Learning from the EU AI Act: What Africa Can Adopt

I. Introduction

Africa is at that critical crossroads in the increasingly rapid developments surrounding AI. It is at this significant juncture to unpack and leverage AI for transformative impact in crucial areas like health, agriculture, education, and governance. Still, it also faces an urgent need to implement comprehensive regulatory frameworks. These frameworks will be fundamental in informing ethical and responsible development and deployment of AI technologies in service of the interest and protection of the communities they are developed to serve.

The potentiality of the role of AI in Africa is immense. The progress is unparalleled, from innovative uses such as AI-enhanced diagnostic tools that transform healthcare delivery in hard-to-reach communities to intelligent farming technologies that increase yields. A 2019 report by Google underlined this, with a notable rise in AI research contributions by African scholars, indicating increasing local expertise with a drive toward Indigenous innovation. The rapid embracing of this technology does not come without its drawbacks. Data privacy concerns, algorithmic bias, and the ability to amplify existing disparities are issues that will all need to be treated with the utmost care and consideration.

The presentation of the EU AI Act in April 2021 marked a milestone in the global landscape of AI. This is the first pioneering legislation ever to regulate AI systems because they risk society, using a risk-based framework. The Act classifies AI applications of risk and prescribes specific requirements for developers and users to ensure transparency, human oversight, and accountability. It aims to create public trust in AI technologies while creating an environment promoting innovation.

Although the EU AI Act is underpinned by design with the European context, foundational principles, and regulatory approaches, much can be shared with African policymakers facing similar challenges in the governance of AI. Given Africa’s rich cultural heritage, diverse economies, and distinct developmental needs, it provides a unique opportunity to draw lessons from the EU regulatory effort. With due attention to the text and revision where necessary, relevant portions of the EU AI Act can help African countries get around regulatory challenges more efficiently to lay a solid foundation for ethical, transparent, and accountable AI systems. These steps create an environment that instills confidence in the citizens and responsible investments in AI, placing Africa at the forefront of ethical AI in tune with the continent’s more significant development goals and values.

Developing a practical AI governance framework in Africa will not, however, be without its challenges:

• Issues of limited resources.
• Varying states of technological infrastructure.
• There is a need to strike a proper balance between regulation and innovation.

The successful performance of such a task depends on a reflective adaptation of the EU model to the peculiar African situation.

The discussion herein explores how Africa might draw inspiration from the EU AI Act by identifying those elements that could realistically be adopted or adapted for the African environment. We provide actionable insights for African policymakers, innovators, and other stakeholders by exploring some of the critical principles of the Act, including risk-based categorizations and governance arrangements. It is meant to set up an African way of governing AI in such a manner that the goal is protecting citizens, promoting innovation, and capitalizing on its transformative power to sustainable development.

II. Overview of the EU AI?Act

The European Union’s Artificial Intelligence Act, proposed in April 2021, represents a landmark effort to create a comprehensive regulatory framework for AI. Understanding its key components is crucial for African policymakers to consider the elements that must be adopted or adapted. Here’s an overview of the Act’s main features:

A. Key Principles and Objectives

The EU AI Act is built on several core principles:

1. Protection of fundamental rights: Ensuring AI systems respect human rights, democracy, and the rule of law.
2. Risk-based approach: Regulating AI systems based on their potential risk to society.
3. Legal certainty: Providing a clear framework for AI developers and users.
4. Innovation-friendly: Balancing regulation with the need to foster AI innovation.
5. Human-centric AI: Emphasizing human oversight and control over AI systems.

The Act aims to create an ecosystem of trust around AI while supporting the EU’s technological leadership.

B. Risk-based Approach to AI Regulation

The Act categorizes AI systems into four risk levels:

1. Unacceptable risk: AI systems threatening safety, livelihoods, and rights are prohibited.
2. High risk: Systems used in critical areas (e.g., healthcare, law enforcement) face strict obligations.
3. Limited risk: Systems with specific transparency obligations (e.g., chatbots, emotion recognition systems).
4. Minimal risk: All other AI systems are allowed with minimal restrictions.

This tiered approach allows for proportionate regulation based on the potential harm of different AI applications.

