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P195


Making and doing AI from Africa: critical insights on AI and data science 
Convenors:
Yousif Hassan (University of Michigan - Ann Arbor)
Kwame Edwin Otu (Georgetown University)
Kebene Wodajo (ETH Zurich)
Jia Hui Lee (University of Bayreuth)
Laila Hussein Moustafa (University of Illinois)
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Chairs:
Yousif Hassan (University of Michigan - Ann Arbor)
Jia Hui Lee (University of Bayreuth)
Discussants:
Kebene Wodajo (ETH Zurich)
Laila Hussein Moustafa (University of Illinois)
Kwame Edwin Otu (Georgetown University)
Format:
Traditional Open Panel

Short Abstract:

This panel explores the questions of what analytical frameworks for the social study of AI and data might look like when they are shaped by knowledges and experiences from Africa and how understandings of concepts such as intelligence, learning, and computing are contested in an African context.

Long Abstract:

This panel grapples with two interrelated questions: What might analytical frameworks for the social study of Artificial Intelligence (AI), machine learning, and data science look like when they are shaped by knowledges and experiences from Africa? How are understandings of key concepts, such as intelligence, learning, data, digitality, virtuality, artificiality, computing, and so forth contested in an African context? Several scholars in African studies, anthropology, information science, and other allied fields have argued for analyses of technology that do not take for granted systems of knowledge based in Europe and the West (Archambault 2017; Hassan 2022; Newell and Pype 2021; Nyabola 2018). Africans and other communities in the Majority World have been grappling with the social and political effects of AI and data science. Kenyan employees have reported experiencing post-traumatic stress disorder as a result of labeling datasets and moderating AI content for OpenAI and Facebook. Mobile phone companies continue to collect and use personal information of its African users while expanding demand for its uses. Several African cities have embarked on projects of ‘smartness,’ including rolling out digital platforms for government services, monitoring environmental change with sensors, and supporting innovation hubs, such as Yabacon Valley and Konza Technopolis. As some quarters embrace digitization, others have warned of a new algorithmic (Birhane 2020) or digital colonization. We invite papers that examine African experiences of AI and data that intervene into discussions about labor, ethics, environmental impact, governance, practices of data collection, and innovation. We encourage, but not limited to, presentations that articulate how African experiences of AI and data might contribute to analytical frameworks for a critical study of AI and data.

Accepted papers: