Discrimination in Online Advertising: A Multidisciplinary Inquiry

By Amit Datta, Anupam Datta, Jael Makagon, Deirdre K. Mulligan, Michael Carl Tschantz. 2018

58 citations recorded by the source corpus.

Subfields: AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In AI Ethics, Fairness & Society

1. Sharad Goel
2. Jamie H. Morgenstern
3. Dan Jurafsky
4. Sebastian Benthall
5. Bruce D. Haynes
6. Abeba Birhane

Citations in the corpus

• Racial disparities in automated speech recognition (2020, background): “There is worry, however, that speech recognition systems suffer from racial bias (1–4), a problem that has recently come to light in several other advancing applications of machine learning, such as face recognition (5, 6), natural language processing (7–11), online advertising (12, 13), and risk pr”
• Racial categories in machine learning (2018, background): “Studies about user perception [43] and legal liability [13] have explored these issues in depth.”
• Predictive Inequity in Object Detection (2019, background): “Advertising systems show ads based upon numerous demographic features; as a result, a number of findings have shown certain gender or racial groups receive certain ads at much higher rates than others (Zhao et al., 2017; Datta et al., 2018; Sweeney, 2013).”