FairER: Entity Resolution with Fairness Constraints
Efthymiou, Vasilis; Stefanidis, Kostas; Pitoura, Evaggelia; Christophides, Vassilis (2021-10-26)
Efthymiou, Vasilis
Stefanidis, Kostas
Pitoura, Evaggelia
Christophides, Vassilis
26.10.2021
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202210267881
https://urn.fi/URN:NBN:fi:tuni-202210267881
Kuvaus
Peer reviewed
Tiivistelmä
<p>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.</p>
Kokoelmat
- TUNICRIS-julkaisut [20517]