The Global Labours of AI and Data Intensive Systems

Abstract

The remarkable capacities of AI-infused and data intensive systems are regularly presented as emblematic of technoscientific innovation and progress. Far less recognised is the human labour required to sift through and sort data, and ultimately train these systems. This is a labour distributed well beyond the presumed centres of technological innovation, and spun into and strewn across geographically distributed regions in the "Global South". This workshop brings together scholars and practitioners interested in and conducting research on the hidden labours that lie behind AI and data intensive systems. Participants will share perspectives or results of their research and, through highly exploratory engagements and dialogues, set out a critical mode of inquiry for future directions in research, design and policy.

Articulation, or the persistent problem with explanation

“…Only then could the work begin of incoporating sensibility to unrecognised work into the social study of innovation and its "apparatus of explanation," and gain the efficacy it has today, for example, in the form of a critical exposé of the invisible labelling that workers in Kenya, India and other countries perform, so that "generative AI" can do well at image recognition (Catanzariti et al, 2021).…”

“…At the time, innovation was a restrictive notion ‐ and it still is ‐ which allocates a disproportionate amount of agency to the engineers who create technology, while defining the rest of us, workers and consumers, as more or less passive “users.” Feminist studies of invisible labour in technology‐mediated work used ethnographic description to demonstrate how often female workers accomplished tasks ‐ performing flight checks, copying documents ‐ that were later acribed to the smooth running of “technology.” It was these descriptions of invisible labour that allowed this phenomenon to be named and defined. Only then could the work begin of incoporating sensibility to unrecognised work into the social study of innovation and its “apparatus of explanation,” and gain the efficacy it has today, for example, in the form of a critical exposé of the invisible labelling that workers in Kenya, India and other countries perform, so that “generative AI” can do well at image recognition (Catanzariti et al., 2021).…”

REGROW: Reimagining Global Crowdsourcing for Better Human-AI Collaboration

“…NeurIPS '20 and VLDB '21 workshops on trust, ethics and data excellence in crowdsourcing [52,53]. Finally, at CSCW '21, a workshop was organised around the notion of the hidden labour that powers modern day's AI systems [7].…”