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Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining

Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining Sara Hajian Eurecat Barcelona, Spain Francesco Bonchi ISI Foundation Turin, Italy Carlos Castillo Eurecat Barcelona, Spain sara.hajian@eurecat.org ABSTRACT francesco.bonchi@isi.it 1. chato@acm.org INTRODUCTION Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [5]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [16]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining

Association for Computing Machinery — Aug 13, 2016

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Datasource
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISBN
978-1-4503-4232-2
doi
10.1145/2939672.2945386
Publisher site
See Article on Publisher Site

Abstract

Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining Sara Hajian Eurecat Barcelona, Spain Francesco Bonchi ISI Foundation Turin, Italy Carlos Castillo Eurecat Barcelona, Spain sara.hajian@eurecat.org ABSTRACT francesco.bonchi@isi.it 1. chato@acm.org INTRODUCTION Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [5]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [16]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have

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