3 citations · 9 across the 10 of their papers we have counts for
10 papers
Robust Ambiguity Detection (RAD) From Model- and Feature-Space Consistency
Manya Singh, Mark T. Keane, Arjun Pakrashi
Machine learning models should be robust, in the sense of remaining predictively consistent under permissible variations. A model's predictions should ideally remain unchanged when…
Informative Semi-Factuals for XAI: The Elaborated Explanations that People Prefer
Saugat Aryal, Mark T. Keane
Recently, in eXplainable AI (XAI), explanations -- so-called semi-factuals -- have emerged as a popular strategy that explains how a predicted outcome $\textit{c…
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…
Even if Explanations: Prior Work, Desiderata & Benchmarks for Semi-Factual XAI
Saugat Aryal, Mark T Keane
Recently, eXplainable AI (XAI) research has focused on counterfactual explanations as post-hoc justifications for AI-system decisions (e.g. a customer refused a loan might be told:…
Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise
Courtney Ford, Mark T Keane
Very few eXplainable AI (XAI) studies consider how users understanding of explanations might change depending on whether they know more or less about the to be explained domain (i.…
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…
Solving the Class Imbalance Problem Using a Counterfactual Method for Data Augmentation
Mohammed Temraz, Mark T. Keane
Learning from class imbalanced datasets poses challenges for many machine learning algorithms. Many real-world domains are, by definition, class imbalanced by virtue of having a ma…
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future
Mohammed Temraz, Eoin Kenny, Elodie Ruelle +3
Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet. In this paper, we examine a CBR system (…
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation
Mark T Keane, Eoin M Kenny, Mohammed Temraz +2
Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ide…