ConTrust: Robust Contrastive Explanations for Deep Neural Networks

(started Aug 2022, duration 4 years)

Motivation

The area of Explainable Artificial Intelligence (XAI) is concerned with providing methods and tools to improve the interpretability of learned models, such as Deep Neural Networks (DNNs). A widely recognised factor contributing to this end is the availability of contrastive explanations, arguments supporting or contrasting the decisions taken by a DNN. While several approaches exist to generate such explanations, they are often lacking robustness, i.e., they may produce completely different explanations for similar events.

In this project, I will extend techniques for the verification of DNNs and develop new explainability methods to generate robust contrastive explanations for deep neural networks. Such an approach is motivated by several similarities between the two research areas, but also by the lack of effective solutions within XAI for generating robust explanations.

Publications

1. Interval Abstractions for Robust Counterfactual Explanations. J. Jiang, F. Leofante, A. Rago, F. Toni. The journal of Artificial Intelligence (AIJ), 2024.
2. Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations. L. Marzari, F. Leofante, F. Cicalese, A. Farinelli. The 27th European Conference on Artificial Intelligence (ECAI 2024).
3. Robust Counterfactual Explanations in Machine Learning: A Survey. J.Jiang, F. Leofante, A. Rago, F. Toni. The 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024).
4. Recourse under Model Multiplicity via Argumentative Ensembling. J.Jiang, A. Rago, F. Leofante, F. Toni. The 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2024).
5. Promoting Counterfactual Robustness through Diversity. F. Leofante, N. Potyka. The 38th AAAI Conference on Artificial Intelligence (AAAI 2024).
6. Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation. J. Jiang, J. Lan, F. Leofante, A. Rago, F. Toni. The 15th Asian Conference on Machine Learning (ACML 2023).
7. Robust Explanations for Human-Neural Multi-agent Systems with Formal Verification. F. Leofante, A. Lomuscio. The 20th European Conference on Multi-Agent Systems (EUMAS 2023).
8. Counterfactual Explanations and Model Multiplicity: a Relational Verification View. F. Leofante, E. Botoeva, V. Rajani. The 20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023).
9. Towards Robust Contrastive Explanations for Human-Neural Multi-agent Systems. F. Leofante, A. Lomuscio. The 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023).
10. Formalising the Robustness of Counterfactual Explanations for Neural Networks. J. Jiang*, F. Leofante*, A. Rago, F. Toni. The 37th AAAI Conference on Artificial Intelligence (AAAI 2023). * Equal contribution.

Tutorials

1. Robust Explainable AI: the Case of Counterfactual Explanations. F. Leofante. The 26th European Conference on Artificial Intelligence (ECAI 2023).

Other disseminiation activities

1. Robust Explainable AI: the Case of Counterfactual Explanations. F. Leofante. Imperial College London Graduate School Seminars 2023. DOI:10.52843/cassyni.2w7cl0.

Funding

This research is funded by Imperial College London under the ICRF fellowship scheme.