Search Results for author:
Found 11 papers, 6 papers with code
Where Does My Model Underperform? A Human Evaluation of Slice Discovery Algorithms
Motivated by these challenges, ML researchers have developed new slice discovery algorithms that aim to group together coherent and high-error subsets of data.
Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models
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 concept) subsets of the data where an image classifier performs significantly worse.
Use-Case-Grounded Simulations for Explanation Evaluation
SimEvals involve training algorithmic agents that take as input the information content (such as model explanations) that would be presented to each participant in a human subject study, to predict answers to the use case of interest.
Finding and Fixing Spurious Patterns with Explanations
Image classifiers often use spurious patterns, such as "relying on the presence of a person to detect a tennis racket, which do not generalize.
Sanity Simulations for Saliency Methods
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 image.
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Despite increasing interest in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals of consumers' use cases.
A Learning Theoretic Perspective on Local Explainability
In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Explaining Groups of Points in Low-Dimensional Representations
A common workflow in data exploration is to learn a low-dimensional representation of the data, identify groups of points in that representation, and examine the differences between the groups to determine what they represent.
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, which lack guarantees about their explanation quality.
Regularizing Black-box Models for Improved Interpretability
Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable.
Model Agnostic Supervised Local Explanations
Some of the most common forms of interpretability systems are example-based, local, and global explanations.
