59 citations · 100 across the 12 of their papers we have counts for
13 papers
Regulation of Language Models With Interpretability Will Likely Result In A Performance Trade-Off
Eoin M. Kenny, Julie A. Shah
Regulation is increasingly cited as the most important and pressing concern in machine learning. However, it is currently unknown how to implement this, and perhaps more importantl…
Explainable deep learning improves human mental models of self-driving cars
Eoin M. Kenny, Akshay Dharmavaram, Sang Uk Lee +6
Self-driving cars increasingly rely on deep neural networks to achieve human-like driving. The opacity of such black-box planners makes it challenging to accurately anticipate when…
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…
The Utility of "Even if..." Semifactual Explanation to Optimise Positive Outcomes
Eoin M. Kenny, Weipeng Huang
When users receive either a positive or negative outcome from an automated system, Explainable AI (XAI) has almost exclusively focused on how to mutate negative outcomes into posit…
Human-Guided Complexity-Controlled Abstractions
Andi Peng, Mycal Tucker, Eoin Kenny +3
Neural networks often learn task-specific latent representations that fail to generalize to novel settings or tasks. Conversely, humans learn discrete representations (i.e., concep…
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…
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…
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
Linyi Yang, Eoin M. Kenny, Tin Lok James Ng +3
Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intellige…
On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
Eoin M. Kenny, Mark T. Keane
There is a growing concern that the recent progress made in AI, especially regarding the predictive competence of deep learning models, will be undermined by a failure to properly…
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier
Courtney Ford, Eoin M. Kenny, Mark T. Keane
This paper reports two experiments (N=349) on the impact of post hoc explanations by example and error rates on peoples perceptions of a black box classifier. Both experiments show…
The Twin-System Approach as One Generic Solution for XAI: An Overview of ANN-CBR Twins for Explaining Deep Learning
Mark T. Keane, Eoin M. Kenny
The notion of twin systems is proposed to address the eXplainable AI (XAI) problem, where an uninterpretable black-box system is mapped to a white-box 'twin' that is more interpret…
How Case Based Reasoning Explained Neural Networks: An XAI Survey of Post-Hoc Explanation-by-Example in ANN-CBR Twins
Mark T Keane, Eoin M Kenny
This paper surveys an approach to the XAI problem, using post-hoc explanation by example, that hinges on twinning Artificial Neural Networks (ANNs) with Case-Based Reasoning (CBR)…