As AI systems grow more powerful, there is an increasing need to make these complex "black box" models interpretable and explainable. This seminar explores how data visualization techniques can provide crucial insights into how AI models operate and arrive at their outputs. Cutting-edge methods like saliency maps, decision trees, and dimensionality reduction visualizations allow us to peer inside deep neural networks and understand what factors they are considering.

The seminar also covers visualization literacy - effectively communicating AI explainability visualizations to different stakeholders. Case studies highlight best practices for visualizing model behavior, evaluating fairness, and instilling appropriate levels of trust. Attendees will gain an understanding of how visualization can demystify AI, foster transparency, and enable real-world deployment of these systems in high-stakes domains.