One of the challenges associated with using artificial intelligence (AI) models is the negative correlation between the accuracy of the models and their ability to explain the generated results (explainability) intrinsically. This characteristic has led these models to be considered black boxes, meaning that the way the model generates the results cannot be explained in a way that is easily understandable by humans. The figure below illustrates this trade-off, where the higher the accuracy of the results, the less we know about how to explain these results in human-understandable terms.