47 citations · 99 across the 17 of their papers we have counts for
4 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…
On Computing Relevant Features for Explaining NBCs
Yacine Izza, Joao Marques-Silva
Despite the progress observed with model-agnostic explainable AI (XAI), it is the case that model-agnostic XAI can produce incorrect explanations. One alternative are the so-called…
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…
On Tackling Explanation Redundancy in Decision Trees
Yacine Izza, Alexey Ignatiev, Joao Marques-Silva
Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models. The interpretability of decision trees motivates explainability approaches by so-calle…