I'm pretty fascinated by all of this, although have barely dipped my toes in. IMHO - "Fairness" is a technical term-of-art meaning "the outcome _should not_ be

I'm pretty fascinated by all of this, although have barely dipped my toes in. IMHO - "Fairness" is a technical term-of-art meaning "the outcome _should not_ be effected by inputs X, Y and Z", and the collected science around making a system behave that way. It closely but not quite matches the colloquial meaning, ie "that's not fair!" - kinda like how "a fair coin" has a specific meaning that mostly tracks with how people use the word, but not quite, and with a lot more specificity. It's typically applied when you want to correct for real-world "unfair biases" in the training data; which in practical application is typically race, gender and the other legally protected categories - but AFAIK is just whichever inputs you decide you want to not have an impact on the outcome. AFAIK what you get out of the AI/ML "fairness" science is a way to measure, and correct for, dependency on the inputs that you (exterior to the system) have decided that you want to _not_ impact the outcome.