> statistically defensible stereotypes. "defensible" is a weasel word. It goes back to the old "correlation vs causation" Machine learning choose the "shape"

> statistically defensible stereotypes. "defensible" is a weasel word. It goes back to the old "correlation vs causation" Machine learning choose the "shape" of a decision surface. Optimizing parameters of that surface may be defensible, or may nor be. Your algorithm may learn that people with my skin color, or who live in my neighborhood, on have a certain average behavior on your objective function. I would word your #1 and #2 as (1) baseless prejudice that midjudges people in general, (2) statistical prejudice that misjudges many individuals. But it's still a leap to treat me as the "average" in that group, instead of judging me on the content of my character. What guided your choice of parameters? Even if you did the best within practical limits, it still may be unfair.