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

Javier Sánchez‐Monedero, Lina Dencik+1 more|arXiv (Cornell University)|Sep 28, 2019

Digital Economy and Work Transformation35 citations

TL;DR

The paper critically examines three UK-used automated hiring systems (HireVue, Pymetrics, Applied) to analyze how they design, validate, and audit bias mitigation within the UK legal context.

ABSTRACT

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

Motivation & Objective

• Assess how AHSs claim to detect and mitigate discrimination against protected groups.
• Describe how HireVue, Pymetrics, and Applied are designed, validated, and audited for bias.
• Situate bias-mitigation practices within the UK social, legal, and data protection frameworks.

Proposed method

• Review publicly available documents from three automated hiring systems (HireVue, Pymetrics, Applied).
• Describe how each tool is designed, validated, and audited for bias.
• Analyze assumptions and limitations of bias-mitigation approaches.
• Contextualize findings within UK equality, hiring, and data protection law.

Experimental results

Research questions

• RQ1 How do three prominent UK-regularly used AHSs understand and attempt to mitigate bias and discrimination?
• RQ2 What assumptions and limitations underlie the bias-mitigation methods of these AHSs?
• RQ3 How does the UK socio-legal context affect transparency and the ability to meet EU data protection standards in AHS bias mitigation?
• RQ4 Do US-developed AHSs potentially obscure systemic discrimination when deployed in the UK?
• RQ5 What are the implications for transparency and fairness in automated hiring under UK law?

Key findings

• AHSs claim to detect and mitigate bias, but public documentation reveals underlying assumptions and limitations.
• The design, validation, and auditing practices of HireVue, Pymetrics, and Applied vary in transparency and rigor.
• UK data protection and equality laws present a distinct regulatory landscape that may challenge certain bias-mitigation claims.
• UK context could undermine the ability of AHSs to definitively satisfy EU legal standards for transparency and fairness.
• US-developed AHSs, when deployed in the UK, may obscure rather than improve systemic workplace discrimination.