The models TIM generates are not black-box models. The features TIM identified in a model can be provided to the user for further inspection. TIM also extracts canonical features from a Model Zoo to provide an overview of the driving features of the dataset at hand. This white-box view of the models can enable the user to gain important insights about the data and its structure. The treemap shown below is an example of how TIM can demonstrate the importance of the different identified features.
In many industries, it is legally obligated to only base decisions on the output of explainable models. In other cases, explainability is preferred simple due to the possibilities it creates in terms of gaining new insights. TIM is designed to provide the benefits of white-box models while minimizing any loss in performance. In many situations, TIM performs better than black-box alternatives.