Abstract: Artificial intelligence systems increasingly shape everyday life, influencing decisions in domains such as hiring, education, healthcare, and content generation. A growing body of evidence shows that these systems do not perform equally across populations: they often achieve lower predictive accuracy for sensitive groups, amplify historical biases present in data, and, in the case of generative models, produce stereotypical or skewed representations. 
In this talk, I will briefly cover how fairness evolved from predictive ML to generative AI and then move to a new class of fairness problems that emerge with large language models (LLMs) and increasingly agentic AI systems. These new settings rely on communication and dialogue in natural language, and sequential decisions that shape user experience and downstream outcomes. I will illustrate how this perspective opens new directions for evaluating and regulating fairness in AI, by extending existing notions of distributional and procedural fairness with the concept of interactional fairness.

Dr. Ruta Binkyte