12 citations · 51 across the 21 of their papers we have counts for
6 papers · 1 filter
Unifying Formal Explanations: A Complexity-Theoretic Perspective
Shahaf Bassan, Xuanxiang Huang, Guy Katz
Previous work has explored the computational complexity of deriving two fundamental types of explanations for ML model predictions: (1) *sufficient reasons*, which are subsets of i…
SHAP scores fail pervasively even when Lipschitz succeeds
Olivier Letoffe, Xuanxiang Huang, Joao Marques-Silva
The ubiquitous use of Shapley values in eXplainable AI (XAI) has been triggered by the tool SHAP, and as a result are commonly referred to as SHAP scores. Recent work devised examp…
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation
Yacine Izza, Xuanxiang Huang, Antonio Morgado +3
The uses of machine learning (ML) have snowballed in recent years. In many cases, ML models are highly complex, and their operation is beyond the understanding of human decision-ma…
Towards trustable SHAP scores
Olivier Letoffe, Xuanxiang Huang, Joao Marques-Silva
SHAP scores represent the proposed use of the well-known Shapley values in eXplainable Artificial Intelligence (XAI). Recent work has shown that the exact computation of SHAP score…
The Inadequacy of Shapley Values for Explainability
Xuanxiang Huang, Joao Marques-Silva
This paper develops a rigorous argument for why the use of Shapley values in explainable AI (XAI) will necessarily yield provably misleading information about the relative importan…
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…