Data Feminism for AI

In Data Feminism (MIT Press, 2020), Klein and her coauthor Catherine D'Ignazio established a set of principles for doing more just and equitable data science. Informed by the past several decades of intersectional feminist activism and critical thought, the principles of data feminism modeled how to examine and challenge power, rethink binaries and hierarchies, elevate emotion and embodiment, consider context, embrace pluralism, and make labor visible. How can these principles be applied to the current conversation about AI, its present harms, and its future possibilities? This talk will briefly summarize the principles of data feminism before moving to a set of examples that show how these principles can be applied - and extended - in our current technological landscape.

Please RSVP for this talk via this Eventbrite Link.

The lecture will be followed up by a panel by Professors Adrian Daub and Chiara Sabatti of Stanford University. The talk will be followed by a reception. RSVP is necessary for entry.