FairER: Entity Resolution With Fairness Constraints

Vasilis Efthymiou , Kostas Stefanidis , Evaggelia Pitoura , Vassilis Christophides (1, 2)

1 ENSEA - Ecole Nationale Supérieure de l'Electronique et de ses Applications
2 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes

There is an urgent call to detect and prevent "biased data" at the earliest possible stage of the data pipelines used to build automated decision-making systems. In this paper, we are focusing on controlling the data bias in entity resolution (ER) tasks aiming to discover and unify records/descriptions from different data sources that refer to the same real-world entity. We formally define the ER problem with fairness constraints ensuring that all groups of entities have similar chances to be resolved. Then, we introduce FairER, a greedy algorithm for solving this problem for fairness criteria based on equal matching decisions. Our experiments show that FairER achieves similar or higher accuracy against two baseline methods over 7 datasets, while guaranteeing minimal bias.

Domaines: Intelligence artificielle [cs.AI]

Vasilis Efthymiou, Kostas Stefanidis, Evaggelia Pitoura, Vassilis Christophides. FairER: Entity Resolution With Fairness Constraints. 30th ACM International Conference on Information & Knowledge Management (CIKM '21), Nov 2021, New York, France. ⟨10.1145/3459637.3482105⟩. ⟨hal-03608623⟩