C. Prohibited AI Practices

The Act outlines several AI practices that are explicitly prohibited, including:

• Social scoring by governments
• Exploitation of vulnerabilities of specific groups
• Real-time remote biometric identification in public spaces for law enforcement (with some exceptions)

D. Requirements for High-Risk AI?Systems

High-risk AI systems must meet stringent requirements, including:

• Implementing risk management systems
• Ensuring high-quality datasets to minimize bias
• Maintaining detailed documentation for traceability
• Providing clear and adequate information to users
• Ensuring human oversight
• Meeting high standards of robustness, accuracy, and cybersecurity

E. Transparency Obligations for Certain AI?Systems

Some AI systems, while not high-risk, must meet specific transparency requirements:

• Notifying users when they interact with AI systems (e.g., chatbots)
• Disclosing when emotion recognition or biometric categorization systems are in use
• Labeling deep fakes or other artificially manipulated content

F. Governance Framework and Enforcement Mechanisms

The Act establishes a robust governance structure:

• Establish a European Artificial Intelligence Board that will facilitate implementation.
• Designation of national competent authorities in each member state
• Heavy fines in case of infringement, up to €30 million or, in case of an undertaking, up to 6% of the total worldwide annual turnover.

The Act also encourages the creation of regulatory sandboxes to foster AI innovation within a controlled environment.

The EU’s comprehensive approach to AI governance is a rich source of ideas and strategies. African policymakers should consider these elements, adapting them to address unique challenges and opportunities presented by this powerful technology in domestic contexts. As discussed throughout this report, while wholesale adoption neither will nor should be feasible, many of the principles and approaches represented in the EU’s AI Act may provide critical lessons to inform Africa’s approach toward the regulation of AI.

III. The Current State of AI Governance in?Africa

In my earlier article, “AI Governance in Africa: What Needs to be Done?”, I discussed the critical need for a practical regulatory framework that balances innovation and ethics. Revisiting this topic today, I want to dive deeper into what’s happening on the ground?—?because understanding the current landscape is critical to building a stronger future for AI in Africa. While policies like the EU AI Act have particularly inspired progress, the journey is one of varied strides and persistent challenges.

A. Existing AI Policies and Strategies in African Countries

Across Africa, there have been some commendable moves toward developing AI policies:

• South Africa: In 2021, South Africa introduced its draft National Artificial Intelligence Framework, which focuses on socio-economic growth through AI while emphasizing ethics, human rights, and inclusivity.
• Kenya: In 2022, Kenya launched its National Artificial Intelligence Strategy to position itself as an African AI leader. The strategy touches on plans for adoption across sectors and addresses ethical implications.
• Nigeria: with its 2020 National Digital Economy Policy and Strategy, Nigeria has begun laying the groundwork for AI development. A specific AI policy is still in the works.
• Egypt: Egypt’s 2019 National AI Strategy focuses on education, research, and sectoral implementation in healthcare and agriculture.
• Mauritius: Mauritius announced an Artificial Intelligence Council in 2018 to drive its vision of becoming an AI hub.

These are promising steps, yet we must recognize that many African countries still need comprehensive AI policies.

B. Challenges in AI Adoption and Regulation in?Africa

Despite progress in some areas, Africa faces several challenges in AI adoption and regulation:

1. Limited AI expertise: Many African countries need more AI specialists and researchers, affecting AI technology development and regulation.
2. Data availability and quality: Many African countries need more comprehensive, high-quality datasets for training AI systems and informing evidence-based policymaking.
3. Digital divide: Uneven access to the internet and digital technologies across the continent creates disparities in AI adoption and raises concerns about inclusivity.
4. Resource constraints: Limited financial resources can hinder both AI development and the implementation of robust regulatory frameworks.
5. Ethical concerns: Issues like algorithmic bias and data privacy are particularly pertinent in the diverse African context but must be adequately addressed in existing policies.
6. Balancing innovation and regulation: There’s a need to encourage AI innovation while protecting citizens’ rights, a balance many countries struggle to strike.

C. The Need for a Coordinated Approach to AI Governance

While individual countries are making progress, there’s a growing recognition of the need for a more coordinated, continent-wide approach to AI governance:

1. African Union initiatives: The African Union’s Digital Transformation Strategy for Africa (2020–2030) includes provisions for emerging technologies like AI, signaling a move towards a more unified approach.
2. Regional collaborations: Initiatives like the AI for Development in Africa (AI4D Africa) program, supported by Canada’s IDRC and the Swedish International Development Cooperation Agency, foster pan-African AI research and policy collaboration.
3. International partnerships: Many African countries engage with international bodies like UNESCO and the OECD on AI ethics and governance, indicating a desire to align with global best practices.
4. Calls for an African AI strategy: Academics, policymakers, and industry leaders increasingly call for a comprehensive African AI strategy that addresses the continent’s unique challenges and opportunities.

