Marion Coville’s research while affiliated with IAE de Poitiers and other places

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.

This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ and ‘artificial intelligence impersonation’. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes – not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness.