TY - GEN
T1 - Equality of Opportunity in Ranking
T2 - 2nd International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021
AU - Beretta, E.
AU - Vetrò, A.
AU - Lepri, B.
AU - De Martin, J.C.
N1 - © 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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’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.
AB - 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’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.
UR - https://www.scopus.com/pages/publications/85111454401
UR - https://www.scopus.com/pages/publications/85111454401#tab=citedBy
U2 - 10.1007/978-3-030-78818-6_6
DO - 10.1007/978-3-030-78818-6_6
M3 - Conference contribution
SN - 9783030788179
T3 - Communications in Computer and Information Science
SP - 51
EP - 63
BT - Advances in Bias and Fairness in Information Retrieval
A2 - Boratto, Ludovico
A2 - Faralli, Stefano
A2 - Marras, Mirko
A2 - Stilo, Giovanni
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 April 2021 through 1 April 2021
ER -