Integrating shapley value and least core attribution for robust explainable AI in rent prediction

Abstract

With the widespread application of artificial intelligence in real estate price prediction, model explainability has become a critical factor influencing its acceptability and trustworthiness. The Shapley value, as a classic cooperative game theory method, quantifies the average marginal contribution of each feature, ensuring global fairness in the explanation allocation. However, its focus on average fairness lacks robustness under data perturbations, model changes, and adversarial attacks. To address this limitation, this paper proposes a hybrid explainability framework that integrates the Shapley value and Least Core attribution. The framework leverages the Least Core theory by formulating a linear programming problem to minimize the maximum dissatisfaction of feature subsets, providing bottom-line fairness. Furthermore, the attributions from the Shapley value and Least Core are combined through a weighted fusion approach, where the weight acts as a tunable hyperparameter to balance the global fairness and worst-case robustness. The proposed framework is seamlessly integrated into mainstream machine learning models such as XGBoost. Empirical evaluations on real-world real estate rental data demonstrate that this hybrid attribution method not only preserves the global fairness of the Shapley value but also significantly enhances the explanation consistency and trustworthiness under various data perturbations. This study provides a new perspective for robust explainable AI in high-risk decision-making scenarios and holds promising potential for practical applications.