AI for High-Stakes Decision Making with Hima Lakkaraju

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re joined by Hima Lakkaraju, an Assistant Professor at Harvard University with appointments in both the Business School and Department of Computer Science.

At CVPR, Hima was a keynote speaker at the Fair, Data-Efficient and Trusted Computer Vision Workshop, where she spoke on Understanding the Perils of Black Box Explanations. Hima talks us through her presentation, which focuses on the unreliability of explainability techniques that center perturbations, such as LIME or SHAP, as well as how attacks on these models can be carried out, and what these attacks look like. We also discuss people’s tendency to trust computer systems and their outputs, her thoughts on collaborator (and former TWIML guest) Cynthia Rudin’s theory that we shouldn’t use black-box algorithms, and much more.

Thanks to our sponsor

I’d like to send a huge thank you to our friends at Qualcomm for their support of the podcast, and their sponsorship of this series! Qualcomm AI Research is dedicated to advancing AI to make its core capabilities — perception, reasoning, and action — ubiquitous across devices. Their work makes it possible for billions of users around the world to have AI-enhanced experiences on Qualcomm Technologies-powered devices. To learn more about what Qualcomm is up to on the research front, visit here.

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“More On That Later” by Lee Rosevere licensed under CC By 4.0

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