82 citations · 110 across the 6 of their papers we have counts for
6 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…
OpenXAI: Towards a Transparent Evaluation of Model Explanations
Chirag Agarwal, Dan Ley, Satyapriya Krishna +6
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introd…
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…
Rethinking Stability for Attribution-based Explanations
Chirag Agarwal, Nari Johnson, Martin Pawelczyk +4
As attribution-based explanation methods are increasingly used to establish model trustworthiness in high-stakes situations, it is critical to ensure that these explanations are st…
Learning Predictive and Interpretable Timeseries Summaries from ICU Data
Nari Johnson, Sonali Parbhoo, Andrew Slavin Ross +1
Machine learning models that utilize patient data across time (rather than just the most recent measurements) have increased performance for many risk stratification tasks in the i…