The Dark Side of Recruitment in Crowdsourcing: Ethics and Transparency in Micro-Task Marketplaces
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Micro-task crowdsourcing marketplaces like Figure Eight (F8) connect a large pool of workers to employers through a single online platform, by aggregating multiple crowdsourcing platforms (channels) under a unique system. This paper investigates the F8 channels’ demographic distribution and reward schemes by analysing more than 53k crowdsourcing tasks over four years, collecting survey data and scraping marketplace metadata. We reveal an heterogeneous per-channel demographic distribution, and an opaque channel commission scheme, that varies over time and is not communicated to the employer when launching a task: workers often will receive a smaller payment than expected by the employer. In addition, the impact of channel commission schemes on the relationship between requesters and crowdworkers is explored. These observations uncover important issues on ethics, reliability and transparency of crowdsourced experiment when using this kind of marketplaces, especially for academic research.
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Number of citations to item: 3
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- Siwan Noh, Kyung-Hyune Rhee (2024): Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems, In: Computers, Materials & Continua 3(79), doi:10.32604/cmc.2024.050949
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