3 citations · 7 across the 7 of their papers we have counts for
7 papers
Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks
Jeremy Goldwasser, Giles Hooker
Counterfactuals are a popular framework for interpreting machine learning predictions. These what if explanations are notoriously challenging to create for computer vision models:…
Targeted Learning for Data Fairness
Alexander Asemota, Giles Hooker
Data and algorithms have the potential to produce and perpetuate discrimination and disparate treatment. As such, significant effort has been invested in developing approaches to d…
Gaussian Rank Verification
Jeremy Goldwasser, Will Fithian, Giles Hooker
Statistical experiments often seek to identify random variables with the largest population means. This inferential task, known as rank verification, has been well-studied on Gauss…
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
Xi Xin, Giles Hooker, Fei Huang
The adoption of artificial intelligence (AI) across industries has led to the widespread use of complex black-box models and interpretation tools for decision making. This paper pr…
Longitudinal Counterfactuals: Constraints and Opportunities
Alexander Asemota, Giles Hooker
Counterfactual explanations are a common approach to providing recourse to data subjects. However, current methodology can produce counterfactuals that cannot be achieved by the su…
Statistical Significance of Feature Importance Rankings
Jeremy Goldwasser, Giles Hooker
Feature importance scores are ubiquitous tools for understanding the predictions of machine learning models. However, many popular attribution methods suffer from high instability…
Stabilizing Estimates of Shapley Values with Control Variates
Jeremy Goldwasser, Giles Hooker
Shapley values are among the most popular tools for explaining predictions of blackbox machine learning models. However, their high computational cost motivates the use of sampling…