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Cited by 79Ethics and Social Impacts of AIArtificial Intelligence ApplicationsDigitalization, Law, and Regulation

Responsible AI in Africa—Challenges and Opportunities

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Okolo, Chinasa T., Aruleba, Kehinde, Obaido, George·2023·Social and Cultural Studies of Robots and AI
DOI 10.1007/978-3-031-08215-3_3
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Abstract

This chapter provides an analysis into the factors that impact the effective adoption and successful implementation of artificial intelligence (AI) technologies in Africa. The study begins by defining the concept of “responsible AI” and what this means specifically for technologies developed and used within Africa. Further sections within the chapter present challenges including digital literacy, lack of local AI talent and governmental barriers that impede. The chapter also goes into an in-depth analysis of the AI startup and research landscape within the African continent, highlighting organisations of interest and concerning trends. ‘Challenges to effective AI adoption and Implementation in Africa’ concludes by envisioning what responsible AI could look like in the African context and provides actionable recommendations for making strides towards this goal.

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  • Building African-centric and Indigenous AI systems based on small-scale local data, culturally sensitive algorithms, socio-ecologically balanced forms of AI deployments, and capacity building initiatives are essential to advance equitable and sustainable AI development on the continent.

    “Building African-centric and Indigenous AI systems based on small-scale local data, culturally sensitive algorithms, socio-ecologically balanced forms of AI deployments, and capacity building initiatives are thus essential to advance equitable and sustainable AI development on the continent [46].”

    From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [46]

    The Recommendations section calls for AI initiatives and training data aligned with local communities, alongside community engagement and training; the Datasets section also stresses cultural nuances. The paper does not recommend small-scale data or socio-ecologically balanced deployments. In fact, the Datasets section says machine learning relies on vast amounts of data. (LLM, conf=0.87)

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Cited by

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Authors
Okolo, C. T., Aruleba, K., & Obaido, G.
Affiliations
  • Cornell University (US)
  • University of Leicester (GB)
  • University of California, Berkeley (US)
Corresponding author
Obaido, George
ORCID
Venue
Social and Cultural Studies of Robots and AI
Publisher
Springer International Publishing
Published
2023
Pages
35-64
ISSN
  • 2523-8523 (ISSN-L)
  • 2523-8531
DOI
10.1007/978-3-031-08215-3_3
OpenAlex
W4313445099
Cited by
79
References
4
Citations by year
2026: 22 · 2025: 27 · 2024: 25 · 2023: 5
FWCI
50.84
Type
book chapter
Language
en
Open access
Licence
Locations
Social and cultural studies of robots and AI (publishedVersion, cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
responsible AI, artificial intelligence adoption, Africa, digital literacy, AI startups, artificial intelligence research, AI governance

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