IV. Key Elements of the EU AI Act Relevant to?Africa

While the AI Act focuses mainly on the European Union, numerous elements from it cut pretty well across the African landscape and would work in an African setting. This concerns universal concerns in the governance of AI while offering frameworks that could be adapted to unique challenges and opportunities on the African continent.

A. Risk-based Categorization of AI?Systems

The EU AI Act’s risk-based approach to AI regulation is highly relevant for Africa:

1. Adaptability: This approach allows for flexible regulation tailored to different sectors and use cases, which is crucial given the diverse economies and tech landscapes across African countries.
2. Resource Efficiency: Countries can allocate limited regulatory resources more effectively by focusing stringent regulations on high-risk AI applications.
3. Innovation-Friendly: This tiered approach allows innovation in low-risk areas while ensuring adequate safeguards for high-risk applications.

African Context: This could help countries like Kenya or Nigeria, seeing rapid AI adoption in various sectors, prioritize regulatory efforts on critical areas like healthcare or financial services AI applications.

B. Focus on Fundamental Rights and Ethical?AI

The Act’s emphasis on protecting fundamental rights and promoting ethical AI aligns well with African values and needs:

1. Human Rights Protection: This focus is crucial given the history of data exploitation and privacy concerns in some African countries.
2. Non-Discrimination: The Act’s provisions against AI systems that might discriminate based on protected characteristics are particularly relevant in Africa’s diverse socio-cultural context.
3. Transparency and Explainability: These principles can help build public trust in AI systems, essential for widespread adoption.

African Context: Countries like South Africa, which emphasizes human rights in its draft AI framework, could benefit from the EU’s detailed approach to operationalizing these principles in AI governance.

C. Transparency and Accountability Measures

The Act’s requirements for transparency and accountability in AI systems are highly relevant:

1. Documentation Requirements: Mandating detailed documentation of AI systems can help audit and ensure compliance.
2. Human Oversight: The emphasis on human oversight aligns with the need to maintain human agency in AI decision-making processes.
3. Notification of AI Use: Requirements to inform users when interacting with AI systems (like chatbots) can help build digital literacy.

African Context: These measures could be instrumental in countries like Egypt or Mauritius, which are positioning themselves as AI hubs and need to build trust in their AI ecosystems.

D. Quality Management for High-Risk AI?Systems

The Act’s stipulations for quality management in high-risk AI systems offer valuable guidance:

1. Data Quality: Requirements for high-quality training data are crucial for addressing concerns about AI bias, a significant issue in diverse African societies.
2. Robustness and Accuracy: These requirements can help ensure AI systems perform reliably, which is crucial in critical sectors like healthcare or finance.
3. Cybersecurity: Given the increasing cybersecurity threats in Africa, the Act’s emphasis on AI system security is highly relevant.

African Context: Countries like Nigeria, with growing fintech sectors, could adopt these quality management principles to ensure the reliability and security of AI in financial services.

E. Governance Framework

While the specific governance structure of the EU AI Act may not be directly transferable, certain elements are worth considering:

1. National Competent Authorities: Designating specific bodies to oversee AI governance could help streamline regulation efforts.
2. Regulatory Sandboxes: This concept could foster AI innovation while maintaining regulatory oversight.
3. Penalties for Non-Compliance: While the specific penalties may differ, the principle of enforcement mechanisms is crucial for effective regulation.

African Context: The African Union could coordinate AI governance efforts across the continent, similar to the European AI Board.

These elements of the EU AI Act offer valuable insights for African countries as they develop their own AI governance frameworks. However, it’s crucial to note that any adoption or adaptation must carefully consider the unique African context, including resource constraints, diverse cultural considerations, and specific developmental goals. The following section will explore how these elements might be adapted to suit African needs and realities.

V. Adapting EU AI Act Elements to the African?Context

While the EU AI Act offers valuable insights for AI governance, its successful implementation in Africa requires careful adaptation to address the continent’s unique challenges and opportunities. This section explores how critical elements of the Act can be tailored to suit the African context.

A. Addressing Unique Challenges and Opportunities in?Africa

1. Infrastructure and Digital Divide Considerations

Challenge: Many African countries face significant infrastructure gaps and digital divides, hindering AI adoption and complicating regulation.

