8 papers
Causal Explanations of Process Monitor Predictions
Tom Yaacov, Nathan Blake, Hana Chockler
Process mining is widely used to diagnose processes and identify performance and compliance issues. Specifically, Predictive Process Monitoring (PPM) techniques use AI models to pr…
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…
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…
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…
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…