8 papers
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…
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…
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…