In machine learning often a tradeoff must be made between accuracy and intelligibility. This tradeoff sometimes limits the accuracy of models that can be safely deployed in mission-critical applications such as healthcare and criminal justice where being able to understand, validate, edit, and ultimately trust a learned model is important. In this talk I’ll present a case study where intelligibility is critical to uncover surprising patterns in the data that would have made deploying a black-box model dangerous. I’ll also show how distillation with intelligible models can be used to detect bias inside black-box models.