I think you’re arguing semantics a bit. What you’re saying checks out, but one could say that overfitting was occurring but the test dataset distribution was not wide enough to catch it.
(Though maybe the term as used in industry is less strict.)
It would be like if your car was driving in circles and you called a mechanic to fix your steering, and they told you that the actual problem was that both right wheels were missing. That's not a steering problem, and no repair to the steering system will fix it. The only fix is to put new wheels on.
When AI makes a decision, right now, people only uses the probability output. Hiring A has .6 probability while hiring B has .4. then we will hire A instead of B. However, if we consider the confidence intervals, the decision might not be that clear. Say +/- .5 to hire A but .2 to hire B. If exploration is considered too, very likely that we will give B a chance.
AI is in the realm of probabilistic decision making, while normal people don't follow. The bias is not from the training side. It's the decision making process incorporating AI should change.