@inproceedings{a5296bd6a4794c2eb10b3dde5735c765,
title = "Equality of Opportunity in Ranking: A Fair-Distributive Model",
abstract = "{\textcopyright} 2021, Springer Nature Switzerland AG.In this work, we define a Fair-Distributive ranking system based on Equality of Opportunity theory and fair division models. The aim is to determine the ranking order of a set of candidates maximizing utility bound to a fairness constraint. Our model extends the notion of protected attributes to a pool of individual{\textquoteright}s circumstances, which determine the membership to a specific type. The contribution of this paper are i) a Fair-Distributive Ranking System based on criteria derived from distributive justice theory and its applications in both economic and social sciences; ii) a class of fairness metrics for ranking systems based on the Equality of Opportunity theory. We test our approach on an hypothetical scenario of a selection university process. A follow up analysis shows that the Fair-Distributive Ranking System preserves an equal exposure level for both minority and majority groups, providing a minimal system utility cost.",
author = "E. Beretta and A. Vetr{\`o} and B. Lepri and {De Martin}, J.C.",
year = "2021",
doi = "10.1007/978-3-030-78818-6_6",
language = "English",
isbn = "9783030788179",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "51--63",
editor = "L. Boratto and S. Faralli and M. Marras and G. Stilo",
booktitle = "Advances in Bias and Fairness in Information Retrieval - 2nd International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Proceedings",
note = "2nd International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021 ; Conference date: 01-04-2021 Through 01-04-2021",
}