Journal article on the perception of user-work in AI training in Aeoteroa

New article published on open access licence on Taylor & Francis’ Kōtuitui: New Zealand Journal of Social Sciences Online. In the article, that I co-authored with a team of colleagues at Waipapa Taumata Rau (The University of Auckland), we investigate various perspectives on user-labour in AI training.

Hidden humans: exploring perceptions of user-work and training artificial intelligence in Aotearoa New Zealand
Briony Blackmore, Michelle Thorp, Andrew Tzer-Yeu Chen, Fabio Morreale, Brent Burmester, Elham Bahmanteymouri, Matt Bartlett

Artificial intelligence systems require large amounts of data to allow them to learn and achieve high performance. That data is increasingly collected in extractive and exploitative ways, which transfer value and power from individuals to AI system owners. Our research focuses on data that is collected from users of digital platforms, through direct and indirect interaction with those platforms, in ways that are not communicated to users, without consent or compensation. This paper presents our findings from a series of interviews and workshops in the Aotearoa New Zealand context to identify common themes and concerns from a variety of perspectives. Reframing this type of interaction as work or labour brings into view an otherwise unrecognised harm of using this data for training AI systems, and illustrates a new class of exploitative data practices that have become normalised in the digital age. We found that participants particularly emphasised moral or ethical justifications for intervention over financial or economic reasons to act.