SHAP, which stands for SHapley Additive exPlanations, is a powerful method used in machine learning to explain individual predictions. Developed by Lundberg and Lee in 2017, SHAP leverages concepts from cooperative game theory, specifically Shapley values, to attribute the contribution of each feature to a model’s output. This approach not only enhances the interpretability of complex models, such as neural networks and gradient boosting, but also provides insights into the decision-making process of these models. By understanding SHAP values, practitioners can demystify black-box models and ensure greater accountability in AI applications.