O’Rourke says that explainable ML uses a black box model and explains it afterwards, whereas interpretable ML uses models that are no black boxes.
Christoph Molnar says interpretable ML refers to the degree to which a human can understand the cause of a decision (of a model). He then uses interpretable ML and explainable ML interchangably.
Wikipedia says on the topic "Explainable artificial intelligence" that it refers to AI methods and techniques such that the results of the solution can be understood by human experts. It contrasts with the concept of the black box in machine learning where even their designers cannot explain why the AI arrived at a specific decision. The technical challenge of explaining AI decisions is sometimes known as the interpretability problem.
Doshi-Velez and Kim say that that interpretable machine learning systems provide explanation for their outputs.
Obviously, there are a lot of definitions but they do not totally agree. Ultimatively, what should be explained: The results of the model, the model itself or how the model makes decissions? And what is the difference between interpret and explain?