  Rankings of people and items has been highly used in selection-making,
match-making, and recommendation algorithms that have been deployed on ranging
of platforms from employment websites to searching tools. The ranking position
of a candidate affects the amount of opportunities received by the ranked
candidate. It has been observed in several works that the ranking of candidates
based on their score can be biased for candidates belonging to the minority
community. In recent works, the fairness-aware representative ranking was
proposed for computing fairness-aware re-ranking of results. The proposed
algorithm achieves the desired distribution of top-ranked results with respect
to one or more protected attributes. In this work, we highlight the bias in
fairness-aware representative ranking for an individual as well as for a group
if the group is sub-active on the platform. We define individual unfairness and
group unfairness and propose methods to generate ideal individual and group
fair representative ranking if the universal representation ratio is known or
unknown. The simulation results show the quantified analysis of fairness in the
proposed solutions. The paper is concluded with open challenges and further
directions.
