Toward an African AI Safety Agenda: Emerging Risks, Global Gaps, and the Path Forward
Artificial intelligence is not coming to Africa—it's already here. From facial recognition systems deployed for security and border control to algorithmic credit scoring that determines who can access financial services, from automated content moderation that shapes what Africans see online to AI-powered agricultural advisory systems, artificial intelligence is increasingly embedded in African life. The question isn't whether Africa will be affected by AI; it's whether Africans will have any say in how that technology is designed, deployed, and governed.
Right now, the answer is largely no. And if that doesn't change, AI will become another layer of digital colonialism—technology designed elsewhere, for other purposes, imposed on African populations with little regard for local context, rights, or interests.
This isn't hypothetical. It's happening. And Africa needs an AI safety agenda that's about more than catching up to Western standards—it's about defining what safe, equitable, and liberatory AI looks like from an African perspective.
The Stakes: Why AI Safety Matters for Africa
The global AI industry is characterized by extraordinary concentration of power. Approximately 80 percent of compute capacity and AI research funding sits in the United States, Western Europe, and East Asia. The models being developed—large language models, computer vision systems, recommendation algorithms—are trained primarily on data from those regions, reflecting their languages, cultural contexts, and societal assumptions.
When those systems are deployed in Africa, the results are predictable and damaging.
Biased datasets mean facial recognition systems that work poorly on darker skin tones, leading to higher error rates in identification and authentication systems used for everything from border control to mobile payments. These aren't minor technical glitches—they're systems that make African faces literally less legible to the technologies increasingly used to determine access to services and security clearance.
Surveillance exports from China, Israel, and Western companies provide African governments with sophisticated monitoring technologies without the regulatory frameworks or civil society capacity to ensure those systems respect rights. The result is infrastructure that can be used—and has been used—to track dissidents, monitor opposition movements, and suppress dissent.
Election disinformation powered by AI-generated content and algorithmic amplification undermines democratic processes. Kenya's 2022 elections saw significant AI-enabled disinformation; Nigeria's 2023 elections were flooded with deepfakes and coordinated inauthentic behavior. As generative AI becomes more sophisticated and accessible, these problems will intensify.
Algorithmic policing systems trained on data from Western contexts are being deployed in African cities despite having no validity in those contexts. Predictive policing algorithms, for instance, often replicate and amplify existing biases in policing—directing resources toward already over-policed communities and creating self-fulfilling prophecies of criminality.
For Black Levers' readership, these issues should resonate as part of a familiar pattern: technology presented as neutral and progressive that, in practice, reproduces and deepens existing hierarchies. AI safety isn't a technical concern separate from questions of sovereignty, dignity, and liberation—it's central to those questions. Who controls the intelligence systems that increasingly mediate African life is a question of power. And power is always a question of freedom.
The Five-Point African AI Safety Plan
Emerging research from African and diaspora scholars, civil society organizations, and forward-thinking policymakers points toward what a comprehensive African AI safety agenda could look like. It rests on five pillars:
1. Policy Alignment: National AI Strategies That Center Human Rights
Several African countries have developed national AI strategies—Rwanda, Mauritius, Egypt, Kenya, and South Africa among them. These strategies vary in quality and ambition, but most focus primarily on AI adoption and economic competitiveness. What's often missing is a clear human rights framework that establishes non-negotiable principles for how AI can and cannot be used.
An African AI safety approach would require that national AI strategies explicitly center:
Privacy protections that recognize both individual and collective rights to data sovereignty
Transparency requirements that make algorithmic decision-making systems auditable and explainable
Accountability mechanisms that create clear pathways for challenging and redressing harms caused by AI systems
Equity principles that require AI systems to be evaluated not just for accuracy but for their impact on marginalized groups
This isn't about copying European GDPR regulations or American AI executive orders. It's about Africa defining what AI safety means in contexts where colonial legacies of surveillance and control are still living realities, where community-oriented cultures may have different conceptions of privacy than Western individualism assumes, and where the relationship between citizens and states is fundamentally shaped by histories of authoritarian governance.
2. Institutional Setup: A Continental AI Observatory
Africa needs continental-level infrastructure to monitor AI development, assess risks, and coordinate responses. The African Union, possibly through its emerging digital divisions under AfCFTA, should establish an African AI Observatory with several key functions:
Monitoring AI deployment across the continent, creating a database of where AI systems are being used, by whom, and for what purposes
Risk assessment that evaluates AI systems for potential harms specific to African contexts—language exclusion, bias against African phenotypes, economic displacement in sectors where Africa has comparative advantage
Standard-setting that establishes African benchmarks for AI safety, distinct from but interoperable with global standards
Coordinating continental positions on AI governance in international forums
This isn't bureaucracy for its own sake. It's recognizing that individual African countries lack the capacity to effectively regulate global AI companies, but collectively, a continent of 1.4 billion people has leverage.
