Key concerns such AHSs include the lack of transparency and potential limitation of to jobs for specific profiles. In relation to the latter, however, of these AHSs claim to detect and mitigate discriminatory practices protected groups and promote diversity and inclusion at work. Yet these tools have a growing user-base around the world, such claims of mitigation are rarely scrutinised and evaluated, and when done so, have exclusively been from a US socio-legal perspective. In this paper, we a perspective outside the US by critically examining how three automated hiring systems (AHSs) in regular use in the UK, HireVue, and Applied, understand and attempt to mitigate bias and. Using publicly available documents, we describe how their tools designed, validated and audited for bias, highlighting assumptions and, before situating these in the socio-legal context of the UK. The has a very different legal background to the US in terms not only of hiring equality law, but also in terms of data protection (DP) law. We argue that might be important for addressing concerns about transparency and could a challenge to building bias mitigation into AHSs definitively capable of EU legal standards. This is significant as these AHSs, especially those in the US, may obscure rather than improve systemic discrimination in workplace.
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Sánchez‐Monedero et al. (2019) studied this question.