Potential for Discrimination in Online Targeted Advertising

Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Nunes Ribeiro, George Arvanitakis, Fabrício Benevenuto, Krishna P. Gummadi, Patrick Loiseau, Alan Mislove
Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR 81:5-19, 2018.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v81-speicher18a, title = {Potential for Discrimination in Online Targeted Advertising}, author = {Speicher, Till and Ali, Muhammad and Venkatadri, Giridhari and Ribeiro, Filipe Nunes and Arvanitakis, George and Benevenuto, Fabrício and Gummadi, Krishna P. and Loiseau, Patrick and Mislove, Alan}, booktitle = {Proceedings of the 1st Conference on Fairness, Accountability and Transparency}, pages = {5--19}, year = {2018}, editor = {Friedler, Sorelle A. and Wilson, Christo}, volume = {81}, series = {Proceedings of Machine Learning Research}, month = {23--24 Feb}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v81/speicher18a/speicher18a.pdf}, url = {https://proceedings.mlr.press/v81/speicher18a.html}, 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.} }
Endnote
%0 Conference Paper %T Potential for Discrimination in Online Targeted Advertising %A Till Speicher %A Muhammad Ali %A Giridhari Venkatadri %A Filipe Nunes Ribeiro %A George Arvanitakis %A Fabrício Benevenuto %A Krishna P. Gummadi %A Patrick Loiseau %A Alan Mislove %B Proceedings of the 1st Conference on Fairness, Accountability and Transparency %C Proceedings of Machine Learning Research %D 2018 %E Sorelle A. Friedler %E Christo Wilson %F pmlr-v81-speicher18a %I PMLR %P 5--19 %U https://proceedings.mlr.press/v81/speicher18a.html %V 81 %X 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.
APA
Speicher, T., Ali, M., Venkatadri, G., Ribeiro, F.N., Arvanitakis, G., Benevenuto, F., Gummadi, K.P., Loiseau, P. & Mislove, A.. (2018). Potential for Discrimination in Online Targeted Advertising. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, in Proceedings of Machine Learning Research 81:5-19 Available from https://proceedings.mlr.press/v81/speicher18a.html.

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