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Democratizing AI: Exploiting Generative AI to Expand Policy Access and Improve Public Participation in Policy-Making in sub-Saharan Africa

Authors: Sorunke, Ridwan; Durosaye, Malcolm; Lawal, Promise; Ogheneochuko, Godson;

Democratizing AI: Exploiting Generative AI to Expand Policy Access and Improve Public Participation in Policy-Making in sub-Saharan Africa

Abstract

Technologies based on Artificial Intelligence (AI) present an opportunity for Africa to enhance government transparency, drive transformation, and reimagine its economic growth. Its usage could, however, exacerbate existing social and economic inequalities. This paper focuses on the potential of Generative AI (GenAI) systems based on Large Language Models (LLMs) to expand citizens’ access to government policies and promote public participation in policy-making in sub-Saharan Africa. It also explores strategies to mitigate inequality and uphold fundamental rights and freedoms, such as the right to privacy and personal data protection. This paper has four objectives 1. Assess how GenAI presents opportunities for open governance and citizen participation 2. Analyze national strategies and frameworks for AI by governments in sub-Saharan Africa and their alignment with global standards; 3. Identify obstacles and possible solutions to introducing AI in policy-making and governance 4. Identify challenges and risks posed by AI to governance in Africa and develop recommendations to address data protection and human rights issues. The discussion was informed by strategies and policy documents on AI in Africa and insights from users of GovTech solutions.

This paper discusses potential approaches to increase the adoption of GenAI for effective policy dissemination and increased citizen participation in sub-Saharan Africa. It also explores potential strategies to address the challenges and risks posed by AI on governance in sub-Saharan Africa.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green