Online labour markets (OLMs) are a vital source of income for globally diverse and dispersed freelancers. Despite their promise of neutrality, OLMs are known to perpetuate hiring discrimination, vested in how OLMs are designed and what kinds of interactions they enable between freelancers and hirers. In this study, we go beyond understanding mechanisms of hiring discrimination in OLMs, to identifying platform design features that can minimise hiring discrimination. To do so, we draw on a methodology guided by the design justice ethos. Drawing on a survey on UK-based freelancers and interviews with a purposefully drawn sub-sample, we collaboratively identify five platform design interventions to minimise hiring discrimination in OLMs: community composition, identity-signalling flairs, text only reviews, union membership, and an antidiscrimination prompt. The core of our study is an innovative experiment conducted on a purpose-built, mock OLM, Mock-Freelancer.com. On this mock OLM, we experimentally test mechanisms of discrimination, including how these mechanisms fare under the five altered platform design interventions through a discrete-choice experiment. We find that both community and flairs were important in encouraging the hiring of women and non-White freelancers. We also establish that anonymity universally disadvantages freelancers. We conclude with recommendations to design OLMs that minimise labour market discrimination.

中文翻译：

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为自由职业者的正义而设计：测试平台干预措施，以尽量减少在线劳动力市场中的歧视

在线劳动力市场 (OLM) 是全球多元化和分散的自由职业者的重要收入来源。尽管 OLM 承诺保持中立，但众所周知，它会延续招聘歧视，这取决于 OLM 的设计方式以及它们在自由职业者和雇佣者之间实现的互动类型。在这项研究中，我们不仅了解 OLM 中的招聘歧视机制，还确定了可以最大限度减少招聘歧视的平台设计功能。为此，我们采用了以设计正义精神为指导的方法。根据对英国自由职业者的调查以及对有目的地抽取的子样本的访谈，我们合作确定了五种平台设计干预措施，以最大限度地减少 OLM 中的招聘歧视：社区组成、身份信号天赋、纯文本评论、工会会员资格和反歧视提示。我们研究的核心是在专门构建的模拟 OLM（Mock-Freelancer.com）上进行的创新实验。在这个模拟 OLM 上，我们通过离散选择实验对歧视机制进行了实验测试，包括这些机制在五种改变的平台设计干预下的表现如何。我们发现社区和天赋对于鼓励雇用女性和非白人自由职业者都很重要。我们还发现，匿名普遍对自由职业者不利。最后，我们提出了设计 OLM 的建议，以最大限度地减少劳动力市场歧视。