9 citations · 31 across the 10 of their papers we have counts for
8 papers · 1 filter
Rigorous Explanations for Tree Ensembles
Yacine Izza, Alexey Ignatiev, Xuanxiang Huang +2
Tree ensembles (TEs) find a multitude of practical applications. They represent one of the most general and accurate classes of machine learning methods. While they are typically q…
Uncovering Bugs in Formal Explainers: A Case Study with PyXAI
Xuanxiang Huang, Yacine Izza, Alexey Ignatiev +1
Formal explainable artificial intelligence (XAI) offers unique theoretical guarantees of rigor when compared to other non-formal methods of explainability. However, little attentio…
Most General Explanations of Tree Ensembles (Extended Version)
Yacine Izza, Alexey Ignatiev, Sasha Rubin +2
Explainable Artificial Intelligence (XAI) is critical for attaining trust in the operation of AI systems. A key question of an AI system is ``why was this decision made this way''.…
Axiomatic Aggregations of Abductive Explanations
Gagan Biradar, Yacine Izza, Elita Lobo +2
The recent criticisms of the robustness of post hoc model approximation explanation methods (like LIME and SHAP) have led to the rise of model-precise abductive explanations. For e…
Delivering Inflated Explanations
Yacine Izza, Alexey Ignatiev, Peter Stuckey +1
In the quest for Explainable Artificial Intelligence (XAI) one of the questions that frequently arises given a decision made by an AI system is, ``why was the decision made in this…
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…