150 citations · 619 across the 52 of their papers we have counts for
27 papers · 1 filter
A Refutation of Shapley Values for Explainability
Xuanxiang Huang, Joao Marques-Silva
Recent work demonstrated the existence of Boolean functions for which Shapley values provide misleading information about the relative importance of features in rule-based explanat…
Explainability is NOT a Game
Joao Marques-Silva, Xuanxiang Huang
Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding complex machine learning (ML) models. One of the hallmarks of XAI are measures of rela…
On Logic-Based Explainability with Partially Specified Inputs
Ramón Béjar, António Morgado, Jordi Planes +1
In the practical deployment of machine learning (ML) models, missing data represents a recurring challenge. Missing data is often addressed when training ML models. But missing dat…
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…
From Robustness to Explainability and Back Again
Xuanxiang Huang, Joao Marques-Silva
Formal explainability guarantees the rigor of computed explanations, and so it is paramount in domains where rigor is critical, including those deemed high-risk. Unfortunately, sin…
Disproving XAI Myths with Formal Methods -- Initial Results
Joao Marques-Silva
The advances in Machine Learning (ML) in recent years have been both impressive and far-reaching. However, the deployment of ML models is still impaired by a lack of trust in how t…