Potential for discrimination in online targeted advertising.
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Date
2018
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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.
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Keywords
Facebook
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: <http://proceedings.mlr.press/v81/speicher18a/speicher18a.pdf>. Acesso em: 15 fev. 2019.