The TREC 2020 Fair Ranking Track focuses on the evaluation of retrieval systems according to how well they fairly rank academic papers. The evaluation metric considered estimates how much the ranked papers are relevant and fairly represent different groups of authors, groups unknown to the track’s participants. In this paper, we present the three different solutions proposed by our team to the given problem. The first solution is built on a learning-to-rank model to predict how much the documents are relevant for a given query and modify the ranking based on this relevance and a randomization strategy. The second approach is also based on the relevance predicted by a learning-to-rank model, but it additionally selects the authors using categories defined by analyzing collaborations between authors. The third approach uses the DELTR framework, and it considers different categories of authors based on the corresponding H-class. The results show that the first approach gives the best performance, with the additional advantage that it does not require extra information about the authors.