Adaptation Strategies:

• Phased Implementation: Introduce AI regulations gradually, starting with urban areas or specific sectors with better digital infrastructure.
• Mobile-First Approach: Given the prevalence of mobile technology in Africa, adapt regulations to prioritize mobile AI applications.
• Public-Private Partnerships: Encourage collaborations to improve digital infrastructure alongside AI development.

Example: Kenya could adapt the EU’s risk-based approach by initially focusing on regulating AI in its well-developed mobile banking sector, gradually expanding to other areas as digital infrastructure improves.

2. Capacity Building and Skills Development

Challenge: There is a significant AI skills gap in many African countries, which affects both AI development and regulation.

Adaptation Strategies:

• Integrated Education Programs: Incorporate AI and data science into national education curricula at various levels.
• Regulatory Capacity Building: Invest in training programs for regulators and policymakers to enhance AI governance capabilities.
• International Collaborations: Partner with global institutions for knowledge transfer and capacity building.

Example: Nigeria could adopt the EU’s emphasis on AI expertise by establishing partnerships with international universities to develop AI governance courses for regulators and policymakers.

B. Tailoring Risk Categories to African Priorities

Adaptation Need: The EU’s risk categories may need to align fully with Africa’s most pressing AI-related concerns.

Strategies:

• Contextualized Risk Assessment: Develop risk categories that reflect African priorities, such as AI applications in agriculture, public health, or financial inclusion.
• Inclusive Categorization Process: Involve diverse stakeholders in defining risk categories, including rural communities and marginalized groups.
• Dynamic Updating: Establish mechanisms to regularly review and update risk categories as the AI landscape evolves.

Example: South Africa could adapt the EU’s risk-based approach by adding a specific high-risk category for AI systems used in mining safety, reflecting the industry’s importance and associated risks.

C. Adapting Transparency and Accountability Measures

Adaptation Need: Transparency requirements must consider varying levels of AI literacy and diverse linguistic landscapes in Africa.

Strategies:

• Multilingual Disclosures: AI disclosures are required in multiple local languages.
• Visual Communication: Develop standardized visual cues to indicate AI use, catering to varying literacy levels.
• Community Engagement: Implement community-based explanations of AI systems, leveraging traditional communication structures.

For example, with its linguistic diversity, Ethiopia could adapt to the EU’s transparency requirements by mandating AI disclosures in major local languages and developing pictorial representations for AI system notifications.

D. Contextualizing Data Protection and?Privacy

Adaptation Need: Data protection approaches must consider unique African contexts, including communal data ownership concepts and limited existing data protection frameworks.

Strategies:

• Cultural Sensitivity: Develop data protection guidelines that respect local cultural privacy and data sharing norms.
• Tiered Data Protection: Implement graduated data protection requirements based on an organization’s size and capacity.
• Cross-Border Data Flows: Develop Africa-specific guidelines for cross-border data transfers, considering regional integration efforts.

Example: The East African Community could adapt to the EU’s data protection requirements by developing regional data-sharing agreements that respect individual privacy and traditional communal data ownership concepts.

E. Fostering Innovation While Ensuring Ethical?AI

Adaptation Need: Given AI’s potential to address pressing developmental challenges, the need to foster AI innovation must be balanced with ethical considerations.

Strategies:

• Sector-Specific Sandboxes: Develop regulatory sandboxes for critical sectors like agriculture or healthcare, allowing controlled AI experimentation.
• Ethics Review Boards: Establish AI ethics review boards with diverse perspectives, including traditional leaders and civil society representatives.
• Incentive Structures: Create incentives for developing AI solutions that address critical local needs while adhering to ethical guidelines.

Example: Rwanda could adapt the EU’s regulatory sandbox concept by creating a specialized sandbox for AI applications in agriculture. This would allow innovative solutions while ensuring ethical considerations and local relevance.

F. Building a Pan-African Approach

Adaptation Need: While respecting national sovereignty, there’s a need for coordinated AI governance across Africa to prevent regulatory fragmentation.

Strategies:

• African Union Framework: Develop an African Union AI governance framework that provides general principles, allowing individual countries to adapt to their specific contexts.
• Regional Cooperation: Encourage regional bodies (e.g., ECOWAS, SADC) to develop harmonized AI policies.
• Peer Learning Networks: Establish networks for African countries to share experiences and best practices in AI governance.

