19 citations · 72 across the 35 of their papers we have counts for
3 papers · 1 filter
Formalising the Robustness of Counterfactual Explanations for Neural Networks
Junqi Jiang, Francesco Leofante, Antonio Rago +1
The use of counterfactual explanations (CFXs) is an increasingly popular explanation strategy for machine learning models. However, recent studies have shown that these explanation…
Forecasting Argumentation Frameworks
Benjamin Irwin, Antonio Rago, Francesca Toni
We introduce Forecasting Argumentation Frameworks (FAFs), a novel argumentation-based methodology for forecasting informed by recent judgmental forecasting research. FAFs comprise…
Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement
Antonio Rago, Pietro Baroni, Francesca Toni
Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI. We introduce a conceptualisation for generating argumenta…