3 citations · 13 across the 19 of their papers we have counts for
6 papers · 1 filter
Efficient Contrastive Explanations on Demand
Yacine Izza, Joao Marques-Silva
Recent work revealed a tight connection between adversarial robustness and restricted forms of symbolic explanations, namely distance-based (formal) explanations. This connection 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…
Logic-Based Explainability: Past, Present & Future
Joao Marques-Silva
In recent years, the impact of machine learning (ML) and artificial intelligence (AI) in society has been absolutely remarkable. This impact is expected to continue in the foreseea…
From SHAP Scores to Feature Importance Scores
Olivier Letoffe, Xuanxiang Huang, Nicholas Asher +1
A central goal of eXplainable Artificial Intelligence (XAI) is to assign relative importance to the features of a Machine Learning (ML) model given some prediction. The importance…
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…