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Challenging Biased Hiring Algorithms

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  • Aislinn Kelly-Lyth

Abstract

Many employers now use complex algorithms to assess job applications. These algorithms can have discriminatory effects for women, ethnic minorities, people with disabilities and other legally protected groups. This article evaluates the application of UK law to the use of such algorithms, and finds that equality and data protection laws seek to balance the harms of these tools against the benefits of their use. The article then considers the application of these laws in practice, and finds that the opacity of automated hiring poses a significant challenge to enforcement. The article suggests that a ‘transparent recruitment scheme’ should therefore be introduced, which would incentivise the publication of equality metrics contained in employers’ data protection impact assessments. This scheme should be a collaborative effort between the Information Commissioner’s Office and the Equality and Human Rights Commission.

Suggested Citation

  • Aislinn Kelly-Lyth, 2021. "Challenging Biased Hiring Algorithms," Oxford Journal of Legal Studies, Oxford University Press, vol. 41(4), pages 899-928.
    • Handle: RePEc:oup:oxjlsj:v:41:y:2021:i:4:p:899-928.
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    File URL: http://hdl.handle.net/10.1093/ojls/gqab006
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    Cited by:

    1. Antonio ALOISI & Valerio DE STEFANO, 2022. "Essential jobs, remote work and digital surveillance: Addressing the COVID‐19 pandemic panopticon," International Labour Review, International Labour Organization, vol. 161(2), pages 289-314, June.
    2. Hansen, Sakina & Loftus, Joshua, 2023. "Model-agnostic auditing: a lost cause?," LSE Research Online Documents on Economics 120114, London School of Economics and Political Science, LSE Library.

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