Show some child a cat and ask why it's not a crocodile. They will be able to explain it through the differences in shape, color, behavior and other features of those animals. Whether you consider it post-hoc is unimportant. The explanation is still real and relevant.
So people can explain their deliberate reasoning. Also, reason about reasoning. If they truly can't, it's called gut feeling and no one rational trusts those for solving complex problems with important outcomes. Especially at scale.
Neural networks, at least right now, have no capabilities of this sort. There are some attempts at visualization, but they are inherently limited, because they must make assumptions about the domain of the problem.
The examples I am more worried about are not knowledge based (or judgement based). The ones I am worried about are the stories we tell about the _actions_ we take, why we choose A instead of B. Answering these requires us to introspect our own behavior and that is exceptionally difficult. Take for example a question like "how did you know it was safe to cross the street?" Many people will tell you that they 'looked both ways and saw nothing was coming' but if you have them wear earplugs they will get run over because it was really the fact that they were listening for the cars. Or say you meet someone and you decide you don't like them, you can't really go back an undo that, but it might actually have been the case that you didn't like the perfume they were wearing because it was the same one that your ex used to wear, and it had nothing to do with the person themselves. Virtually no one can catch all the cases like this because we are trapped by our own brains and our own blind spots. Buying your brain's bullshit without attempting some independent verification (eg by asking someone for another perspective or doing little experiments on yourself) is a exceptionally common, and difficult problem to overcome.
You may be able to explain why a cat is different than a dog, but your brain doesn't go through that categorization process before recognizing a cat.
Take for example persons with androgynous features and the difficulty some people have with identifying them. Children are particularly useful for this insight since they are unabashedly curious and inquisitive, and it's not uncommon to hear a child blurt out "is that a boy or a girl?"; it would seem to me that their categorization process, even if not refined, gets goofed by androgyny; something as simple as hair length or facial hair greatly influences a child's ability to easily discern if persno is male or female.
At some level there is a categorization process that seems to happen with humans when it comes to recognition, we just assign high confidence to certain factors. In the case of children, it seems to be they look at person and use common factors like hair length, facial hair, and body shape to determine gender. The sound of one's voice also helps, but this usually isn't something we can pick up as easily or from afar in public places. As an adult, we probably have more refined points, but they are essentially the same as a child's point of view. Instead of "body shape" adults look for specific facial features, size of breasts, walking gait, style of clothing, hair style, etc. These are all just granular differences of how a child understands it.
This gets into personal experience, but when I was in college, I had long hair and really fine skin/facial features. All the time I would hear little kids ask "why does he look like a girl?" or "why does that girl have a beard?". I had no issue with their confusion, they were just curious kids. Something confused their understanding, and once it was explained "sometimes men have long hair", they just sort of accepted it.
We've added instrumentation to our recognition capabilities in order to socialize and gossip about derived categories through language. In addition, we (can sometimes) use the formalized expressions of these categories prepared for socialization to ensure internal consistency.
Artificial neural networks that did something similar could have a number of major philosophical and practical advantages: - enable decentralization of data sets - grow with humans rather than apart from them - enable identifying problematic internal inconsistencies
Sometimes I "know" a design is bad. Explaining why to others requires stepping through a process that May or may not be the one my brain used to make that approach "feel" wrong.
One example of this is Anton-Babinski syndrome [1]. The patient can't see but is completely sure he can.
[1] https://en.wikipedia.org/wiki/Anton%E2%80%93Babinski_syndrom...
A super casual modern day example would be an assertion like "Joe must be gay". You never know until you ask him, and that is if he's honest with you. But everyday-life is full of gross approximations based on bias and partial information.
But the key, even in Joe's case, is you have to go back to the real source. If all we can do is talk about Joe, we can never know for sure. Much like celebrities we are so certain are gay, but haven't come out openly yet. So as long as computers can't leave their data centers, there really isn't any amount of data that can prove anything to them. They can't experience the truth, because ultimately the truth rests on experience and experience of evidence.