3. Public Literacy: Nationwide AI Education Initiatives
AI safety can't be left to technical experts. If Africans are going to meaningfully shape how AI is used on the continent, they need to understand what AI is, how it works, and how it affects their lives. That requires major investment in AI literacy at all levels:
Integration into education systems, from primary school through university, teaching not just technical skills but critical AI literacy—the ability to understand when AI is being used, to evaluate its claims, and to recognize its limitations and biases
Public education campaigns that demystify AI and help citizens understand their rights in relation to AI systems
Civil society capacity building so that human rights organizations, journalists, and community advocates can effectively monitor and challenge harmful AI deployment
This is particularly crucial because AI governance won't be determined by continental summits alone—it will be shaped by thousands of local decisions about whether to adopt particular systems, how to regulate their use, and how to hold deployers accountable. An informed public is essential infrastructure for democratic AI governance.
4. Safety Benchmarks: Africa-Specific Auditing Tools
The AI safety community has developed various benchmarks and auditing tools to evaluate AI systems for bias, fairness, and accuracy. The problem is that these tools are designed for Western contexts—they test for bias against Western demographic categories, evaluate performance on Western languages, and assume Western regulatory frameworks.
Africa needs its own auditing tools that:
Test AI systems against African demographic diversity, including skin tone variation, facial features, and body types underrepresented in training data
Evaluate performance across African languages, including low-resource languages that are often completely ignored by commercial AI systems
Assess cultural appropriateness, recognizing that what counts as harmful content or biased representation varies across cultural contexts
Consider African environmental and infrastructural contexts, including how AI systems perform in contexts of limited connectivity, intermittent power, and varied device capabilities
Several African universities and research centers are beginning this work, but it needs significant investment and coordination to create comprehensive, validated African AI safety benchmarks.
5. Global Voice: African Participation in AI Governance Forums
AI governance is being negotiated in multiple international forums—the UN AI Advisory Body, the OECD AI Policy Observatory, various industry-led initiatives, and bilateral arrangements between major AI-developing countries. African participation in these forums has been limited, meaning African interests and perspectives are largely absent from the rules being written.
Changing this requires:
Ensuring African representation in all major AI governance initiatives, with participants who have genuine mandates to represent continental interests
Building African negotiating capacity, including training diplomats and policymakers in AI technical and policy issues
Coordinating African positions so that African representatives aren't speaking with fractured voices but advancing shared principles
Creating alternative forums when existing governance spaces prove unreformable, potentially through south-south coalitions that can develop AI governance frameworks aligned with postcolonial values
Global Comparisons: Learning and Diverging
It's worth understanding how other regions are approaching AI governance, both to learn from their successes and to understand why Africa needs its own approach.
The European Union's AI Act represents the world's most comprehensive AI regulation, categorizing AI systems by risk level and imposing strict requirements on high-risk applications. It's an achievement in regulatory ambition and reflects genuine concerns about algorithmic harms. But it's designed for European contexts—wealthy societies with strong regulatory capacity, established civil liberties traditions, and the institutional infrastructure to enforce complex rules. African countries adopting EU standards wholesale would struggle to enforce them and might find that European definitions of risk don't map onto African priorities.
U.S. AI governance is fragmented across executive orders, sector-specific regulations, and state-level initiatives. The Biden administration's AI executive order emphasizes testing for bias and discrimination, particularly in areas like employment and housing. This reflects American civil rights history and litigation-driven governance. But it assumes a legal and regulatory infrastructure that many African countries lack, and it's fundamentally oriented toward managing AI's domestic impacts rather than addressing its role in global power asymmetries.
China's algorithmic regulations emphasize state control over AI development and deployment, requiring algorithmic recommendation systems to reflect "core socialist values" and subjecting AI systems to content review and security assessments. This approach reflects China's authoritarian governance model and its desire to control information flows. It offers little that's useful for African countries seeking to build democratic AI governance, though it does demonstrate that alternative regulatory models to Western liberalism exist.
The lesson isn't that Africa should copy any of these approaches. It's that AI governance reflects particular political economies, regulatory traditions, and power structures. Africa's AIgovernance must reflect African political economies, regulatory realities, and power structures—which means building frameworks suited to contexts where:
State capacity is variable, making enforcement-heavy regulatory models difficult to implement consistently
Colonial legacies of surveillance and control create different relationships between citizens, data, and state power than exist in the Global North
Informal economies dominate, meaning AI regulation focused only on formal sector deployment will miss where most Africans actually encounter algorithmic systems
Digital infrastructure is still developing, creating opportunities to build AI governance into systems from the start rather than retrofitting regulations onto entrenched technologies
Community-oriented social structures may prioritize collective data rights and communal decision-making in ways Western individualist frameworks don't accommodate
Africa's opportunity—and it is an opportunity—is to leapfrog extractive, surveillance-heavy AI development models and instead define what liberatory AI looks like. Not AI that's simply less biased, but AI that's designed from the ground up to serve African interests, respect African agency, and advance African sovereignty.
