‘Affordances’ for Machine Learning

Affordances are how the features of a technology shape, but do not determine, the uses and effects of that technology. In this address, I will demonstrate the value of an affordance framework for the analysis and design of ML systems. Specifically, I will delineate and apply the mechanisms and conditions framework of affordance, which models the way technologies request, demand, encourage, discourage, refuse, and allow technical and social outcomes. Illustrated through a case example that traverses critical analysis of an ML systems and its imagined (re)making, the mechanisms and conditions framework lays bare not just that technical choices are profoundly social, but also how and for whom. This approach displaces vagaries and general claims with the particularities of systems in context, empowering critically minded practitioners while holding power—and the systems power relations produce—to account.