Unité mixte de recherche 7235

Artificial intelligence, labour transformations, and inconspicuous inequalities: women’s work on digital ‘micro-tasking’ platforms

Intervenant : Paola Tubaro (CREST)


Around the world, countless workers perform data-driven tasks on online labour platforms to fuel the digital economy. Mostly brief, repetitive, and poorly paid, these so-called ‘micro-tasks’ include, for example, tagging objects in images, recording videos, and transcribing text, mostly for the artificial intelligence industry. Although these platforms have been harshly criticized for precarious working conditions and low wages, access to them is easy and open, even to unskilled people. In principle, women with care duties can be expected to benefit from flexible working hours and the possibility of working from home. However, this new form of online work fails to bridge the digital gender gap and may even exacerbate it. I demonstrate this result in three steps. First, inherited inequalities in the professional and domestic spheres turn platform-mediated micro-tasks into a ‘third shift’ on top of already burdened schedules. Second, the human capital of male and female data workers differs insofar as women are less likely to have received training in scientific and technological fields. Third, their social capital differs: using a ‘position generator’, a specific tool to capture workers’ access to information and support resources that can come from their contacts with people in different occupations, I show that women have fewer ties to digital-related professionals who could provide them with knowledge and advice on how to successfully navigate the world of platforms. Taken together, these factors leave women with fewer career prospects in a technology-driven workforce and reproduce their relegation to lower-level computer jobs already observed in the early history of 20th century technology.


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