Might we be better able to understand what’s going on inside the “black box” of machine learning algorithms? In episode 53, Been Kim from Google Brain talks with us about her research into creating algorithms that can explain why they make the recommendations they do via concepts that are relatable by their users. Her articles “Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)” and “Human-centered tools for coping with imperfect algorithms during medical decision-making” were first published on the open-access preprint server arxiv.org, and presented at the International Conference on Machine Learning in 2018.

Behind the Curtain of Algorithms - Been Kim
Behind the Curtain of Algorithms - Been Kim
Behind the Curtain of Algorithms - Been Kim Behind the Curtain of Algorithms - Been Kim
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Hosts / Producers

Ryan Watkins & Doug Leigh

How to Cite

Watkins, R., Leigh, D., & Kim, B.. (2019, July 9). Parsing Science – Behind the Curtain of Algorithms. figshare. https://doi.org/10.6084/m9.figshare.8862242


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