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

What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems