'Micro-work' consists of fragmented data tasks that myriad providers execute on online platforms. While crucial to the development of data-based technologies, this little visible and geographically spread activity is particularly difficult to measure. To fill this gap, we combined qualitative and quantitative methods (online surveys, in-depth interviews, capture-recapture techniques, and web traffic analytics) to count micro-workers in a single country, France. On the basis of this analysis, we estimate that approximately 260,000 people are registered with micro-work platforms. Of these some 50,000 are 'regular' workers who do micro-tasks at least monthly and we speculate that using a more restrictive measure of 'very active' workers decreases this figure to 15,000. This analysis contributes to research on platform labour and the labour in the digital economy that lies behind artificial intelligence.
Around the world, myriad workers perform micro-tasks on online platforms to train and calibrate artificial intelligence solutions. Despite its apparent openness to anyone with basic skills, this form of crowd-work fails to fill gender gaps, and may even exacerbate them. We demonstrate this result in three steps. First, inequalities in both the professional and domestic spheres turn micro-tasking into a 'third shift' that adds to already heavy schedules. Second, the human and social capital of male and female workers differ-leaving women with fewer career prospects within a tech-driven workforce. Third, female micro-work reproduces relegation of women to lower-level computing work observed in the history of science and technology. Issue 1This paper is part of The gender of the platform economy, a special issue of Internet Policy Review guest-edited by Mayo Fuster Morell, Ricard Espelt and David Megias. Introduction: The gendered dimension of work on micro-tasking platformsMicro-tasking platforms are digital infrastructures that fragment large data projects into small bits, and allocate them to masses of anonymous providers, each of them executing remotely a tiny part of the whole and receiving a small compensation for it. Examples of micro-tasks include labelling images, categorising messages, recording short sentences, and transcribing audio snippets. Generally simple and short, they nonetheless serve to meet the data needs of today's fast-growing artificial intelligence industry (Casilli, 2019;Tubaro & Casilli, 2019;Tubaro et al., 2020a).At first glance, these platforms appear ' gender neutral' and largely inclusive.Clients companies target unidentified and uncredited masses, and are typically given very limited access to individual workers' profiles (if at all). Under these conditions, employment discrimination is unlikely to occur -and indeed the nascent literature on digital platforms has mostly taken it as non-existent. Recently,
Artificial intelligence advancements have reignited job displacement debates that focus on how the use of artificial intelligence affects labour, without considering how the production of this technology influences labour division. The generalisation of machine learning has created an increased demand for outsourced data workers. Outsourcing companies and crowdwork platforms are both used to generate, annotate, and enrich data. This data tasks are performed by workers from low-income countries, who often earn poverty wages. As with traditional outsourcing, workers must integrate complex multinational subcontracting networks. In this article, we examine how France outsources artificial intelligence-related tasks to workers in the African island nation of Madagascar. For our study, we interviewed 26 data workers, eight employees of French start-ups, and conducted secondary research on two artificial intelligence systems – a canteen checkout terminal and an algorithm to detect shoplifters in stores. The data collected allowed us to reconstruct an end-to-end artificial intelligence production value chain, revealing the need for data classification and artificial intelligence problematisation. Commercial artificial intelligence, therefore, does not displace employment by automating service jobs. Rather, by delocalising labour into the Global South, it lengthens the externalisation chain.
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