Vera Liao, Principal Researcher at Microsoft Research, discusses AI explainability and transparency. Topics include UX design and toolkits for AI explainability, interfaces for explanation and model capabilities, online social communities, the difference between explainability and interpretability, the importance of interactivity in explanations, understanding the social context of AI systems, evaluating AI explainability criteria, challenges in AI transparency, and the need for a collaborative community.

Vera Liao, Principal Researcher at Microsoft, discusses the importance of human-centered approach in rendering model explainability within a UI. She shares insights on why example-based explanations tend to out-perform feature-based ones and why traditional XAI methods may not be the solution for every explainability problem. Vera advocates for qualitative research in tandem with model work to improve outcomes and highlights the challenges of responsible AI.