What is the difference between a black-box and a white-box model?

In the context of artificial intelligence and machine learning, a black-box model and a white-box model are two different approaches to building predictive models.

A black-box model is a model where the internal workings of the model are not transparent or easily interpretable. In other words, the model is treated as a "black box," where the input and output are known, but the internal mechanisms or processes that produce the output are unknown or not easily understood. The focus is on the model's accuracy and ability to make accurate predictions rather than understanding how the model works.

In contrast, a white-box model is a model where the internal workings of the model are transparent and easily interpretable. The focus is not just on the accuracy of the model's predictions but also on understanding how the model works and how it arrived at its predictions. The model is designed to provide an explanation of how it arrived at its predictions, making it easier for humans to understand and interpret the results.

For example, a decision tree model is a white-box model, as it is easy to understand how the model works, and how it arrived at its decisions. In contrast, a deep neural network model is a black-box model, as it is difficult to understand how the model works or how it arrived at its predictions.

Overall, the choice of using a black-box or a white-box model depends on the specific application and requirements. While black-box models can achieve high accuracy in some cases, white-box models are often preferred in situations where interpretability and transparency are important.