Towards Decolonising Computational Sciences

This article sets out our perspective on how to begin the journey of decolonising computational fi elds, 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 opp...

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Autores principales:	Abeba Birhane, Olivia Guest
Formato:	article
Lenguaje:	DA EN NB SV
Publicado:	The Royal Danish Library 2021
Materias:	decolonisation computational sciences cognitive sciences machine learning artificial intelligence anti-blackness Social Sciences H
Acceso en línea:	https://doaj.org/article/115ab6417c954ed282ee290c030c3374
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spelling	oai:doaj.org-article:115ab6417c954ed282ee290c030c33742021-11-29T16:53:03ZTowards Decolonising Computational Sciences10.7146/kkf.v29i2.1248992245-6937https://doaj.org/article/115ab6417c954ed282ee290c030c33742021-02-01T00:00:00Zhttps://tidsskrift.dk/KKF/article/view/124899https://doaj.org/toc/2245-6937 This article sets out our perspective on how to begin the journey of decolonising computational fi elds, 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; and b) rejection of the idea that centring 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 fi elds’ histories and heritage holds the key to avoiding mistakes of the past. In contrast to, for example, initiatives such as “diversity boards”, which can be harmful because they superfi cially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the work of many women of colour, 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 artifi cial intelligence. We aspire to progress away from these fi elds’ stagnant, sexist, and racist shared past into an ecosystem that welcomes and nurtures demographically diverse researchers and ideas that critically challenge the status quo. Abeba BirhaneOlivia GuestThe Royal Danish Libraryarticledecolonisationcomputational sciencescognitive sciencesmachine learningartificial intelligenceanti-blacknessSocial SciencesHDAENNBSVKvinder, Køn & Forskning, Vol 29, Iss 1 (2021)
institution	DOAJ
collection	DOAJ
language	DA EN NB SV
topic	decolonisation computational sciences cognitive sciences machine learning artificial intelligence anti-blackness Social Sciences H
spellingShingle	decolonisation computational sciences cognitive sciences machine learning artificial intelligence anti-blackness Social Sciences H Abeba Birhane Olivia Guest Towards Decolonising Computational Sciences
description	This article sets out our perspective on how to begin the journey of decolonising computational fi elds, 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; and b) rejection of the idea that centring 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 fi elds’ histories and heritage holds the key to avoiding mistakes of the past. In contrast to, for example, initiatives such as “diversity boards”, which can be harmful because they superfi cially appear reformatory but nonetheless center whiteness and maintain the status quo. Building on the work of many women of colour, 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 artifi cial intelligence. We aspire to progress away from these fi elds’ stagnant, sexist, and racist shared past into an ecosystem that welcomes and nurtures demographically diverse researchers and ideas that critically challenge the status quo.
format	article
author	Abeba Birhane Olivia Guest
author_facet	Abeba Birhane Olivia Guest
author_sort	Abeba Birhane
title	Towards Decolonising Computational Sciences
title_short	Towards Decolonising Computational Sciences
title_full	Towards Decolonising Computational Sciences
title_fullStr	Towards Decolonising Computational Sciences
title_full_unstemmed	Towards Decolonising Computational Sciences
title_sort	towards decolonising computational sciences
publisher	The Royal Danish Library
publishDate	2021
url	https://doaj.org/article/115ab6417c954ed282ee290c030c3374
work_keys_str_mv	AT abebabirhane towardsdecolonisingcomputationalsciences AT oliviaguest towardsdecolonisingcomputationalsciences
_version_	1718407207985348608

Towards Decolonising Computational Sciences

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