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2019 | OriginalPaper | Chapter

Fairness-Enhancing Interventions in Stream Classification

Authors : Vasileios Iosifidis, Thi Ngoc Han Tran, Eirini Ntoutsi

Published in: Database and Expert Systems Applications

Publisher: Springer International Publishing

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Abstract

The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to “fix” a (fair) model over the data stream as changes in the data might incur changes in the underlying model therefore, affecting its fairness. In this work, we propose fairness-enhancing interventions that modify the input data so that the outcome of any stream classifier applied to that data will be fair. Experiments on real and synthetic data show that our approach achieves good predictive performance and low discrimination scores over the course of the stream.

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Title: Fairness-Enhancing Interventions in Stream Classification
Authors: Vasileios Iosifidis
Thi Ngoc Han Tran
Eirini Ntoutsi
Publisher: Springer International Publishing
Book: Database and Expert Systems Applications
Print ISBN: 978-3-030-27614-0

Electronic ISBN: 978-3-030-27615-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-27615-7_20

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