I'm no fan of the A.I. hype of today, but I can comment that deep learning actually makes the "contentious decision" easier to detect. With the "old way", i.e.

I'm no fan of the A.I. hype of today, but I can comment that deep learning actually makes the "contentious decision" easier to detect. With the "old way", i.e. jam some features into an SVM, there was no accountability / confidence. At least I've never seen a reliable SVM-based confidence score. But, for example, confidence scores are actually a byproduct of running a modern CNN for image classification. This is very useful for "finding mistakes" (assuming you have 100% accurate training data, and other prerequisites). It even helps fixing issues in the training data, we use it all the time. That being said, the big dirty secret that nobody's pointing out is: there has been no real "theory-level" advancement in AI in the last 20 years that have borne fruit (at least in computer vision and neural nets). The models are just bigger (and much better) with some slight tweaks (dropout, etc). It's hardware that's made that possible. Academics have done a thorough and wonderful search of the model space and have some great finds. But I think we still need a few solid theoretical leaps before the singularity.