The Politics of Memory: NLP Models as Liberating Artifacts

The main argument of this article is that natural language processing (NLP) projects of Indigenous African languages can be understood as an effort to restore and preserve Africa’s memory in the digital. Language has always occupied a particular place in the African decolonial imaginary. Several African states embarked on processes of Africanisation of postcolonial institutions in their respective national contexts. One of the main features of this project in many African countries was the restoration of African languages in education and public service. Taking a social constructivist approach rooted in science and technology studies and looking through the analytical lens of decoloniality in Africa, I conceptualise NLP models of Indigenous African languages as liberating artifacts. I mobilise decolonial theory to look at language as a technology that can be used to dis-member and re-member the African subjects of their memory of being as people. The coloniser erased the memory of the African subjugated by cutting off their linguistic connections and instead planted the European memory. From this perspective, decolonisation can be understood as a process of re-memberment that can be achieved through the return of the languages of those whose humanity has been denied or questioned. In this sense, NLP projects offer instances of the digital that represent spaces of restoration and negotiated digital futurities. I contend that these digital spaces in Africa are social spaces of contestation about what common investments should be made, what collective futures are desirable and what are the perceived material consequences of such shared imaginations.

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Journal
Science Technology and Society
Published
2024-10-31
DOI
https://doi.org/10.1177/09717218241282551
Primary Topic
Multimodal Machine Learning Applications
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article
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article

The Politics of Memory: NLP Models as Liberating Artifacts

Yousif Hassan
Science Technology and Society
Multimodal Machine Learning Applications
article

The Politics of Memory: NLP Models as Liberating Artifacts

Yousif Hassan
article en

Abstract

The main argument of this article is that natural language processing (NLP) projects of Indigenous African languages can be understood as an effort to restore and preserve Africa’s memory in the digital. Language has always occupied a particular place in the African decolonial imaginary. Several African states embarked on processes of Africanisation of postcolonial institutions in their respective national contexts. One of the main features of this project in many African countries was the restoration of African languages in education and public service. Taking a social constructivist approach rooted in science and technology studies and looking through the analytical lens of decoloniality in Africa, I conceptualise NLP models of Indigenous African languages as liberating artifacts. I mobilise decolonial theory to look at language as a technology that can be used to dis-member and re-member the African subjects of their memory of being as people. The coloniser erased the memory of the African subjugated by cutting off their linguistic connections and instead planted the European memory. From this perspective, decolonisation can be understood as a process of re-memberment that can be achieved through the return of the languages of those whose humanity has been denied or questioned. In this sense, NLP projects offer instances of the digital that represent spaces of restoration and negotiated digital futurities. I contend that these digital spaces in Africa are social spaces of contestation about what common investments should be made, what collective futures are desirable and what are the perceived material consequences of such shared imaginations.

Science Technology and SocietyVol. 29(4)
University of Michigan (US)
Quality Education
Openalex Percentile: Top 81%
Multimodal Machine Learning Applications
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The Politics of Memory: NLP Models as Liberating Artifacts — Yousif Hassan · Science Technology and Society (2024) | TGRS Research Map | TGRS