Race After Technology: Abolitionist Tools for the New Jim Code

By Ruha Benjamin. 2019

438 citations recorded by the source corpus.

Benjamin argues that automation, far from being a sinister story of racist programmers scheming on the dark web, has the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent when compared to the racism of a previous era. Presenting the concept of the “New Jim Code,” she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life.

Subfields: Communication, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “For example, Benjamin (2019) recounts an Apple employee who worked on speech recognition for Siri:”
Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing (2020, background): “The reality of FPT deployments, however, reveals that many of these uses are quite vulnerable to abuse, especially when used for surveillance and coupled with predatory data collection practices that, intentionally or unintentionally, discriminate against marginalized groups [2].”
Understanding and Evaluating Racial Biases in Image Captioning (2021, background): “Racial bias has been identifed in other automated systems [9] (e.”
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “As Benjamin (2019) notes, “whereas in a previous era, the intention to deepen racial inequities was more explicit, today coded inequity is perpetuated precisely because those who design and adopt such tools are not thinking carefully about systemic racism”.”
Towards a critical race methodology in algorithmic fairness (2019, neutral): “Third, and relatedly, as Wendy Chun and Ruha Benjamin have discussed [11, 24], race operates both with and as technology.”
A Survey of Race, Racism, and Anti-Racism in NLP (2021, background): “We quote Benjamin (2019): “[technical systems and social codes] operate within powerful systems of meaning that render some things visible, others invisible, and create a vast array of distortions and dangers.””
Taxonomy of Risks posed by Language Models (2022, background): “Unfair treatment can also emerge from LMs that perform better for some social groups than others [18].”
Critical Race Theory for HCI (2020, background): “They highlight negative representations of racial minorities [3, 89], reveal racist applications of technology [10, 22], celebrate the unique use of technology by speciﬁc racial groups [10, 19, 87], and showcase ways to ﬁght racism [11, 83].”
AI Art and its Impact on Artists (2023, background): “We thus advocate for a computer science education system that stresses the manner in which power interacts with technology [19, 105].”
Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models (2020, background): “…on standard corpora as state-of-the-art is the NER equivalent of developments in photography, which was optimized for perfect exposure of white skin, and which is the assumed technical reason for many failures of computer vision applications when applied to dark skinned people (Benjamin, 2019).”
Algorithmic Bias and Risk Assessments: Lessons from Practice (2022, background): “10 For more on this, see Andrus and Villenueve (2022) and Benjamin (2019). 11 For example, see Watcher et al.”
Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models (2023, background): “For example, if a language model is primarily designed to generate content for a certain demographic or industry, it may inadvertently reinforce existing biases and exclude different perspectives [40, 41].”
The Whiteness of AI (2020, background): “(Benjamin 2019, 28) As a further example, she describes a Black computer scientist who chose a White voice for his app rather than a Black one, so as not to “create friction” (Benjamin 2019, 28–29) .”
Racial and ethnic imbalance in neuroscience reference lists and intersections with gender (2020, background): “Manifestations of structural bias with respect to race and ethnicity include the over-policing and over-incarceration of communities of color (56, 57); in academia, they also include hiring and retention practices in which racial struggles remain unaccounted for while diversity service work (with wh”
Toward situated interventions for algorithmic equity: lessons from the field (2020, background): “Existing scholarship on algorithmic fairness often begins with a recognition of real-world harms [7, 11, 49], yet many contributions to date offer technical solutions that precede application or evaluation of systems in use.”
Critical Perspectives on Governance Mechanisms for AI/ML Systems (2020, background): “in response to this upsurge in AI boosterism (Benjamin, 2019; Eubanks, 2018) but have at times struggled to gain traction with policymakers (Crawford et al., 2019).”
“It’s Complicated”: Negotiating Accessibility and (Mis)Representation in Image Descriptions of Race, Gender, and Disability (2021, background): “given increasing awareness of AI bias, but its history of misuse is characterized by secrecy and evading accountability [10, 80].”