Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy

By Tony Doyle. 2017

3,197 citations recorded by the source corpus.

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

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Citations in the corpus

Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, neutral): “…et al. 2019; Garimella et al. 2019; Gaut et al. 2020; Habash et al. 2019; Hashempour 2019; Hoyle et al. 2019; Lee et al. 2019a; Lepp 2019; Qian 2019; Qian et al. 2019; Shariﬁrad et al. 2019; Shariﬁrad and Matwin 2019; Stanovsky et al. 2019; O’Neil 2016; Blodgett et al. 2020; Nangia et al. 2020).”
Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions (2018, background): “Fairness in ML has been explored in popular books [101, 37] and a White House report [99], surveyed in technical review papers [13, 41, 19], tutorials [9, 94, 92, 23], and an in-progress textbook [10], and inspired a number of software packages [129, 39, 45].”
FairSight: Visual Analytics for Fairness in Decision Making (2019, background): “It has been pointed out that algorithmic decision making from data with inherent biases are just nothing but a systematic way of disseminating such biases to large number of people at once [30].”
Do Not Trust Additive Explanations (2019, background): “Models that make unexplainable predictions may be harmful (O’Neil, 2016).”
Large image datasets: A pyrrhic win for computer vision? (2020, background): “Image classiﬁcation and labelling practices, without the necessary precautions and awareness of these problematic histories, pick up these stereotypes and prejudices and perpetuate them [31, 58, 59].”
'It's Reducing a Human Being to a Percentage': Perceptions of Justice in Algorithmic Decisions (2018, background): “3 After consulting relevant literature on how ML systems are being applied in each context (e.g. [43, 38, 43, 55, 72]), we settled on the following contexts: 1.”
Disembodied Machine Learning: On the Illusion of Objectivity in NLP (2021, neutral): “ML models were initially hailed as objective, unimpeded by subjective human biases, and by extension by social marginalisation (O’Neil 2016).”
Algorithms in practice: Comparing web journalism and criminal justice (2017, background): “They argue that algorithmic techniques are not necessarily neutral, raising questions about biases in the training data and the disparate impact that algorithms can have on protected groups (Barocas and Selbst, 2016; O’Neil, 2016).”
Fairness in Machine Learning: A Survey (2020, neutral): “Machine Learning (ML) technologies solve challenging problems that often have high social impact, such as examining re-offence rates (e.g., References [11, 25, 27, 44, 100, 224]), automating chat and (tech) support, and screening job applications (see References [241, 285]).”