NEXYTE product description Page 9 6.2 Explainable ML Models Machine Learning explainability is about understanding and interpreting the decisions made by machine learning models. It builds trust and transparency by uncovering the factors that influence predictions. This helps experts assess reliability, identify biases, and make informed decisions. ML explainability ensures responsible and ethical use of AI. To ensure transparency, ML-generated insights and enrichments in NEXYTE are accompanied by explanatory information which provides users with comprehensive understanding of the models’ behavior. For example, some ML-generated predictions are provided with decision trees that visualize the model’s decision flow when generating them. Alternatively, NEXYTE can present the impact and magnitude that each feature had on a model’s prediction. Visualization of a model's decision flow Explanation of which features in a risk scoring model led to a high score