arXiv:2601.08703v1 Announce Type: cross 
Abstract: Explainable artificial intelligence (XAI) is concerned with producing explanations indicating the inner workings of models. For a Rashomon set of similarly performing models, explanations provide a way of disambiguating the behavior of individual models, helping select models for deployment. However explanations themselves can vary depending on the explainer used, and need to be evaluated. In the paper "Evaluating Model Explanations without Ground Truth", we proposed three principles of explanation evaluation and a new method "AXE" to evaluate the quality of feature-importance explanations. We go on to illustrate how evaluation metrics that rely on comparing model explanations against ideal ground truth explanations obscure behavioral differences within a Rashomon set. Explanation evaluation aligned with our proposed principles would highlight these differences instead, helping select models from the Rashomon set. The selection of alternate models from the Rashomon set can maintain identical predictions but mislead explainers into generating false explanations, and mislead evaluation methods into considering the false explanations to be of high quality. AXE, our proposed explanation evaluation method, can detect this adversarial fairwashing of explanations with a 100% success rate. Unlike prior explanation evaluation strategies such as those based on model sensitivity or ground truth comparison, AXE can determine when protected attributes are used to make predictions.

يناقش المقال بعنوان 'تقييم قدرة الشروحات على توضيح النماذج في مجموعة راشومون' دور الذكاء الاصطناعي القابل للتفسير (XAI) في توضيح سلوك النماذج ضمن مجموعة راشومون، حيث تؤدي نماذج متعددة أداءً مشابهًا. يقدم طريقة AXE لتقييم شروحات أهمية الميزات، مؤكدًا على الحاجة إلى مقاييس تقييم فعالة تكشف عن الاختلافات السلوكية بين النماذج.

El artículo titulado 'Evaluando la capacidad de las explicaciones para desambiguar modelos en un conjunto de Rashomon' discute el papel de la inteligencia artificial explicable (XAI) en la clarificación del comportamiento de los modelos dentro de un conjunto de Rashomon, donde múltiples modelos tienen un rendimiento similar. Introduce el método AXE para evaluar explicaciones de importancia de características, enfatizando la necesidad de métricas de evaluación efectivas que revelen diferencias de comportamiento entre los modelos.

L'article intitulé 'Évaluation de la capacité des explications à disambiguïser les modèles dans un ensemble de Rashomon' aborde le rôle de l'intelligence artificielle explicable (XAI) dans la clarification du comportement des modèles au sein d'un ensemble de Rashomon, où plusieurs modèles ont des performances similaires. Il introduit la méthode AXE pour évaluer les explications d'importance des caractéristiques, en soulignant la nécessité de métriques d'évaluation efficaces qui révèlent les différences comportementales entre les modèles.

The paper titled 'Evaluating the Ability of Explanations to Disambiguate Models in a Rashomon Set' discusses the role of explainable artificial intelligence (XAI) in clarifying the behavior of models within a Rashomon set, where multiple models perform similarly. It introduces the AXE method for evaluating feature-importance explanations, emphasizing the need for effective evaluation metrics that reveal behavioral differences among models.

Evaluating the Ability of Explanations to Disambiguate Models in a Rashomon Set

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Evaluating the Ability of Explanations to Disambiguate Models in a Rashomon Set

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