mlsanityData to 7 Oct 2026

Est-ce que vous compute? Code-switching, cultural identity, and AI

arXiv 2112.08256 ↗First posted 15 Dec 2021cs.CY cs.AI cs.CLcited by 6

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Abstract (arXiv)

Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson's (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing that we call 'cultural smothering'. By leaving the socio-dynamic features of cultural code-switching unaddressed, AI systems risk negatively impacting already-marginalised social groups by widening opportunity gaps and further entrenching social inequalities.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 7 of its 76 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

7 of 76 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  4. 2020Artificial Intelligence, Values and Alignmentcited by 443
  5. 2021What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpuscited by 79
  6. 2021Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpuscited by 46
  7. 2019On the Morality of Artificial Intelligencecited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

6 papers.