Black men commit significantly more felonies than Jews, so removing the bias basically means making the model more stupid.
But how about you and I play? Who do you, nurumaik, think is more likely to be guilty? And what rational did you use, and evaluate to make that determination?
The problem you propose is that because the word likely appears, it's ok to use an invalid or inaccurate conclusion. Here it's the equivalent to saying.
all men can fly, Socrates can fly, is it likely that Socrates is a man?
It doesn't matter what context you use to ask the question. No, there's no reason to say Socrates is a man. All birds can fly, so Socrates must be a bird and a man, right?
all pigs can fly, and all bears... thus I have proven it's more likely that Socrates is a man-bear-bird-pig!
A model that makes a prediction based on applying data it wasn't presented with isn't smarter. It's overfit.
Is a model smarter if it's more prone to hallucinating? Given if you point enough examples at it eventually it'll guess right?
edit: bonus point, even if you refuse to agree, it'd be an overfit example. A smarter AI would understand the societal implications to both individuals, and trust in the legal system as a whole, and refuse to profile, and make assumptions based on racial identity. You might want to claim, you're asking about probabilities, and using historical data is valid. But then you'd have to explain why data points like "the defendant is black, and black people commit more crimes" would be inadmissible in any reasonable court?
Second, if I'm generous and assume you meant "statistically higher as a percentage considering their population size" (which is true), we're talking about a likelihood that's so low that even a doubling of the confidence is too small to rank as "probable".
The most likely answer is that neither are guilty.