Truly Explainable AI: Putting the “Cause” in “Because”

There are two serious problems with state-of-the-art machine learning approaches.

One is that the most powerful models are too complicated for anyone to comprehend or explain. For instance, a deep neural network is highly flexible — it can learn very intricate patterns — but it is essentially a “black box” that no one can look inside of. Conversely, more transparent models, like linear regression, are typically too restrictive to be useful.