Abstract As artificial intelligence systems become integral across domains, the demand for explainability, called eXplainable artificial intelligence (XAI), grows. Existing efforts have focused primarily on generating and evaluating explanations for black-box models, while a critical gap in directly enhancing models remains through these evaluations. It is important to consider the potential of this explanation process to improve model quality with feedback on training as well. XAI may be used to improve model performance while increasing model explainability. Under this view, this paper introduces Transformation-Selective hidden input evaluation for learning dynamics (T-SHIELD), a regularization family designed to improve model quality by hiding features of input, forcing the model to generalize without those features. Within this family, we propose XAI-SHIELD (X-SHIELD), a regularization for explainable artificial intelligence that uses explanations to select specific features to hide. In contrast to conventional approaches, X-SHIELD regularization seamlessly integrates into the objective function, enhancing model explainability while also improving performance. Experimental validation on benchmark datasets underscores X-SHIELD’s effectiveness in improving performance and overall explainability. The improvement is validated through experiments comparing models with and without X-SHIELD regularization, with further analysis exploring the rationale behind its design choices. This establishes X-SHIELD regularization as a promising pathway for developing reliable artificial intelligence regularization.
Sevillano-García et al. (Sat,) studied this question.