Representational Defaults and the Challenge of Pluralism for Generative AI
Date:
In the talk titled, I will explore a central tension in decisions in generative AI representations: current approaches to "fixing" representational harms often push toward convergence — consensus on what is appropriate, standardized evaluations, universal guidelines. But lived experiences of representation demand the opposite: multiplicity, context-dependence, the ability to be legible in some contexts while pushing boundaries in others. In this talk I provide high-level guidance that required a focus not only on the distributional effects of disparities in representations, but also the process of creating with generative AI, shaping what possibilities are thinkable, imaginable, and relevant. I enumerate design opportunities: namely 1) context-conditional defaults and friction, for a system that aims to broaden our horizons (or set of possibilities), and 2) artifacts for representational provenance, that aim to carry some of the context alongside AI-generated content, creating opportunities for critical and measured engagement. Overall, the proposed affordances asked us to think about what a representation is intended to depict and stand for, who it's for, and how it that context is carried (or removed) as it is ported into different contexts.
