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…
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…
Sufficient, Necessary and Complete Causal Explanations in Image Classification
David A Kelly, Hana Chockler
Existing algorithms for explaining the outputs of image classifiers are based on a variety of approaches and produce explanations that frequently lack formal rigour. On the other h…