Potential for Discrimination in Online Targeted Advertising

By Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Nunes Ribeiro, G. Arvanitakis, Fabrício Benevenuto, Krishna P. Gummadi, P. Loiseau, Alan Mislove. 2018

199 citations recorded by the source corpus.

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Citations in the corpus

Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms (2019, methodology): “Due to the sparse data issue and the convention in collecting features by socialplatforms [44], we binarize each attribute’s domain values into two classes to reduce the domain sizes.”
Racial categories in machine learning (2018, background): “[50] have investigated Facebook’s advertising platforms and discovered that even without the feature, there are ways to use the platform to discriminate intentionally and unintentionally, and propose that discrimination should be measured by its effects, or its “disparate impact” [4, 19].”
Socio-Technological Challenges and Opportunities: Paths Forward (2021, background): “…AI training and use cases at-scale [Simons and Jones, 2012, Abelson et al., 2015, Gervais et al., 2016, Ahmed et al., 2017, Whittaker et al., 2018, Speicher et al., 2018, Chouldechova et al., 2018, Ekstrand et al., 2018, Selbst et al., 2019, Ali et al., 2019, Babaei et al., 2019, Crawford et al.,…”
One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision (2021, background): “Minorities in the United States are already the target of harmful advertising [9], and the potential for discrimination in online advertising [44] has been noted.”
Investigating sources of PII used in Facebook’s targeted advertising (2018, background): “[35] demonstrated that the measures taken by Facebook against discrimination (i.”