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Based on a qualitative survey among 203 US workers active on the microwork platform Amazon Mechanical Turk, we analyze potential biases embedded in the institutional setting provided by ondemand crowdworking platforms and their effect on perceived workplace fairness. We explore the triadic relationship between employers, workers, and platform providers, focusing on the power of platform providers to design settings and processes that affect workers' fairness perceptions. Our focus is on workers' awareness of the new institutional setting, frames applied to the mediating platform, and a differentiated analysis of distinct fairness dimensions.

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Algorithmic management is used to govern digital work platforms such as Upwork or Fiverr. However, algorithmic decision-making is often non-transparent and rapidly evolving, forcing workers to constantly adapt their behavior. Extant research focuses on how workers experience algorithmic management, while often disregarding the agency that workers exert in dealing with algorithmic management. Following a sociomateriality perspective, we investigate the practices that workers develop to comply with (assumed) mechanisms of algorithmic management on digital work platforms. Based on a systematic content analysis of 12,294 scraped comments from an online community of digital freelancers, we show how workers adopt direct and indirect “anticipatory compliance practices”, such as undervaluing their own work, staying under the radar, curtailing their outreach to clients and keeping emotions in check, in order to ensure their continued participation on the platform, which takes on the role of a shadow employer. Our study contributes to research on algorithmic management by (1) showing how workers adopt practices aimed at “pacifying” the platform algorithm; (2) outlining how workers engage in extra work; (3) showing how workers co-construct the power of algorithms through their anticipatory compliance practices.

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