DISEMBEDDEDNESS IN MACHINE LEARNING DATA WORK

Abstract

Firms and research organizations require humans to annotate raw data to make it compatible with machine learning algorithms. These tasks are often outsourced to individuals worldwide through labor platforms or infrastructures that serve as marketplaces where labour is exchanged as a commodity. The firms that operate them consider workers as “independent contractors” without the social and economic benefits of traditional employment relations. This presentation explores the personal networks of Latin American data workers who train and verify data for machine learning algorithms from their homes. A series of in-depth interviews and an analysis of a self-completion questionnaire and web traffic data suggests that these workers are embedded of networks of trusts build on online and offline interactions. These findings show a continuation of exploitative supply chains in the current artificial intelligence market, where wealthy companies and research institutions in advanced economies profit from the economic and political situation of developing countries to access disembedded labor. This paper concludes by arguing that, though outsourced online labour, artificial intelligence developers not only extract value from their workers, but also indirectly from their communities and personal networks.