Privacy-preserving Ground-truth Data for Evaluating Additive Feature Attribution in Regression Models with Additive CBR and CQV

Explainable artificial intelligence (XAI) methods produce information outputs based on a target artificial intelligence model to be explained. The most popular information output is produced by XAI methods of the category feature attribution, which produce the relative contribution of each input feature in a local instance. These relative contributions indicate how important each input feature is in a decision; this type of information is expected to provide explanatory value to users. In real-world regression tasks, feature attribution methods are crucial for comprehending model predictions. However, robust evaluation of such methods remains challenging due to a lack of ground-truth data and widely accepted evaluation metrics, such as accuracy for classification or mean absolute error for regression. This paper proposes a novel approach for generating synthetic, privacy-preserving ground-truth datasets for regression problems that retain original feature behaviour, enabling rigorous feature attribution evaluation without compromising sensitive information. We introduce additive case-based reasoning (AddCBR) as a model-aligned and interpretable baseline to benchmark additive feature attribution methods. This work also demonstrates the first use of the coefficient of quartile variation (CQV) as a statistical measure to quantify the consistency and stability of feature attribution methods. Altogether, these contributions form a comprehensive evaluation methodology for objectively assessing and comparing feature attribution methods in regression models. By providing a controlled evaluation pipeline with reliable baselines and metrics, this work addresses the current lack of consensus and benchmarking in XAI evaluation for regression models.