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The Gradient: Perspectives on AI
Vera Liao: AI Explainability and Transparency

Vera Liao: AI Explainability and Transparency

On designing AI systems for explainability and transparency, pitfalls in explainability methods, and transparency in the age of LLMs.

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In episode 101 of The Gradient Podcast, Daniel Bashir speaks to Vera Liao.

Vera is a Principal Researcher at Microsoft Research (MSR) Montréal where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics) group. She is trained in human-computer interaction research and works on human-AI interaction, currently focusing on explainable AI and responsible AI. She aims to bridge emerging AI technologies and human-centered design practices, and use both qualitative and quantitative methods to generate recommendations for technology design. Before joining MSR, Vera worked at IBM TJ Watson Research Center, and her work contributed to IBM products such as AI Explainability 360, Uncertainty Quantification 360, and Watson Assistant.

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