Rank aggregation combines multiple ranked lists into a consensus ranking. In fields like
biomedical data sharing, rankings may be distributed and require privacy. This motivates the …

Minimizing social bias strengthens societal bonds, promoting shared understanding and
better decision-making. We revisit the definition of bias by discovering new bias types (eg …

For the problem of aggregating several rankings into one ranking, Kemeny (1959) proposed
two methods: the median rule which selects the ranking with the smallest total swap distance …

Individual fairness requires that similar individuals are treated similarly. It is supposed to
prevent the unfair treatment of individuals on the subgroup level and to overcome the …

There has been great interest in fairness in machine learning, especially in relation to
classification problems. In ranking-related problems, such as in online advertising …

Given a set of voters' preferences expressed as rankings, rank aggregation approaches
combine them into a unique consensus ranking. Even if some methods guarantee fair …

The exponential growth of image collections has demanded a significant increase in the use
of machine learning and image retrieval applications across various scenarios. Despite the …