Discrimination in online ad delivery

By Latanya Sweeney. 2013

1,020 citations recorded by the source corpus.

Google ads, black names and white names, racial discrimination, and click advertising.

Subfields: Artificial Intelligence, Marketing, Sociology and Political Science, AI Ethics, Fairness & Society

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Citations in the corpus

Toward Gender-Inclusive Coreference Resolution (2019, background): “, 2015), replicating stereotypes (Sweeney, 2013; Caliskan et al., 2017) (particular those that are cisnormative and/or heteronormative), and quality of service differentials (Buo-”
Exposing and Correcting the Gender Bias in Image Captioning Datasets and Models (2019, methodology): “…makes its way into the models trained on them which has been analysed by various works like [Stock and Cisse, 2018, Beigman and Klebanov, 2009, Sweeney, 2013, Flores et al., 2016, Buolamwini and Gebru, 2018, Bolukbasi et al., 2016], show how the blatant gender bias in datasets is inherited by…”
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings (2016, background): “A number of online systems have been shown to exhibit various biases, such as racial discrimination and gender bias in the ads presented to users [36, 6].”
Semantics derived automatically from language corpora contain human-like biases (2016, background): “Documented cases of automated prejudice range from online advertising (Sweeney, 2013) to criminal sentencing (Angwin et al., 2016).”
On Measuring Social Biases in Sentence Encoders (2019, neutral): “…of the angry black woman stereotype, we create a test whose target concepts are black-identifying and white-identifying female given names from Sweeney (2013, Table 1) and whose attributes are adjectives used in the discussion of the stereo-type in Collins (2004, pp. 87-90) and their antonyms.”
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints (2017, background): “Such issues have been demonstrated in various learning systems, including online advertisement systems (Sweeney, 2013), word embedding models (Bolukbasi et al.”
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods (2018, neutral): “Similar observations have been made in vision and language models (Zhao et al., 2017), online news (Ross and Carter, 2011), web search (Kay et al., 2015) and advertisements (Sweeney, 2013).”
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems (2018, background): “In general, any predictive model built on historical data may inadvertently inherit human biases based on gender, ethnicity, race, or religion (Sweeney, 2013; Datta et al., 2015).”
Learning Gender-Neutral Word Embeddings (2018, background): “Stereotype Analysis Implicit stereotypes have been observed in applications such as online advertising systems (Sweeney, 2013), web search (Kay et al.”
Gender Bias in Contextualized Word Embeddings (2019, background): “Gender bias has been shown to affect several realworld applications relying on automatic language analysis, including online news (Ross and Carter, 2011), advertisements (Sweeney, 2013), abusive language detection (Park et al.”
Demographic Dialectal Variation in Social Media: A Case Study of African-American English (2016, neutral): “3 Some methods exist to associate messages with authors’ races; one possibility is to use birth record statistics to identify African-American-associated names, which has been used in (non-social media) social science studies (Sweeney, 2013; Bertrand and Mullainathan, 2003).”
Inherent Trade-Offs in the Fair Determination of Risk Scores (2016, background): “Second, in a very different domain, researchers have begun to analyze the ways in which different genders and racial groups experience advertising and commercial content on the Internet differently [9, 26].”
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “These techniques are already common in lending [52, 84, 42], hiring [88, 89, 55], and online advertising [119], and increasingly figure into decisions regarding pretrial detention [3, 80, 27], immigration detention [77], child maltreatment screening [122, 34, 13], public health [95, 105], and welfar”
On formalizing fairness in prediction with machine learning (2017, background): “mputing devices to swi›ly replicate decision-making can lead to magnifying erroneous behavior [9]. Barocas and Selbst [6], Citron and Pasquale [12], Edelman and Luca [24], Kay et al. [42] and Sweeney [65] show that algorithms can make discriminatory decisions even if the computing process is well-in”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “The researchers should deliberate over not only the beneﬁts of research, but also over the possible types of harms (Barocas et al., 2017), the aﬀected groups, and how to test for adverse impact (Sweeney, 2013).”
Stable and Fair Classification (2019, background): “, to check gender inequality in recommending high-paying jobs (Farahat & Bailey, 2012; Datta et al., 2015; Sweeney, 2013).”
The Price of Fair PCA: One Extra Dimension (2018, background): “…2015], searches for African American names caused the display of arrest record advertisements with higher frequency than searches for white names [Sweeney, 2013], facial recognition has wildly different accuracy for white men than dark-skinned women [Buolamwini and Gebru, 2018], and recidivism…”