Interpreting and Steering AI Explanations with Interactive Visualizations

Artificial Intelligence (AI) has advanced at a rapid pace and is expected to revolutionize many biomedical applications. However, current AI methods are usually developed via a data-centric approach regardless of the usage context and the end users, posing challenges for domain users in interpreting AI, obtaining actionable insights, and collaborating with AI in decision-making and knowledge discovery.As a visualization researcher, I aim to address this challenge by combining interactive visualizations with interpretable AI. In this talk, I discuss and demonstrate the prospects for interactive visual explanations in the application of biomedical AI via real-world case studies. I present two methodologies for achieving this goal: 1) visualizations that explain AI models and predictions and 2) interaction mechanisms that integrate user feedback into AI models. Despite some challenges, I will conclude on an optimistic note: interactive visual explanations should be indispensable for Human-AI collaboration. The methodology discussed can be applied generally to other applications where human-AI collaborations are involved, assisting domain experts in data exploration and insight generation with the help of AI.