1 citations · 2 across the 19 of their papers we have counts for
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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…
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…
Evaluation of Black-Box XAI Approaches for Predictors of Values of Boolean Formulae
Stav Armoni-Friedmann, Hana Chockler, David A. Kelly
Evaluating explainable AI (XAI) approaches is a challenging task in general, due to the subjectivity of explanations. In this paper, we focus on tabular data and the specific use c…
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…
Defining and Quantifying Creative Behavior in Popular Image Generators
Aditi Ramaswamy, Hana Chockler, Melane Navaratnarajah
Creativity of generative AI models has been a subject of scientific debate in the last years, without a conclusive answer. In this paper, we study creativity from a practical persp…
SpecReX: Explainable AI for Raman Spectroscopy
Nathan Blake, David A. Kelly, Akchunya Chanchal +3
Raman spectroscopy is becoming more common for medical diagnostics with deep learning models being increasingly used to leverage its full potential. However, the opaque nature of s…
3D ReX: Causal Explanations in 3D Neuroimaging Classification
Melane Navaratnarajah, Sophie A. Martin, David A. Kelly +2
Explainability remains a significant problem for AI models in medical imaging, making it challenging for clinicians to trust AI-driven predictions. We introduce 3D ReX, the first c…