NEXYTE product description Page 10 Explanation of threat scoring outcome By being able to review ML models’ explainable data, as well as user rankings, NEXYTE users are capable of making better decisions of how and whether to use these models. 6.3 Vetting of Results Additionally, NEXYTE users can use voting mechanisms (e.g. thumbs up/down) to review and rank a model’s outcomes or predictions. Reviewing the average ranking can assist them to understand the reliability and accuracy of a model. In addition, the organization’s data scientists can decide based on rankings whether a model need to be fine-tuned or trained on additional data. This capability helps to guard against negative outcomes and biases.