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06/13/2023
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
Machine learning (ML) models that achieve high average accuracy can stil...
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07/08/2022
Evaluating Systemic Error Detection Methods using Synthetic Images
We introduce SpotCheck, a framework for generating synthetic datasets to...
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06/05/2022
Use-Case-Grounded Simulations for Explanation Evaluation
A growing body of research runs human subject evaluations to study wheth...
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06/03/2021
Finding and Fixing Spurious Patterns with Explanations
Machine learning models often use spurious patterns such as "relying on ...
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05/13/2021
Sanity Simulations for Saliency Methods
Saliency methods are a popular class of feature attribution tools that a...
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03/10/2021
Towards Connecting Use Cases and Methods in Interpretable Machine Learning
Despite increasing interest in the field of Interpretable Machine Learni...
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11/02/2020
A Learning Theoretic Perspective on Local Explainability
In this paper, we explore connections between interpretable machine lear...
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03/03/2020
Explaining Groups of Points in Low-Dimensional Representations
A common workflow in data exploration is to learn a low-dimensional repr...
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05/31/2019
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Most of the work on interpretable machine learning has focused on design...
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02/18/2019
Regularizing Black-box Models for Improved Interpretability
Most work on interpretability in machine learning has focused on designi...
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07/09/2018
