We introduce an abstraction framework based on interval neural networks to verify the ∆-robustness of CFXs against a possibly infinite set of changes to the model parameters, i.e., weights and biases. To remedy this, we propose the first notion to formally and deterministically assess the robustness (to model changes) of CFXs for neural networks, that we call ∆-robustness.

Existing attempts towards solving this problem are heuristic, and the robustness to model changes of the resulting CFXs is evaluated with only a small number of retrained models, failing to provide exhaustive guarantees. However, recent studies have shown that these explanations may not be robust to changes in the underlying model (e.g., following retraining), which raises questions about their reliability in real-world applications. The use of counterfactual explanations (CFXs) is an increasingly popular explanation strategy for machine learning models. Toni F et al., 2023, Formalising the robustness of counterfactual explanations for neural networks, 37th AAAI Conference on Artificial Intelligence (AAAI 2023), Publisher: Association for the Advancement of Artificial Intelligence, Pages: 14901-14909, ISSN: 2374-3468 We show how JPAA and (the considered formof) JPABA can be used to support forecasting by 1) supporting different forecasters (jurors) to determine the probability of arguments (and, in the JPABA case,sentences) with respect to their own probability spaces, while sharing arguments(and their components) and 2) supporting the aggregation of individual forecaststo produce group forecasts. The form of JPABA we consider is an instance of JPAA andresults from integrating Assumption-Based Argumentation (ABA) and probabilityspaces expressed by Bayesian networks, under the so-called constellation approach.It keeps the underlying structured argumentation and probabilistic reasoning modules separate while integrating them. In this paper we show how Jury-based Probabilistic Abstract Argumentation (JPAA) and a form of Jury-based Probabilistic Assumptionbased Argumentation (JPABA) can naturally support forecasting, whereby subjective probability estimates are combined to make predictions about future occurrences of events. Jury-based Probabilistic Argumentation supports the combination of opinionsby different reasoners. Probabilistic Argumentation supports a form of hybrid reasoning by integratingquantitative (probabilistic) reasoning and qualitative argumentation in a naturalway.

Toni F, Rago A, Cyras K, 2023, Forecasting with jury-based probabilistic argumentation, Journal of Applied Non Classical Logics, ISSN: 1166-3081

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