It is crucial that the decision-making algorithms are fair, that they do not systematically transmit or amplify the already existing biases in today’s society. Researchers in the field of fairness algorithms have provided various possible ranking algorithms. We cite a few examples here.
Zehlike et al provided an algorithmic solution to the Fair Top-k Ranking problem over a single binary type attribute. With respect to the group fairness criteria, they provided a greedy algorithm which selects the best k candidates from a large pool while ensuring maximal utility.
Asudeh et al. have focused on designing fair scoring functions by assigning a weighted sum of numeric attribute values to each item.
Singh and Joachims have proposed a general framework of fairness constraints that employs probabilistic rankings and linear programming, allowing the computation of the utility- maximising ranking.
Celis et al. provided a linear time approximation algorithm of the ranking problem in ethical data processing.
The above described algorithms can be found in following literature: