Designing for justice in freelancing: testing platform interventions to minimise discrimination in online labour markets

Sian Brooke and Aliya Rao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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.

Keywords: future of work; gender; design justice; ethnicity; platforms; affordances; STICERD; SRG21\210549; REF fund (search for similar items in EconPapers)
JEL-codes: J01 R14 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2024-03-30
New Economics Papers: this item is included in nep-dcm and nep-exp
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Published in Big Data and Society, 30, March, 2024, 11(1). ISSN: 2053-9517

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:122152

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