Example: The African Union could adapt the EU’s approach by developing an “African AI Act” that provides overarching principles and guidelines while allowing member states flexibility in implementation based on their unique contexts.

By thoughtfully adapting elements of the EU AI Act, African countries can develop AI governance frameworks that are both globally aligned and locally relevant. This approach can help harness AI’s potential for development while addressing the continent’s unique challenges and upholding African values and priorities.

VIII. Case?Studies

A. Examples of Successful AI Regulation in African Countries

1. Tunisia: Pioneering AI Governance

Tunisia has emerged as a leader in AI governance on the African continent. 2018, the country established a dedicated AI task force and developed a comprehensive national AI strategy.

Key features:

• Creation of the “AI Task Force” under the Ministry of Higher Education and Scientific Research
• Development of the “AI Strategy: Unlocking Tunisia’s capabilities” in 2018
• Focus on key sectors: healthcare, agriculture, transportation, and education
• Emphasis on ethical AI development and use

Example initiative: The Tunisian government partnered with the UNESCO Chair in AI to develop AI ethics guidelines tailored to the Tunisian context. These guidelines address issues such as data privacy, algorithmic bias, and the socioeconomic impact of AI in Tunisia.

Outcome: Tunisia’s proactive approach has positioned it as a hub for AI development in North Africa, attracting international partnerships and investments in the AI sector.

2. Kenya: Balancing Innovation and Regulation

Kenya, known for its vibrant tech scene, has created an enabling environment for AI while addressing regulatory concerns.

Key features:

• Establishment of the Distributed Ledgers Technology and Artificial Intelligence Task Force in 2018
• Development of the National ICT Policy, which includes provisions for AI governance
• Creation of regulatory sandboxes to test AI applications in fintech

Example initiative: The Kenyan Central Bank’s regulatory sandbox has allowed AI-powered fintech solutions to be tested in a controlled environment, ensuring compliance with financial regulations while fostering innovation.

Outcome: Kenya has maintained its position as East Africa’s tech hub while implementing measured regulatory oversight, particularly in the financial sector.

B. Lessons from Other Developing Regions

1. India: Comprehensive Approach to AI Ethics and Governance

India’s approach to AI regulation offers valuable insights for African countries, given similarities in developmental challenges and the scale of implementation.

Key features:

• National Strategy for Artificial Intelligence (#AIforAll) launched in 2018
• Establishment of the AI Ethics Committee by the Ministry of Electronics and Information Technology
• Development of the Responsible AI for Social Empowerment (RAISE) framework

Example initiative: The “AI for All” strategy focuses on leveraging AI for social good, addressing issues like healthcare accessibility and agricultural productivity. The approach emphasizes developing inclusive AI solutions catering to India’s diverse population.

Lessons for Africa:

1. Importance of aligning AI strategies with broader development goals
2. Need for inclusive AI development that considers diverse demographics
3. Value of public-private partnerships in Driving AI Innovation and adoption

2. Brazil: Sector-Specific AI Regulation

Brazil’s approach to AI regulation, focusing on sector-specific guidelines rather than a one-size-fits-all policy, offers an alternative model for African countries to consider.

Key features:

• Brazilian AI Strategy launched in 2021
• Sector-specific AI guidelines, particularly in finance and healthcare
• Focus on data protection through the General Data Protection Law (LGPD)

Example initiative: The Brazilian Central Bank has developed specific guidelines for using AI in the financial sector, addressing issues such as algorithmic transparency, data privacy, and cybersecurity.

Lessons for Africa:

1. Potential benefits of tailoring AI regulations to specific sectors
2. Importance of strong data protection laws as a foundation for AI governance
3. Value of gradual, iterative approach to AI regulation

3. Singapore: Building AI Literacy and?Trust

Singapore’s emphasis on building public trust and understanding of AI provides valuable lessons for African countries looking to foster AI adoption.

Key features:

• National AI Strategy launched in 2019
• AI Governance Framework focusing on building public trust
• Investments in AI education and literacy programs

Example initiative: Singapore’s “AI for Everyone” program aims to provide basic AI literacy to 100,000 citizens and residents, including non-technology workers and students.

Lessons for Africa:

1. Importance of public education and engagement in AI governance
2. Value of transparency in building trust in AI systems
3. Need for continuous skills development to support AI adoption

These case studies demonstrate various approaches to AI regulation and governance, each tailored to the specific context and needs of the country or region. African nations can draw valuable lessons from these examples, adapting and applying them to their unique circumstances as they develop their AI regulatory frameworks.

