Algorithms of Oppression: How Search Engines Reinforce Racism

By Safiya Umoja Noble. 2018

431 citations recorded by the source corpus.

Subfields: Hardware and Architecture, AI Ethics, Fairness & Society

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Citations in the corpus

Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy (2019, background): “The downstream effects range from perpetuating harmful stereotypes [55] to increasing the likelihood of being unfairly suspected of a crime (e.”
Manipulating and Measuring Model Interpretability (2018, background): “culty of understanding how a potentially complex model has made a decision or how this decision can be improved. For example, it has been argued that certain blackbox models are systematically unfair [27, 57, 70], sometimes causing managers to refrain from deploying them [17]. To address this proble”
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020, background): “data and algorithms played a large role [52, 54, 57].”
What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness (2020, background): “From determining which individuals should be detained awaiting trial [38] to choosing which images are retrieved through Internet search [62], algorithmic systems are increasingly tasked with mak-”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, background): “On the other, there are growing concerns about how identity is being represented [18, 80, 150], whether it is fair [58, 108, 134], and what the outcomes are when it is not [14, 122, 157].”
Review of "Algorithms of oppression: how search engines reinforce racism," by Noble, S. U. (2018). New York, New York: NYU Press. (2019, background): “3321392 Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism.”
Between Subjectivity and Imposition (2020, background): “What is (relatively) new is that arbitrary classications are increasingly established and stabilized through automated algorithmic systems[57, 62].”
Habits and the electronic herd: The psychology behind social media’s successes and failures (2020, background): “Paired with evidence of misinformation, algorithmic bias, and wider societal impacts, these have led to increasing proposals of government regulation of social networks (Aral, 2020; Noble, 2018).”
Ethics as Methods: Doing Ethics in the Era of Big Data Research—Introduction (2018, background): “In this and other cases, the ethos, it turns out, disenfranchises already-margin-alized groups (cf. Eubanks, 2018; Noble, 2018).”
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, result): “Some studies support this argument by showing that AI algorithms may be coded biasedly, which can produce racist decisions [45].”
Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse (2019, background): “Moreover, representational or intimate harms are not easily or intuitively remedied, as made clear by Sa ﬁ ya Noble (2018) and her account of Google search ’ s explicit and degrading results for the query ‘ black girls.”
Artificial intelligence and communication: A Human–Machine Communication research agenda (2019, background): “For critical, cultural, and feminist scholars, emerging technologies of AI encapsulate the worldviews and biases of their creators that are enacted within their use (e.g. Broussard, 2018; Noble, 2018).”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, background): “Accordingly, Noble (2018) found that internet search algorithms privilege whiteness and discriminate against African-Americans, particularly against African-American women.”
Big Data from the South(s): Beyond Data Universalism (2019, background): “Various disciplines, at the inter-section of social sciences, the humanities, and informatics, have called attention to the potential inequality, discrimination, and exclusion harbored by the mechanisms of big data (e.g., Eubanks 2018; Gangadharan 2012; Noble 2018).”
Artificial Intelligence and Journalism (2019, background): “Every technological system reflects the conscious and unconscious bias of its makers; AI is no different (Angwin & Larson, 2016; Angwin, Larson, Mattu, & Kirchner, 2016; Barocas & Selbst, 2016; Crawford, 2016, 2017; S. U. Noble, 2018; Zook et al., 2017).”
The ethics of algorithms: key problems and solutions (2020, neutral): “In particular, literature focusing on the ethical risks of racial profiling using algorithmic systems has demonstrated the limits of this approach highlighting, among other things, that long-standing structural inequalities are often deeply embedded in the algorithms’ datasets and are rarely, if eve”
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation (2019, background): “, [34] for a deeper look at these issues and their impact.”