Over the last year and a half, the audience for AI has grown from IT experts to almost anyone with internet access. Large Language Models enable online users to interact with AI systems, regardless of whether they can code. A large part of why Generative AI has been such a game changer is that it eliminates the need for specialised AI knowledge to reap the benefits of technology. In short, the opportunity to democratise AI has never been better.
However, for Africa to seize this opportunity and truly democratise AI, much work remains to be done in terms of increasing access to internet connectivity and digital literacy. The challenges ahead go beyond the need for increased investment in critical infrastructure and capabilities.
Many public and private organisations in Africa see the risk of new safety and regulatory requirements as a major impediment to the wider adoption of the technology. Leaders must thus collaborate to accelerate AI governance. To support these efforts, a few key focus areas can positively contribute to the work ahead.
The first is to implement and build upon new government-led AI safety frameworks. Several African countries have already begun to formulate their own legal and policy frameworks and are helping to lead discussions around AI policy and strategy development on a regional, continental, and global scale, offering valuable insights.
While at different stages of implementation, they all are looking to find a balance between the need to create guardrails for the new technology while at the same time wanting to help a nascent industry grow, innovate, and adopt these new and emerging technologies.
Secondly, while most potential AI scenarios do not pose significant risks, it’s going to be increasingly important to identify those high-risk situations that will require ‘safety brakes’.
This is particularly relevant when it comes to systems that manage or control critical infrastructure such as electricity grids, water systems, traffic systems or emergency responses. These brakes ensure systems can be quickly controlled or stopped by humans if necessary.
One way for governments to begin developing this safety mechanism is by defining the class of high-risk AI systems that are being deployed to control critical infrastructure and then requiring developers to build and embed such added layers of security in the form of ‘safety brakes’. From there, operators can rigorously test and monitor these high-risk systems, making certain that they can avoid unintended consequences and remain under human control.
Thirdly, to address AI's legal and regulatory challenges, a framework mirroring AI's technology architecture is needed, focusing on the three layers of the tech stack, with different obligations for the level of applications and the layers beneath, which are the AI foundational models and the infrastructure.
The law will also need to place various regulatory responsibilities upon different actors based on their role in managing the different aspects of AI technology.
The fourth strategy revolves around promoting transparency and ensuring academic and public access to AI. A key aspect of AI policy that will require serious discussion in the coming months and years is the balance and tension between security and transparency. For example, some experts think that keeping AI model weights (which are parts of a model that are crucial for a model’s abilities) secret will be necessary for security as this might be required to safeguard vital national security and public safety interests.
However, in many other cases, transparency will be important to improve the understanding of security needs and develop best practices. This is why it’s important to think through how one can provide different types of transparency in different circumstances.
Lastly, it will be critical to pursue new public-private partnerships to use AI as an effective tool to address the inevitable societal challenges that come with new technology. We’ve seen the effectiveness of this approach in countries like Nigeria where we’ve partnered with the United Nations Development Programme to co-convene the AI for Development Reference Group, a multi-stakeholder and interdisciplinary collaboration tasked with helping shape the country’s AI agenda.
In Kenya, we launched a Responsible AI series with Bowmans, Strathmore University, and other stakeholders to discuss AI policy, regulatory frameworks and governance within the country’s AI ecosystem.
In conclusion therefore, while we certainly don’t have all the answers to the questions that this new AI era brings, we believe that by working with stakeholders across the continent, we can help shape a future where AI is a tool that benefits everyone. Grounded in responsible regulation and collaborative partnerships, Africa can fully realise the opportunities presented by a future with AI.
Akua Gyekye is the Government Affairs Director, at Microsoft Africa.
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