The incentive gap in data work in the era of large models

Katy Ilonka Gero, Payel Das, Pierre L. Dognin, Inkit Padhi, Prasanna Sattigeri, Kush R. Varshney

Abstract: There are repeated calls in the AI community to prioritize data work — collecting, curating, analysing and otherwise considering the quality of data. But this is not practised as much as advocates would like, often because of a lack of institutional and cultural incentives. One way to encourage data work would be to reframe it as more technically rigorous, and thereby integrate it into more-valued lines of research such as model innovation.