mlsanityData to 7 Oct 2026

Towards decolonising computational sciences

arXiv 2009.14258 ↗First posted 29 Sep 2020cs.CYcited by 17

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Abstract (arXiv)

This article sets out our perspective on how to begin the journey of decolonising computational fields, such as data and cognitive sciences. We see this struggle as requiring two basic steps: a) realisation that the present-day system has inherited, and still enacts, hostile, conservative, and oppressive behaviours and principles towards women of colour (WoC); and b) rejection of the idea that centering individual people is a solution to system-level problems. The longer we ignore these two steps, the more "our" academic system maintains its toxic structure, excludes, and harms Black women and other minoritised groups. This also keeps the door open to discredited pseudoscience, like eugenics and physiognomy. We propose that grappling with our fields' histories and heritage holds the key to avoiding mistakes of the past. For example, initiatives such as "diversity boards" can still be harmful because they superficially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the shoulders of many WoC's work, who have been paving the way, we hope to advance the dialogue required to build both a grass-roots and a top-down re-imagining of computational sciences -- including but not limited to psychology, neuroscience, cognitive science, computer science, data science, statistics, machine learning, and artificial intelligence. We aspire for these fields to progress away from their stagnant, sexist, and racist shared past into carving and maintaining an ecosystem where both a diverse demographics of researchers and scientific ideas that critically challenge the status quo are welcomed.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2020.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 4; 2024: 1; 2025: 3.

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  1. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  2. 2023Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generatorscited by 56
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  5. 2022Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approachescited by 23
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  8. 2021The brain is a computer is a brain: neuroscience's internal debate and the social significance of the Computational Metaphorcited by 3
  9. 2023Enhancing Content Moderation with Culturally-Aware Modelscited by 3
  10. 2024The State of Computer Vision Research in Africacited by 3
  11. 2022Building and curating conversational corpora for diversity-aware language science and technologycited by 2
  12. 2023Towards a Better Understanding of the Computer Vision Research Community in Africacited by 2
5 more
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  3. 2021Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoderscited by 0
  4. 2025A Preliminary Framework for Intersectionality in ML Pipelinescited by 0
  5. 2025QUINTA: Reflexive Sensibility For Responsible AI Research and Data-Driven Processescited by 0