You wouldn't even go about training a machine learning algorithm that way as it would be pointless. The idea is to let it make better predictions, not train to to make the same predictions as an existing person. Rejected applications are pointless for training as you don't know whether they were a good or bad rejection, whereas if you just give it approved loans and the outcome (i.e., was the loan defaulted on) then the AI can try to develop a set of rules. Typically you feed some large percentage of your data to the algorithm as training data and then use the left over part to test accuracy to see how many times it predicts correctly.
If you truly wanted to avoid racial or gender bias you would just remove that information from what you feed into the algorithm, at which point it can't a priori be biased against anyone because it can't even evaluate them based on those criteria. But let's suppose you do that and then look at the results after the fact, add that data back in and come to the startling conclusion that your AI is disproportionately rejecting candidates from some group. It can't possibly be because it knows they're a member of that group, but because that group happens to have worse outcomes.
If you stop to think about this, its not too hard to come to a reasonable conclusion that if your AI that knows nothing about race is suggesting that black/white/latino borrowers are a higher risk, it's because they're a higher risk. Reality doesn't care about feelings or trying to make sure that outcomes are equal across groups, so we conclude that some group is a worse risk. It probably is the case that black borrowers are more likely to default, but it's not because they're black, but because blacks are typically less well off so of course they're going to default on loans more often. In reality they probably shouldn't (and maybe wouldn't have) received a loan, but some policy designed to make it easier for them to get approval caused it to happen, but that doesn't make them a safer risk, it just lets some people feel better about the world.
If you want to check if your AI is racist find a group of loan applications that are for all intents the same with the only difference being the race of the applicant see if you get a different results based on race for that input set. My guess is that you probably wouldn't. Because if you're stripped out racial data as a category to train on, the algorithm wouldn't suddenly decide to discriminate based on it. Also, for some machine learning algorithms (e.g., anything like a decision tree) you can look at precisely how it evaluates a case, so you could see pretty easily if the AI has a step where race==groupX ? reject : approve becomes pretty apparent. That's not true for all algorithms, but just because its an AI doesn't means its a black box that is beyond all human understanding.