B
Been Kim
Researcher at Google
Publications - 75
Citations - 13180
Been Kim is an academic researcher from Google. The author has contributed to research in topics: Interpretability & Computer science. The author has an hindex of 38, co-authored 70 publications receiving 8631 citations. Previous affiliations of Been Kim include Massachusetts Institute of Technology & Allen Institute for Artificial Intelligence.
Papers
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Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez,Been Kim +1 more
TL;DR: This position paper defines interpretability and describes when interpretability is needed (and when it is not), and suggests a taxonomy for rigorous evaluation and exposes open questions towards a more rigorous science of interpretable machine learning.
Posted Content
SmoothGrad: removing noise by adding noise
TL;DR: SmoothGrad is introduced, a simple method that can help visually sharpen gradient-based sensitivity maps and lessons in the visualization of these maps are discussed.
Posted Content
Sanity Checks for Saliency Maps
TL;DR: It is shown that some existing saliency methods are independent both of the model and of the data generating process, and methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model.
Proceedings Article
Sanity Checks for Saliency Maps
TL;DR: In this article, the authors propose an actionable methodology to evaluate what kinds of explanations a given saliency method can and cannot provide, and find that reliance solely on visual assessment can be misleading.
Proceedings Article
Examples are not enough, learn to criticize! Criticism for Interpretability
TL;DR: Motivated by the Bayesian model criticism framework, MMD-critic is developed, which efficiently learns prototypes and criticism, designed to aid human interpretability.