1 citations · 1 across the 16 of their papers we have counts for
18 papers
Causal Analysis of Failures of Cyber-Physical Systems with Continuous Inputs
Tom Yaacov, David A. Kelly, Khen Elimelech +1
Modern autonomous Cyber-Physical Systems (CPSs), such as self-driving cars, operate in increasingly complex environments while expected to maintain high reliability. The black-box…
Mask-Induced Displacement in Audio XAI via Logit Trajectory Decomposition
Nico García-Peguinho, David Kelly, Fabrizio Smeraldi +1
Perturbation-based XAI methods for audio classifiers often estimate feature importance by masking spectrogram regions and crediting output changes to the retained signal. Yet they…
Generating Medical Image Counterfactuals using Causal Explanations
David A. Kelly, Tom Yaacov, Nathan Blake +2
Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by limited explainability. Count…
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…
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…
I Guess That's Why They Call it the Blues: Causal Analysis for Audio Classifiers
David A. Kelly, Hana Chockler
It is well-known that audio classifiers often rely on non-musically relevant features and spurious correlations to classify audio. Hence audio classifiers are easy to manipulate or…
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…
I Am Big, You Are Little; I Am Right, You Are Wrong
David A. Kelly, Akchunya Chanchal, Nathan Blake
Machine learning for image classification is an active and rapidly developing field. With the proliferation of classifiers of different sizes and different architectures, the probl…
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…
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…
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…
Real-Time Incremental Explanations for Object Detectors in Autonomous Driving
Santiago Calderón-Peña, Hana Chockler, David A. Kelly
Object detectors are widely used in safety-critical real-time applications such as autonomous driving. Explainability is especially important for safety-critical applications, and…
MRxaI: Black-Box Explainability for Image Classifiers in a Medical Setting
Nathan Blake, Hana Chockler, David A. Kelly +2
Existing tools for explaining the output of image classifiers can be divided into white-box, which rely on access to the model internals, and black-box, agnostic to the model. As t…
You Only Explain Once
David A. Kelly, Hana Chockler, Daniel Kroening +4
In this paper, we propose a new black-box explainability algorithm and tool, YO-ReX, for efficient explanation of the outputs of object detectors. The new algorithm computes explan…