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Paper short abstract

Drawing on a historical overview of computer vision’s role in contemporary AI, the research traces the material conditions of its production, from platform-based gig work to BPO companies in the global South, often operating under the narrative of impact sourcing.

Paper long abstract

Computer vision is the field that has primarily driven the contemporary expansion of subsymbolic AI. One specific event is commonly cited as the beginning of this new phase: the success of AlexNet in the 2012 ImageNet competition. While AlexNet’s success was certainly grounded in the innovative architecture of the neural network, it was also the outcome of a new form of labor organization: the large-scale image annotation carried out by Amazon Mechanical Turk microworkers, one of the first online platforms to develop a strong focus on AI training.

Since then, data annotation for AI training has expanded significantly, generating not only a multitude of different platforms but also redirecting a substantial portion of the BPO industry toward the training and fine-tuning of neural networks. This production regime is often framed as impact sourcing, i.e., the practice of recruiting highly marginalized social groups with low levels of education and limited access to formal employment. Behind the narrative that portrays this production model as a vehicle for emancipation and empowerment, often lies a business strategy that exploits the limited bargaining power of these social groups and their abundant labor supply.

Building on a historical and socio-technical analysis of AI, the proposal examines the case of companies in India that recruit marginalized groups along gendered, cultural, and religious lines of division to train computer vision models. Drawing primarily on semi-structured interviews with workers, the analysis addresses the concrete material conditions under which contemporary computer vision models are produced.