19 citations · 72 across the 35 of their papers we have counts for
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cs.LG2023★ 2 cited
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
Junqi Jiang, Jianglin Lan, Francesco Leofante +2
Counterfactual Explanations (CEs) have received increasing interest as a major methodology for explaining neural network classifiers. Usually, CEs for an input-output pair are defi…
cs.AI2023★ 1 cited
Interactive Explanations by Conflict Resolution via Argumentative Exchanges
Antonio Rago, Hengzhi Li, Francesca Toni
As the field of explainable AI (XAI) is maturing, calls for interactive explanations for (the outputs of) AI models are growing, but the state-of-the-art predominantly focuses on s…