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Title: Potential for discrimination in online targeted advertising.
Authors: Speicher, Till
Ali, Muhammad
Venkatadri, Giridhari
Ribeiro, Filipe Nunes
Arvanitakis, George
Souza, Fabrício Benevenuto de
Gummadi, Krishna P.
Loiseau, Patrick
Mislove, Alan
Keywords: Facebook
Issue Date: 2018
Citation: SPEICHER, T. et al. Potential for discrimination in online targeted advertising. Journal of Machine Learning Research, v. 81, p. 5-19, 2018. Disponível em: <>. Acesso em: 15 fev. 2019.
Abstract: Recently, online targeted advertising platforms like Facebook have been criticized for allowing advertisers to discriminate against users belonging to sensitive groups, i.e., to exclude users belonging to a certain race or gender from receiving their ads. Such criticisms have led, for instance, Facebook to disallow the use of attributes such as ethnic affinity from being used by advertisers when targeting ads related to housing or employment or financial services. In this paper, we show that such measures are far from sufficient and that the problem of discrimination in targeted advertising is much more pernicious. We argue that discrimination measures should be based on the targeted population and not on the attributes used for targeting. We systematically investigate the different targeting methods offered by Facebook for their ability to enable discriminatory advertising. We show that a malicious advertiser can create highly discriminatory ads without using sensitive attributes. Our findings call for exploring fundamentally new methods for mitigating discrimination in online targeted advertising.
ISSN: 26403498
Appears in Collections:DECSI - Artigos publicados em periódicos

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