Building Fair and Accountable AI Systems

Join us for a conversation on bias and fairness in AI systems as they move from research into real-world deployment. We’ll explore how bias emerges in data and models, the tradeoffs between performance and equitable outcomes, and why fairness requires structured evaluation beyond standard model metrics. The discussion will also cover the role of data quality, human oversight, and why fairness must be clearly defined and continuously monitored in production.