Embedded reproduction in platform data work

ABSTRACT

This paper focuses on the experiences of Latin American data workers who annotate data for machine learning algorithms through labor platforms. It introduces the notion of ‘embedded reproduction’: the relationship between embeddedness, the degree to which non-economic institutions and their social environment constrain socioeconomic activity, and social reproduction, or the activities that nurture, maintain, and regenerate the workforce. The analysis of 38 interviews with platform workers suggests they are situated in a highly disembedded market due to the lack of regulations on the data production process, giving free rein to platforms to set rules to their detriment. This article explores how this disembeddedness shapes social reproduction by studying three forms of collective social support received by workers: from family members, neighbors and local communities, and online groups. The support of these networks is primarily local, depends on high levels of trust, and is gendered. These findings suggest that platform data work is unsustainable from an embedded reproductive perspective since platform intermediation leads workers and local communities to carry out the social and economic risks associated with this form of gig work. This research invites a dialogue between the embeddedness framework with social reproduction as well as a consideration of the importance of nature and natural resources in the study of social environments.

KEYWORDS:

• Embeddedness
• social reproduction
• artificial intelligence
• platform labor
• data work
• gig economy

Acknowledgements

I want to acknowledge the workers who shared their knowledge and experience with me so that these ideas could be developed. Special thanks to Erika Biddle, Alessandro Delfanti, Josie Greenhill, Milagros Miceli, Sarah Sharma, Christine Tran, Rianka Singh, Paola Tubaro, Antonio Casilli, and the anonymous reviewers for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This work was supported by International Development Research Centre [Doctoral Research Award] and Schwartz Reisman Institute [Graduate Fellowship].

Notes on contributors

Julian Posada

Julian Posada is a Ph.D. candidate at the University of Toronto's Faculty of Information. His research, funded by the International Development Research Centre, studies the experiences of workers in Latin America who annotate data for machine learning through digital labour platforms and questions the current sustainability of AI systems.