“解决”招聘中的歧视问题是什么意思?:英国对自动化招聘系统的社会、技术和法律观点

J. Sánchez-Monedero, L. Dencik, L. Edwards
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引用次数: 108

摘要

招聘和雇用中的歧视性做法是一个持续存在的问题,它不仅关系到工作场所关系,而且关系到对经济公正和不平等的更广泛理解。获得并保持一份工作的能力是参与社会和维持生计的一个关键方面。然而,随着数据驱动工具驱动的自动化招聘系统(AHSs)的出现和发展,决定谁有资格获得工作以及为什么要这样做的方式正在迅速改变。在全球范围内,这种采用程度的证据很少,但最近的一份报告估计,98%的财富500强公司在招聘过程中使用了某种形式的申请人跟踪系统,这是一种被认为是效率措施和成本节约的趋势。对这类AHSs的主要关切包括缺乏透明度和可能限制特定概况的就业机会。然而,就后者而言,其中一些AHSs声称发现和减轻对受保护群体的歧视做法,并促进工作中的多样性和包容性。然而,尽管这些工具在世界各地拥有越来越多的用户基础,但这种“减轻偏见”的说法很少受到审查和评估,即使这样做,也几乎完全是从美国社会法律的角度进行的。在本文中,我们通过批判性地研究在英国经常使用的三个著名的自动招聘系统(AHSs), HireVue, Pymetrics和Applied,如何理解并试图减轻偏见和歧视,介绍了美国以外的视角。之所以选择这些系统,是因为它们明确声称要解决招聘中的歧视问题,而且与许多竞争对手不同,它们提供了一些有关其系统如何工作的信息,这些信息可以为分析提供信息。使用公开可用的文件,我们描述了他们的工具是如何设计、验证和审计偏见的,突出了假设和局限性,然后将这些置于英国的社会法律背景下。英国的法律背景与美国截然不同,不仅在招聘和平等法方面,在数据保护(DP)法方面也是如此。我们认为,这对于解决对透明度的担忧可能很重要,并且可能意味着在最终能够满足欧盟法律标准的AHSs中建立偏见缓解的挑战。这一点很重要,因为这些AHSs,尤其是在美国开发的AHSs,可能会掩盖而不是改善工作场所的系统性歧视。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What does it mean to 'solve' the problem of discrimination in hiring?: social, technical and legal perspectives from the UK on automated hiring systems
Discriminatory practices in recruitment and hiring are an ongoing issue that is a concern not just for workplace relations, but also for wider understandings of economic justice and inequality. 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. Evidence of the extent of this uptake around the globe is scarce, but a recent report estimated that 98% of Fortune 500 companies use Applicant Tracking Systems of some kind in their hiring process, a trend driven by perceived efficiency measures and cost-savings. 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. These systems have been chosen as they explicitly claim to address issues of discrimination in hiring and, unlike many of their competitors, provide some information about how their systems work that can inform an analysis. 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.
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