1 citations · 1 across the 16 of their papers we have counts for
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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…
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…
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…
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…