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Found 6 papers, 4 papers with code
Contestable AI needs Computational Argumentation
AI has become pervasive in recent years, but state-of-the-art approaches predominantly neglect the need for AI systems to be contestable.
Interval Abstractions for Robust Counterfactual Explanations
Counterfactual Explanations (CEs) have emerged as a major paradigm in explainable AI research, providing recourse recommendations for users affected by the decisions of machine learning models.
Robust Counterfactual Explanations in Machine Learning: A Survey
Counterfactual explanations (CEs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models.
Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)
Model Multiplicity (MM) arises when multiple, equally performing machine learning models can be trained to solve the same prediction task.
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
In this work, we propose Provably RObust and PLAusible Counterfactual Explanations (PROPLACE), a method leveraging on robust optimisation techniques to address the aforementioned limitations in the literature.
Formalising the Robustness of Counterfactual Explanations for Neural Networks
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.
