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

By Alexandra Carter. 2018

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification (2017, background): “(2) Treatment on the basis of inadequately justified factors, not including membership in the protected class, that disadvantages members of the protected class (we’ll call this ‘disparate impact’)”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “…data are increasingly used to drive policies, to shape products and services, and for automated decision making, it is critical to gain a better understanding of the limitations around the use of various social datasets and of the eﬀorts to address them (boyd and Crawford, 2012; O’Neil, 2016).”
50 Years of Test (Un)fairness: Lessons for Machine Learning (2018, background): “The rise of interest in fairness today has corresponded with public interest in the use of machine learning in criminal sentencing and predictive policing, including discussions around compas [16, 20, 42] and PredPol [25, 49].”
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, neutral): “While these recent innovations have led to some societal benefits, they have also demonstrated their potential to be abused or misused in ways their designers could not have imagined (O’Neil 2016).”
Gender Recognition or Gender Reductionism?: The Social Implications of Embedded Gender Recognition Systems (2018, background): “Algorithmic Fairness, Human Autonomy and Automatic Recognition Systems Previous research has identified the possibility of unintentionally replicating human biases in automatic systems [23,36].”
Working and organizing in the age of the learning algorithm (2018, background): “They are worth billions of dollars and are tightly protected as intellectual property (O’Neil 2017).”
Anxiety, panic and self-optimization (2018, background): “However, algorithms increasingly learn and self-sharpen, often causing unintended side effects and amplifying discrimination, refracting and sharpening classed and gendered bias (O’Neil, 2016).”
The Forgotten Margins of AI Ethics (2022, background): “These have been brought forth by landmark studies [6, 13, 60] and foundational books [17, 22, 25, 29, 58, 61, 62, 86].”
Entanglement HCI The Next Wave? (2019, unknown): “…collection and storage of data combined with ever more sophisticated machine learning algorithms has opened a pandora’s box that looked like benign advertising and turned into a mass manipulation tool that fundamentally undermines democratic institutions and principles of equality [O’Neil 2016].”
Predictably Unequal? The Effects of Machine Learning on Credit Markets (2020, unknown): “(2014), Varian (2014), Mullainathan and Spiess (2017), Chernozhukov et al.”
Fair, Transparent, and Accountable Algorithmic Decision-making Processes (2017, background): “…a nominee for the National Book Award, Cathy O’Neil’s book, “Weapons of Math Destruction”, details several case studies on harms and risks to public accountability asso-Fair ciated with big data-driven algorithmic decision-making, particularly in the areas of criminal justice and education [45].”
Algorithmic bias? An empirical study into apparent gender-based discrimination in the display of STEM career ads (2019, unknown): “It is popular to suppose that such outcomes occur either because those who program the algorithm intend to discriminate or have unconscious biases, or because the algorithm itself will learn to be biased on the basis of the behavioral data that feeds it (O’Neil, 2016).”