  As Artificial Intelligence systems become integral across domains, the demand
for explainability grows. While the effort by the scientific community is
focused on obtaining a better explanation for the model, it is important not to
ignore the potential of this explanation process to improve training as well.
While existing efforts primarily focus on generating and evaluating
explanations for black-box models, there remains a critical gap in directly
enhancing models through these evaluations. This paper introduces SHIELD
(Selective Hidden Input Evaluation for Learning Dynamics), a regularization
technique for explainable artificial intelligence designed to improve model
quality by concealing portions of input data and assessing the resulting
discrepancy in predictions. In contrast to conventional approaches, SHIELD
regularization seamlessly integrates into the objective function, enhancing
model explainability while also improving performance. Experimental validation
on benchmark datasets underscores SHIELD's effectiveness in improving
Artificial Intelligence model explainability and overall performance. This
establishes SHIELD regularization as a promising pathway for developing
transparent and reliable Artificial Intelligence regularization techniques.
