The last decade has seen a dramatic capture of digital material for machine learning production. This data is the basis for sense-making in AI, not as classical representations of the world with individual meaning, but as mass collections: ground truth for machine abstractions and operations. What happens when data is seen as an aggregate, stripped of context, meaning, and specificity? In what ways does training data limit what and how machine learning systems interpret the world? And most importantly, what forms of power do these approaches enhance and enable? Professor Kate Crawford is a leading international scholar of the social and political implications of artificial intelligence. In this lecture, Crawford shares new work that reflects on what’s at stake in the architecture and contents of training sets, and how they are increasingly part of our urban, legal, logistical, and commercial infrastructures. Series: "UC Berkeley Graduate Lectures" [Humanities] [Science] [Show ID: 37729]