AI Governance for Africa > Part 3 > Section 3
There are arguably two main paths to enacting AI governance — or any other policy framework. As detailed below, each has its strengths and weakness.
Path 1: Comprehensive regulation. This the long road to enacting a single, comprehensive law to regulate all types of AI technology – such as the recent EU AI Act.
Path 2: Patchwork regulation. This is a (slightly) shorter path – securing quick wins through small changes in law and policy that combine to make a patchwork. This could include sectoral AI policies or rules (for example, rules issued by the Mauritius banking commission on the use of AI tools for investment products[1]). Or it could take the form of partial regulation of specific uses of AI which are bundled into a broader law. A common example of this is data protection law: of the 36 African countries with a data protection law, at least 31 include at least some regulation on the use of AI for data processing.[2]
Both approaches have their merits. Here are some of the common strengths and weaknesses of each:
Weighing up the pros and cons of each approach, and taking into account your local context, a key question for advocates:
Which approach should you prioritise in your advocacy strategy, and why?
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Part 1: Introduction to AI Governance
Part 1 gives an overview of AI governance principles and approaches, and outlines international frameworks, with case studies from the European Union, the United States, and China. It discusses common concerns and themes driving AI governance.
Part 2: Emerging AI Governance in Africa
Part 2 examines existing and emerging AI governance instruments in Southern Africa – in particular, South Africa, Zambia, and Zimbabwe. More broadly, it also outlines continental responses and details existing governing measures in Africa.
