There are different ways of defining fairness . One major distinction is between group fairness and individual fairness. The guidance material of the AI Fairness 360 Toolkit explains group fairness as such: Group fairness, in its broadest sense, partitions a population into groups defined by protected attributes and seeks for some statistical measure to be equal across groups. The Fairlearn toolkit takes a group fairness approach and explains it as follows: Read more...

Whereas group fairness aims for some form of parity between groups characterized by some protected attribute, individual fairness is instead aimed at treating similar individuals similarly.