I'll point out that broad categories of fairness have nothing to do with AI/ML and everything to do with institutions ensuring fair outcomes. For instance, we

I'll point out that broad categories of fairness have nothing to do with AI/ML and everything to do with institutions ensuring fair outcomes. For instance, we would be appalled if a court found that the conviction rate for people like the defendant was extremely low, so the trial was going to start with the presumption of 80% guilt. What's the problem? It's not one fixed by better modelling or a better bias percentage. The problem is that fairness in many contexts is about processes and institutions. We expect courts to follow principles like due process and equal protection under the law. Companies like Google can't jump right to social science and social engineering without thinking about fairness and justice in these terms. Can a user confirm that the data about them is accurate? If not, what can they do about it? If a business has its account algorithmically cancelled, what is the appeals process? How is it fair? When companies like Google start getting into social science, the problem is extremely complicated fast. For example, it starts looking like a judicial system in parts, but one that isn't established and maintained by a legitimate government.