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Towards decolonising computational sciences

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arxiv 2009.14258 v1 pith:DQ3HFHAL submitted 2020-09-29 cs.CY

classification cs.CY
keywords computationalfieldssciencesciencescognitivedatadecolonisingpast
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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.

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  1. "I Would Never Trust Anything Western": Kumu (Educator) Perspectives on Use of LLMs for Culturally Revitalizing CS Education in Hawaiian Schools

    cs.CY 2025-01 conditional novelty 6.0 of 10

    Educators in Hawaiian immersion schools see LLMs as time-saving but culturally unreliable, so AI tools should default to Hawaiian epistemologies and cite their sources.

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