On Algorithmic decision making
No matter how much data we collect, two people who look the same to the algorithm can always end up making different choices.
Two definitions of fairness:
- keep the error rates comparable between groups, and
- treat people with the same risk scores in the same way.
Both of these definitions are totally defensible! But satisfying both at the same time is impossible.