Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning models to achieve desired outputs. While existing research primarily addresses static scenarios, real-world applications often involve data or model changes, potentially invalidating previously generated CFEs and rendering user-induced input changes ineffective. Current methods addressing this issue often support only specific models or change types, require extensive hyperparameter tuning, or fail to provide probabilistic guarantees on CFE robustness to model changes. This paper proposes a novel approach for generating CFEs that provides probabilistic guarantees for any model and change type, while offering interpretable and easy-to-select hyperparameters. We establish a theoretical framework for probabilistically defining robustness to model change and demonstrate how our BetaRCE method directly stems from it. BetaRCE is a post-hoc method applied alongside a chosen base CFE generation method to enhance the quality of the explanation beyond robustness. It facilitates a transition from the base explanation to a more robust one with user-adjusted probability bounds. Through experimental comparisons with baselines, we show that BetaRCE yields robust, most plausible, and closest to baseline counterfactual explanations.
@inproceedings{stepka2024cfeprob,title={Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change},author={St\k{e}pka, Ignacy and Lango, Mateusz and Stefanowski, Jerzy},booktitle={31st SIGKDD Conference on Knowledge Discovery and Data Mining - Research Track},publisher={Association for Computing Machinery},address={New York, NY, USA},year={2025},doi={10.1145/3690624.3709300}}
2024
Journal
A Multi–Criteria Approach for Selecting an Explanation from the Set of Counterfactuals Produced by an Ensemble of Explainers
Ignacy Stępka, Mateusz Lango, and Jerzy Stefanowski
International Journal of Applied Mathematics and Computer Science, 2024
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions. They can be generated by a variety of methods that optimize different, sometimes conflicting, quality measures and produce quite different solutions. However, choosing the most appropriate explanation method and one of the generated counterfactuals is not an easy task. Instead of forcing the user to test many different explanation methods and analysing conflicting solutions, in this paper, we propose to use a multi-stage ensemble approach that will select single counterfactual based on multiple-critera analysis, in order to offer a compromise solution that scores well on varied quality measures. This approach exploits the dominance relation and the ideal point decision aid method, which selects one counterfactual from the Pareto front. The conducted experiments demonstrated that the proposed approach generates fully actionable counterfactuals with attractive compromise values of the considered quality measures.
@article{stepka2024multi,title={A Multi--Criteria Approach for Selecting an Explanation from the Set of Counterfactuals Produced by an Ensemble of Explainers},author={St\k{e}pka, Ignacy and Lango, Mateusz and Stefanowski, Jerzy},journal={International Journal of Applied Mathematics and Computer Science},volume={34},number={1},pages={119--133},year={2024},doi={10.61822/amcs-2024-0009},}
Workshop
A SAT-based approach to rigorous verification of Bayesian networks
Ignacy Stępka, Nicholas Gisolfi, and Artur Dubrawski
In Workshop on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI) at Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2024
Recent advancements in machine learning have accelerated its widespread adoption across various real-world applications. However, in safety-critical domains, the deployment of machine learning models is riddled with challenges due to their complexity, lack of interpretability, and absence of formal guarantees regarding their behavior. In this paper, we introduce a verification framework tailored for Bayesian networks, designed to address these drawbacks. Our framework comprises two key components: (1) a two-step compilation and encoding scheme that translates Bayesian networks into Boolean logic literals, and (2) formal verification queries that leverage these literals to verify various properties encoded as constraints. Specifically, we introduce two verification queries: if-then rules (ITR) and feature monotonicity (FMO). We benchmark the efficiency of our verification scheme and demonstrate its practical utility in real-world scenarios.
@inproceedings{stepka2024sat,title={A SAT-based approach to rigorous verification of Bayesian networks},author={St\k{e}pka, Ignacy and Gisolfi, Nicholas and Dubrawski, Artur},year={2024},booktitle={Workshop on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI) at Joint European Conference on Machine Learning and Knowledge Discovery in Databases},}
2023
Conference
On usefulness of dominance relation for selecting counterfactuals from the ensemble of explainers
Ignacy Stępka, Mateusz Lango, and Jerzy Stefanowski
In Proceedings of the 4rd Polish Conference on Artificial Intelligence, PP-RAI 2023, 2023
Counterfactual explanations are widely used to explain ML model predictions by providing alternative scenarios. However, choosing the most appropriate explanation method and one of generated counterfactuals is not an easy task. In this paper, we propose an approach that filters out a large set of counterfactuals generated by a set of diverse algorithms through a multi-criteria subset selection problem solved using the dominance relation. Experiments show that exploiting the dominance relation results in a concise set of counterfactual explanations.
@inproceedings{stepka2023usefulness,title={On usefulness of dominance relation for selecting counterfactuals from the ensemble of explainers},author={St\k{e}pka, Ignacy and Lango, Mateusz and Stefanowski, Jerzy},booktitle={Proceedings of the 4rd Polish Conference on Artificial Intelligence, PP-RAI 2023},year={2023},publisher={Wydawnictwo Politechniki Łódzkiej},doi={10.34658/9788366741928},pages={125-130},}