124 citations · 206 across the 11 of their papers we have counts for
11 papers
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
Nari Johnson, Ángel Alexander Cabrera, Gregory Plumb +1
Machine learning (ML) models that achieve high average accuracy can still underperform on semantically coherent subsets ("slices") of data. This behavior can have significant socie…
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models
Gregory Plumb, Nari Johnson, Ángel Alexander Cabrera +1
A growing body of work studies Blindspot Discovery Methods ("BDM"s): methods that use an image embedding to find semantically meaningful (i.e., united by a human-understandable con…
Use-Case-Grounded Simulations for Explanation Evaluation
Valerie Chen, Nari Johnson, Nicholay Topin +2
A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use…
Finding and Fixing Spurious Patterns with Explanations
Gregory Plumb, Marco Tulio Ribeiro, Ameet Talwalkar
Image classifiers often use spurious patterns, such as "relying on the presence of a person to detect a tennis racket, which do not generalize. In this work, we present an end-to-e…
Sanity Simulations for Saliency Methods
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Saliency methods are a popular class of feature attribution explanation methods that aim to capture a model's predictive reasoning by identifying "important" pixels in an input ima…
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Valerie Chen, Jeffrey Li, Joon Sik Kim +2
Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and t…