I understand your concerns, but there is a large body of XAI research that addresses them. For example, there are post-hoc explainability algorithms like LIME (

I understand your concerns, but there is a large body of XAI research that addresses them. For example, there are post-hoc explainability algorithms like LIME (https://github.com/marcotcr/lime https://github.com/marcotcr/lime) that "explain" the classification decisions of machine learning models. The obvious alternative is to use (deep) learning only to mine simpler, fully explainable models (sacrifice correlation power for intelligibility). What I don't think exist yet are approaches that go full scale with the "reasoning backwards" approach. Like a neural network making a BS classification decision and then trying to "reason backwards" in a symbolic way to defend the decision. But works like this one: https://openai.com/blog/debate/ https://openai.com/blog/debate/ go roughly into this direction (the work seemed to be, at the time of writing, in its infancy).