Algorithms of Oppression: How Search Engines Reinforce Racism

By Safiya Umoja Noble. 2018

815 citations recorded by the source corpus.

Subfields: Hardware and Architecture, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Hardware and Architecture

1. Stefano Puntoni
2. Rebecca Walker Reczek
3. M. Giesler
4. Simona Botti

In Safety, Robustness & Interpretability

1. Stefano Puntoni
2. Rebecca Walker Reczek
3. M. Giesler
4. Simona Botti

Citations in the corpus

• Diagnosing Gender Bias in Image Recognition Systems (2020, neutral): “Because recent research suggests that GCV results may depend upon race and skin tone (Noble 2018), we control for race (coded as white or nonwhite).”
• Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, neutral): “…exposes concerns of bias and injustice in algorithmic systems (Angwin et al. 2016a; Benjamin 2019; Buolamwini and Gebru 2018; Lum and Isaac 2016; Noble 2018; Eubanks 2018), exploitative or extractive data practices (Gray and Suri 2019), and applications of AI that dispossess the identity and…”
• On the genealogy of machine learning datasets: A critical history of ImageNet (2021, neutral): “Yet, there is a growing abundance of examples of how these systems are failing people of color (Noble, 2018; Benjamin, 2019), women (Bolukbasi et al., 2016), LGBT+ communities (Scheuerman et al., 2019), people with disabilities (Hutchinson et al., 2020; Trewin, 2018), and the working class and…”
• Artificial Intelligence and Journalism (2019, neutral): “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).”
• When data is capital: Datafication, accumulation, and extraction (2019, neutral): “…critical studies of information technology; some relevant, recent books include: Digital Sociologies (Daniels et al. 2016), The Intersectional Internet (Noble and Tynes, 2016), Programmed Inequality (Hicks, 2017), Algorithms of Oppression (Noble, 2018), and Automating Inequality (Eubanks, 2018).”
• Big Data from the South(s): Beyond Data Universalism (2019, neutral): “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).”
• Consumers and Artificial Intelligence: An Experiential Perspective (2020, positive): “However, because “algorithms that rank and prioritize for profits compromise our ability to engage with complicated ideas” (Noble 2018, p. 118), the resulting AI experience may not only reduce the complex experiences of targeted marginalized populations to a set of more simplified sociodemographic…”
• The decolonial turn in data and technology research: what is at stake and where is it heading? (2021, neutral): “Two writers, Sa fi ya Noble (2018) and Ruha Benjamin (2019), have had enormous impact by focusing on the workings of algorithmic power and code.”
• Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse (2019, neutral): “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, neutral): “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).”
• Critical Perspectives on Governance Mechanisms for AI/ML Systems (2020, neutral): “Scholars such as Noble (2018), Eubanks (2018), Broussard (2018), and Benjamin (2019) have documented and analyzed the failures of digital tools to properly account for race, gender, sexuality, and other facets of human diversity.”