About
I am a Senior AI researcher at J.P. Morgan Chase, London, in their Trustworthy AI research group. Prior to this, I did my Ph.D. at University College Dublin with Mark Keane, and Postdoc at MIT with Julie Shah. I believe that AGI is converging towards a solved problem, and our efforts to understand the systems has not kept pace. In my research, I bridge this gap by designing interpretability methods which have a clear purpose, such as reliance calibration, model improvement, scientific discovery, and recourse.
My contributions towards this end which I am most proud of are in interpretable reinforcment learning with self-driving cars, and contrastive explanation in recourse. Please see our recent paper accepted to Nature where I demonstrate explanations can improve human mental models of autonomous vehicles.
In my future research, I plan to continue focus on developing explainable systems which focus on purpose-driven evaluation, most specifically in reliance calibration and scientific discovery where I envision it will have the greatest impact.
Recent News
- July 2026: Our work was accepted to Nature!
- July 2026: Our work was presented at ICML!
- May 2026: Our paper was presented at AISTATS.
- May 2026: I will be area chair at this year's NeurIPS conference.
Work Experience
- J.P. Morgan, Senior AI Researcher, 2024 (onwards)
- Postdoctoral Associate, MIT, Feb 2022 - Apr 2024
- Internship, Motional, Oct 2023 - Mar 2024
Education
- Ph.D. in Computer Science, UCD, 2022
- M.S. in Computer Science, UCD, 2019
- M.A in Musicology & Performance, Maynooth University, 2013
- BMus, Maynooth University, 2010
Selected Publications (from Google Scholar)
Explainable deep learning improves human mental models of self-driving cars
TL;DR: We show that concept-based explanations improve human mental models of self-driving cars, and that this translates into improve ability to predict the AV's future actions and general situational awareness.
Eoin M. Kenny, Akshay Dharmavaram, Sang Uk Lee, Tung Phan-Minh, Shreyas Rajesh, Yunqing Hu, Laura Major, Momchil S Tomov, Julie A. Shah
Beyond Proxy Metrics: MLLM-Based Human Surrogate Evaluation for Explainable AI
TL;DR: We ask whether multimodal LLMs can stand in for human participants when evaluating XAI, and find that the rank ordering of explanation methods replicates across five purpose-grounded domains, though not uniformly.
Eoin M. Kenny, Salim I. Amoukou, Tom Bewley
Under review at NeurIPS 2026
CAMP: Coherent Alignment of Multimodal Prototypes for Explainable Complementary Learning
TL;DR: We build the first prototype-based interpretable-by-design framework for multimodal tasks where each modality carries complementary rather than redundant evidence, showing a sub-1M parameter head can rival AutoML systems 100x its size.
Alvaro Lopez Pellicer, Eoin M. Kenny, Simran Lamba, Shubham Sharma, Plamen P Angelov, Saumitra Mishra
An Evaluation of Cost Functions for Algorithmic Recourse
TL;DR: We run the first large-scale benchmark of the cost functions used in algorithmic recourse, and show that Bradley-Terry models are best, but only when scaled up using LLMs as surrogate human labellers.
Eoin M. Kenny, Allan Anzagira, Tom Bewley, Freddy Lecue, Manuela Veloso
A Hybrid Model that Combines Machine Learning and Mechanistic Models for Useful Grass Growth Prediction
TL;DR: We show mechanistic and machine learning models of grass growth fail in complementary ways, and that a screening model choosing between them instance-by-instance beats either alone, especially under out-of-distribution weather.
Eoin M. Kenny, Elodie Ruelle, Mark T. Keane, Laurence Shalloo
The Utility of "Even if" Semifactual Explanation to Optimize Positive Outcomes
TL;DR: We show that semifactuals are more useful than conterfactuals when a user gets a positive outcome from an AI system.
Eoin M. Kenny, Weipeng Huang
Towards Interpretable Deep Reinforcement Learning with Human-Friendly Prototypes
TL;DR: We build the first inherently interpretable, general, well performaning, deep reinforcement learning algorithm.
Eoin M. Kenny, Mycal Tucker, and Julie A. Shah
[ICLR 2023] * Spotlight Presentation (top 25% of accepted papers)
On generating plausible counterfactual and semi-factual explanations for deep learning
TL;DR: We introduce the AI world to semi-factuals, and show a plausible way to generate them (and counterfactuals) using a framework called PIECE.
Eoin M. Kenny and Mark T. Keane
Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming
TL;DR: We show how to accuractly predict grass growth and offer "good" explanations to Irish Dairy Farmers.
Eoin M Kenny, Elodie Ruelle, Anne Geoghegan, Mohammed Temraz, Mark T Keane
[IJCAI 2020] Sister Conference Best Paper Track * Best Paper Award at ICCBR 2019.
Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies
TL;DR: We find that nearest neighbor exemplar-based explanation lead people to view classifiction errors as being “less incorrect”, moreover they do not effect trust.
Eoin M Kenny, Courtney Ford, Molly Quinn, Mark T Keane
Media
National AI Awards Ireland
TL;DR: I won the best application of AI in a student project for my work in Explainable AI in Smart Agriculture.
International Conference on Case-Based Reasoning
TL;DR: I won the best paper award at ICCBR 2019 which was themed on Explainable AI for my paper regarding Smart Agriculture.
Invited Talks (not exhaustive)
- Jun 2023, ML Labs at UCD: One way to do a Ph.D. (and postdoc)
- Dec 2022, Navy Centre for Applied Research in Artificial Intelligence: Interpretable Deep Reinforcement Learning
- Mar 2022, Imperial College London: Explaining Black-box algorithms
- Feb 2022, Robert Gordon University: On the utility of explanation-by-example
- Nov 2019, KBC Data Science Bootcamp: Explaining Artificial Intelligence Black-Box Systems
Academic Services
- Reviewer, NeurIPS 2021-2023
- Reviewer, ICLR 2023
- Reviewer, AAAI 2021
- Reviewer, ICML 2022-2023
- Reviewer, Artificial Intelligence Journal 2021-2022