13 papers
Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI
Akchunya Chanchal, David A. Kelly, Hana Chockler
Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality of the explanations. In this pap…
Causal Explanations for Image Classifiers
Hana Chockler, David A. Kelly, Daniel Kroening +1
Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find them. However, none of the existing…
Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy
David A. Kelly, Nathan Blake
Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be trusted and decisions justif…
Explaining Failures of Cyber-Physical Systems with Actual Causality
Khen Elimelech, Tom Yaacov, David A. Kelly +2
Modern autonomous Cyber-Physical Systems (CPSs), such as self-driving cars, face increasingly complex demands, and yet are expected to act reliably. The black-box nature often char…
Out-of-the-box: Black-box Causal Attacks on Object Detectors
Melane Navaratnarajah, David A. Kelly, Hana Chockler
Adversarial perturbations are a useful way to expose vulnerabilities in object detectors. Existing perturbation methods are frequently white-box, architecture specific and use a lo…
If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models
David A. Kelly, Hana Chockler
In order to gain fresh insights about the information processing characteristics of different audio classification models, we propose transferability analysis. Given a minimal, suf…