Search Results for author:
Found 4 papers, 0 papers with code
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts
In this paper, we combine legal, philosophical and technical arguments to show that post-hoc explanation algorithms are unsuitable to achieve the law's objectives.
Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models
Despite the ubiquity of kernel-based clustering, surprisingly few statistical guarantees exist beyond settings that consider strong structural assumptions on the data generation process.
The Manifold Hypothesis for Gradient-Based Explanations
Across a range of different datasets -- MNIST, EMNIST, CIFAR10, X-ray pneumonia and Diabetic Retinopathy detection -- we demonstrate empirically that the more an explanation is aligned with the tangent space of the data, the more interpretable it tends to be.
A Bandit Model for Human-Machine Decision Making with Private Information and Opacity
A lower bound quantifies the worst-case hardness of optimally advising a decision maker who is opaque or has access to private information.
