How can we better 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.
Websites and other resources
- Been presenting on TCAVs:
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Hosts / Producers
Ryan Watkins & Doug Leigh
How to Cite
Watkins, R., Leigh, D., & Kim, B.. (2019, July 9). Parsing Science – Peeking Behind the Curtain of Algorithms. figshare. https://doi.org/10.6084/m9.figshare.8862242
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