21 citations · 49 across the 7 of their papers we have counts for
3 papers · 1 filter
On Computing Probabilistic Abductive Explanations
Yacine Izza, Xuanxiang Huang, Alexey Ignatiev +3
The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches…
Feature Necessity & Relevancy in ML Classifier Explanations
Xuanxiang Huang, Martin C. Cooper, Antonio Morgado +2
Given a machine learning (ML) model and a prediction, explanations can be defined as sets of features which are sufficient for the prediction. In some applications, and besides ask…
Provably Precise, Succinct and Efficient Explanations for Decision Trees
Yacine Izza, Alexey Ignatiev, Nina Narodytska +2
Decision trees (DTs) embody interpretable classifiers. DTs have been advocated for deployment in high-risk applications, but also for explaining other complex classifiers. Neverthe…