Algorithms of Oppression
By K. MacLeod. 2021
530 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
- Kathryn Henne
- James Y. Zou
- Andrew Smart
- P. Nicholas
- Gurleen Virk
- Jess Gallegos
- Emilio Garcia
- Negar Rostamzadeh
In Safety, Robustness & Interpretability
- Dan Jurafsky
- Federico Bianchi
- Iason Gabriel
- Aylin Caliskan
- Margaret Mitchell
- Douwe Kiela
- Debora Nozza
- Abeba Birhane
Citations in the corpus
- Ethical and social risks of harm from Language Models (2021, background): “…applications ranging from diagnostic healthcare algorithms (Obermeyer et al., 2019) to social outcome prediction (Narayanan, 2021); for a more general introduction see (Chouldechova and Roth, 2018; Kordzadeh and Ghasemaghaei, 2021; Mehrabi et al., 2021; Noble, 2018; Zou and Schiebinger, 2018).”
- Measuring Data (2022, background): “In fact, assessments are often tied to shifting power dynamics involving implicit discrimination and limiting resources and can be used to justify the usage of algorithms as mechanisms of oppression (Noble, 2018).”
- You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings (2022, neutral): “, 2019), sociolinguistics (Labov, 1986; 093 Cheshire, 2007), and critical race theory (Noble, 2018; 094 Benjamin, 2019).”
- How Platform-User Power Relations Shape Algorithmic Accountability: A Case Study of Instant Loan Platforms and Financially Stressed Users in India (2022, background): “and fairer decisions than their human counterparts [45, 52], they could also produce harmful outcomes, worsening inequality in society [20, 23, 50, 54, 92, 94, 98].”
- Critical Race Theory for HCI (2020, background): “Much of this work notes how offline racism is reflected and reinforced online [10, 89].”
- Sociotechnical Harms: Scoping a Taxonomy for Harm Reduction (2022, background): “People marginalized in society face numerous explicit and implicit stereotypes conveyed in various forms of data [147], codingschema [137], and design choices [30] that drive algorithmic systems.”
- Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale (2022, background): “Further, the Stable Diffusion model makes these errors even when such photos clearly exist on the web: even Google Image Search—which in the past has sparked controversy for its reproduction of societal bias (Noble, 2018; Metaxa et al., 2021; Singh et al., 2020)— is capable of showing us, upon searc”
- Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction (2022, methodology): “This harm sub-type was also popularized by Safiya Noble in Algorithms of Oppression [156].”