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Found 11 papers, 2 papers with code
On Correcting SHAP Scores
Recent work uncovered examples of classifiers for which SHAP scores yield misleading feature attributions.
Refutation of Shapley Values for XAI -- Additional Evidence
Recent work demonstrated the inadequacy of Shapley values for explainable artificial intelligence (XAI).
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
A Refutation of Shapley Values for Explainability
This earlier work devised a brute-force approach to identify Boolean functions, defined on small numbers of features, and also associated instances, which displayed such inadequacy-revealing issues, and so served as evidence to the inadequacy of Shapley values for rule-based explainability.
Explainability is NOT a Game
Explainable artificial intelligence (XAI) aims to help human decision-makers in understanding complex machine learning (ML) models.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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From Robustness to Explainability and Back Again
In contrast with ad-hoc methods for eXplainable Artificial Intelligence (XAI), formal explainability offers important guarantees of rigor.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
The Inadequacy of Shapley Values for Explainability
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 importance of features for predictions.
On Computing Probabilistic Abductive Explanations
One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness.
Feature Necessity & Relevancy in ML Classifier Explanations
Given a machine learning (ML) model and a prediction, explanations can be defined as sets of features which are sufficient for the prediction.
On Deciding Feature Membership in Explanations of SDD & Related Classifiers
In contrast, this paper shows that for a number of families of classifiers, FMP is in NP.
Efficient Explanations for Knowledge Compilation Languages
Knowledge compilation (KC) languages find a growing number of practical uses, including in Constraint Programming (CP) and in Machine Learning (ML).
On Efficiently Explaining Graph-Based Classifiers
Recent work has shown that not only decision trees (DTs) may not be interpretable but also proposed a polynomial-time algorithm for computing one PI-explanation of a DT.
