I know correlation != causation, but we're using imprecise language where these mathematical distinctions are mired and muddled. People don't think like statist

I know correlation != causation, but we're using imprecise language where these mathematical distinctions are mired and muddled. People don't think like statisticians, they think like humans who label things and create biases based on these averages without looking at the individuals. If we build these intricate models for predictions that are statistically minded, are we improving our predictive ability or are we just regurgitating the same flawed biases that we ourselves hold? (When I say flawed, I mean unfair.) What if blue agents were the victims of institutional racism for hundreds of years? Obviously, their lack of opportunities will have hampered their abilities to earn high credit scores and obtain legitimate forms of income. What then? It's as if were building models that measure agents who exist in two separate contexts predicting which one belongs to the 'socially acceptable' context, aka, which one belongs to the class of agents who were the perpetrators of social injustice for centuries. Is that really what we want to predict? No, we're trying to predict how trustworthy an individual is. This might mean that our standard techniques need reassessment if we're going to build models that affect the lives of future generations.