Exploring User Perceptions of Discrimination in Online Targeted Advertising

By Angelisa C. Plane, Elissa M. Redmiles, Michelle L. Mazurek, Michael Carl Tschantz. 2017

70 citations recorded by the source corpus.

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Citations in the corpus

How Do Fairness Definitions Fare?: Examining Public Attitudes Towards Algorithmic Definitions of Fairness (2018, background): “…fairness criteria in algorithmic decision making: Pierson (2017) investigated how two different factors influence views on algorithmic fairness, Plane et al. (2017) explored human perceptions of discrimination in targeted online advertising, Grgić-Hlača et al. (2018a) and Grgić-Hlača et al.…”
Racial categories in machine learning (2018, background): “[4, 19]. Related work has been done on Google’s advertising platform. Discriminating ads have been delivered based onracialized search results[51]andgendereduserproﬁles[15].Studiesaboutuserperception [43] and legal liability [13] have explored these issues in depth.Noble[40]arguesthatdigitalmediamon”
Human Decision Making with Machine Assistance (2019, background): “Several studies also explored human perceptions of the fairness of machine decisions, in the context of self-driving cars [5, 12], targeted-advertising [59], loan approval [62], and bail decisions [36–38 Another series of studies has compared human and machine decision-making.”
A Qualitative Exploration of Perceptions of Algorithmic Fairness (2018, background): “Most participants tended to favor and trust human decision-making over algorithmic decision-making (this appears to contrast with Plane et al.’s results [67], which could be due to a variety of factors such as the different populations studied, and bears further investigation).”