12 citations · 25 across the 6 of their papers we have counts for
6 papers
Advancing Post Hoc Case Based Explanation with Feature Highlighting
Eoin Kenny, Eoin Delaney, Mark Keane
Explainable AI (XAI) has been proposed as a valuable tool to assist in downstream tasks involving human and AI collaboration. Perhaps the most psychologically valid XAI techniques…
Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals
Greta Warren, Mark T. Keane, Christophe Gueret +1
Counterfactual explanations are an increasingly popular form of post hoc explanation due to their (i) applicability across problem domains, (ii) proposed legal compliance (e.g., wi…
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ
Eoin Delaney, Arjun Pakrashi, Derek Greene +1
Counterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems due to their p…
Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions
Eoin Delaney, Derek Greene, Mark T. Keane
Whilst an abundance of techniques have recently been proposed to generate counterfactual explanations for the predictions of opaque black-box systems, markedly less attention has b…
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques
Mark T Keane, Eoin M Kenny, Eoin Delaney +1
In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer te…
Instance-based Counterfactual Explanations for Time Series Classification
Eoin Delaney, Derek Greene, Mark T. Keane
In recent years, there has been a rapidly expanding focus on explaining the predictions made by black-box AI systems that handle image and tabular data. However, considerably less…