IX. Conclusion: The Future of AI Regulation in?Africa

As we’ve explored the landscape of AI regulation, drawing insights from the EU AI Act and examining case studies from Africa and other developing regions, it’s clear that Africa stands at a crucial juncture. The continent has the opportunity to shape its AI future in a way that addresses its unique challenges and leverages its diverse strengths. Let’s recap the key points and chart the path forward.

A. Recap of Key?Points

1. Learning from the EU AI Act: The EU AI Act’s risk-based approach offers a valuable framework that African countries can adapt to their specific contexts. This approach allows for nuanced regulation that doesn’t stifle innovation while protecting citizens from potential harm.
2. Protecting Fundamental Rights: As seen in the EU model and successful case studies, protecting privacy, ensuring non-discrimination, and maintaining human oversight are crucial elements of effective AI regulation.
3. Balancing Innovation and Regulation: Countries like Tunisia and Kenya have demonstrated that fostering AI innovation while implementing measured regulatory oversight is possible. Regulatory sandboxes and sector-specific guidelines can play a vital role in this balance.
4. Addressing Unique African Challenges: From using AI to tackle food security in Ethiopia to leveraging AI for wildlife conservation in Tanzania, we’ve seen how AI can be harnessed to address uniquely African problems.
5. Importance of Collaboration: The potential for pan-African collaboration, as exemplified by initiatives like the African Continental Free Trade Area, can significantly boost AI development and governance across the continent.

B. The Path Forward for AI Regulation in?Africa

Looking ahead, Africa’s path to effective AI regulation could include:

1. Development of an “African AI Act”: Drawing inspiration from the EU AI Act but tailored to African realities, a collaborative effort to create a continent-wide framework could provide a solid foundation for national policies. Example: The African Union could spearhead the development of this act, ensuring representation from all regions and key sectors.
2. Investment in AI Education and Literacy: Following Singapore’s example, African countries need to prioritize AI literacy for tech workers and the general public to build trust and understanding. An example is the expansion of initiatives like Egypt’s AI-focused coding schools across the continent.
3. Strengthening Data Protection and Sovereignty: As AI systems rely heavily on data, robust data protection laws and local data storage solutions will be crucial. Example: Nigeria’s efforts to localize data centers for sensitive AI applications could be replicated in other countries.
4. Sector-Specific Regulation: Using Brazil’s approach, African countries could develop targeted regulations for critical sectors like finance, healthcare, and agriculture. Example: Kenya’s regulatory sandbox for AI in fintech could be expanded to other industries and countries.
5. Regional Harmonization: Leveraging existing regional bodies to create harmonized AI standards and regulations will be vital for cross-border collaboration and trade. For example, the East African Community could develop shared AI governance principles for its member states.

C. Call to Action for Policymakers and Stakeholders

As Africa embarks on this journey of AI regulation, all stakeholders must play their part:

1. Policymakers: Engage in inclusive, multi-stakeholder dialogues to develop AI policies that reflect diverse perspectives and needs. Prioritize the development of AI strategies that align with national development goals. Example: South Africa’s ongoing public consultations for AI ethics guidelines serve as a model for inclusive policy development.
2. Private Sector: Actively participate in regulatory discussions, sharing insights from practical AI implementation. Embrace ethical AI development practices and contribute to building local AI ecosystems. Example: Tech hubs like Nairobi’s iHub can be crucial in fostering responsible AI innovation.
3. Academia: Focus research efforts on AI applications that address local challenges. Collaborate with government and industry to ensure AI education programs meet evolving needs. Example: Expansion of pan-African AI research initiatives like Deep Learning Indaba to include policy research.
4. Civil Society: Advocate for transparent and accountable AI systems. Help in building public awareness about the benefits and risks of AI. Example: NGOs can organize community workshops on AI literacy, similar to digital literacy programs.
5. International Partners: Support capacity-building initiatives and share best practices while respecting Africa’s need to develop contextually appropriate solutions. Example: Continue and expand UNESCO’s efforts in developing AI ethics guidelines in countries like Tunisia.

The future of AI in Africa is bright and full of possibilities. By learning from global best practices, adapting them to local contexts, and fostering pan-African collaboration, the continent can develop an AI regulatory framework that protects its citizens and unleashes AI’s transformative potential for sustainable development. The time for action is now?—?let’s shape an AI future that works for all of Africa.