Contributing to research on digital platform labor in the Global South, this research surveyed 149 Brazilian workers in the Amazon Mechanical Turk (AMT) platform. We begin by offering a demographic overview of the Brazilian turkers and their relation with work in general. In line with previous studies of turkers in the USA and India, AMT offers poor working conditions for Brazilian turkers. Other findings we discuss include: how a large amount of the respondents affirmed they have been formally unemployed for a long period of time; the relative importance of the pay they receive to their financial subsistence; and how Brazilian turkers cannot receive their pay directly into their bank accounts due to Amazon restrictions, making them resort to creative circumventions of the system. Importantly, these “ghost workers” (Gray & Suri, 2019) find ways to support each other and self-organize through the WhatsApp group, where they also mobilize to fight for changes on the platform. As this type of work is still in formation in Brazil, and potentially will grow in the coming years, we argue this is a matter of concern.

This article discusses how Brazilian platform workers experience and respond to platform scams through three case studies. Drawing from digital ethnographic research, vlogs/interviews of workers, and literature review, we argue for a conceptualization of “platform scam” that focuses on multiple forms of platform dishonesty and uncertainty. We characterize scam as a structuring element of the algorithmic management enacted by platform labor. The first case engages with when platforms scam workers by discussing Uber drivers’ experiences with the illusive surge pricing. The second case discusses when workers (have to) scam platforms by focusing on Amazon Mechanical Turk microworkers’ experiences with faking their identities. The third case presents when platforms lead workers to scam third parties, by engaging with how Brazilian click farm platforms’ workers use bots/fake accounts to engage with social media. Our focus on “platform scams” thus highlights the particular dimensions of faking, fraud, and deception operating in platform labor. This notion of platform scam expands and complexifies the understanding of scam within platform labor studies. Departing from workers’ experiences, we engage with the asymmetries and unequal power relations present in the algorithmic management of labor.

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In this abstract we show the results of an interdisciplinary research in which we audit fake human faces generated by the website This Person Does Not Exist (TPDNE), and discuss how this system can help perpetuate normativities supported by a dependency on a limited database. Our analysis is centered on the “default generic face” that we created by overlapping random samples of fake human faces generated by TPDNE's algorithms – a version of Generative Adversarial Network, the StyleGAN2. To carry these experiments, we built a database with 4100 fake human faces taken from TPDNE via web scraping; we analysed them through a Python language script; and discussed behaviours identified in results. Our analyses are based on the use of images, called “cluster-images”, created from this overlapping of N arbitrary fake human faces by the TPDNE's algorithm. Our experiments showed that, independently of the group of fake human faces sampled, the same generic white face always appeared as a result. These results intrigue particularly because the lack of diversity of TPDNE's generated faces is not a mere problem to be fixed in this system in this digital infrastructure, but a dynamic of reinforcing standards that historically regulate bodies, territories and practices.