  University rankings are increasingly adopted for academic comparison and
success quantification, even to establish performance-based criteria for
funding assignment. However, rankings are not neutral tools, and their use
frequently overlooks disparities in the starting conditions of institutions. In
this research, we detect and measure structural biases that affect in
inhomogeneous ways the ranking outcomes of universities from diversified
territorial and educational contexts. Moreover, we develop a fairer rating
system based on a fully data-driven debiasing strategy that returns an
equity-oriented redefinition of the achieved scores. The key idea consists in
partitioning universities in similarity groups, determined from multifaceted
data using complex network analysis, and referring the performance of each
institution to an expectation based on its peers. Significant evidence of
territorial biases emerges for official rankings concerning both the OECD and
Italian university systems, hence debiasing provides relevant insights
suggesting the design of fairer strategies for performance-based funding
allocations.