Barriers to Implementation: Real Constraints, Not Excuses
Building an African AI safety agenda faces serious obstacles. Acknowledging them honestly is necessary for developing realistic strategies.
Limited compute power means Africa produces little of the actual AI technology it uses. Without significant computing infrastructure, African researchers and developers can't train large models, can't independently verify how commercial AI systems work, and can't build African alternatives to dominant platforms. This creates dependency—Africa becomes a consumer of AI rather than a producer, which fundamentally limits its ability to shape the technology.
Data localization challenges compound this problem. Much of the data about African populations lives on servers controlled by foreign companies, subject to foreign jurisdictions. Requiring data localization sounds good in principle but is difficult to implement without digital infrastructure investment. And even with local data storage, the question remains: who controls access, under what terms, and with what protections?
Fragmented digital regulation across African countries means there's no unified African digital market or regulatory space. A company operating across Africa must navigate 54 different regulatory environments—or more often, it simply ignores regulations in countries that lack enforcement capacity. Without some degree of regulatory harmonization, African countries lack the market power to demand compliance from global AI companies.
Brain drain in AI research is devastating. African universities produce talented AI researchers and engineers who are immediately recruited by Western tech companies, Chinese firms, or Gulf state initiatives offering salaries and research resources that African institutions cannot match. The result is that African expertise in AI is developed in Africa but deployed elsewhere, leaving the continent dependent on foreign AI development.
Power asymmetry in technology partnerships means that when African governments negotiate with tech companies, they're negotiating from positions of weakness. Google, Microsoft, Alibaba, and other major AI developers have resources that dwarf most African government budgets. They offer "partnerships" that are really dependency relationships—providing services in exchange for data access, market entry, and regulatory forbearance.
These barriers are real. But they're not insurmountable, and framing them as excuses for inaction serves no one. The question is how to build African AI capacity and governance despite these constraints—and how to use the AI governance moment to address these underlying dependencies.
Opportunities for Leadership: Africa's AI Advantage
Here's what's rarely acknowledged in discussions of Africa and AI: Africa has specific advantages that, if leveraged strategically, could allow it to shape global AI development rather than simply adapting to it.
Africa is the youngest continent demographically, with a median age below 20 in many countries. This matters for AI development because the generation growing up with AI as a baseline technology can shape its evolution rather than treating it as foreign imposition. Youth-led innovation in AI, if supported with education and infrastructure, could position Africa as a source of AI solutions rather than just a market.
Africa's linguistic diversity represents both a challenge and an opportunity. While most commercial AI is trained on English, Mandarin, and a handful of other major languages, Africa has over 2,000 languages. Building AI systems that work across this diversity requires fundamental innovation in natural language processing. African researchers working on low-resource language AI are doing cutting-edge technical work that has applications far beyond Africa. This could be an area where African AI research leads globally rather than follows.
Africa's governance challenges in areas like land rights, informal economies, and communal resource management are problems that Western AI hasn't been designed to address. Developing AI systems that can, for instance, help manage communal land rights or facilitate informal sector financial inclusion requires innovation that reflects African social and economic structures. This is AI development that centers African contexts rather than adapting Western models—and it could produce systems that work better globally because they're designed for complexity rather than assuming Western institutional frameworks.
Africa's position in global resource chains gives it leverage. The minerals necessary for AI hardware—cobalt, lithium, rare earth elements—come disproportionately from Africa. If African countries coordinate to demand technology transfer, local AI development capacity, and data sovereignty as conditions for resource access, they have more bargaining power than fragmented negotiations suggest.
Africa's experience with technology leapfrogging in areas like mobile money shows what's possible when African contexts drive innovation rather than simply importing Western solutions. M-Pesa succeeded because it was designed for African realities—limited banking infrastructure, mobile-first populations, need for informal sector financial services. AI development could follow similar patterns if it's centered on African needs rather than treating Africa as a late-adopting market.
Roadmap for Collective Action: What Needs to Happen
Building an African AI safety agenda isn't a technical project—it's a political project that requires coordination across multiple actors and sustained commitment over years. Here's what a realistic roadmap could look like:
Immediate (2025-2026): Foundation Building
Establish the African AI Safety Institute under the AU's infrastructure, possibly housed within the AfCFTA digital trade framework. This doesn't require massive funding initially—it requires political commitment and a small secretariat that can begin coordinating continental positions, compiling information on AI deployment across Africa, and representing African interests in international AI governance forums.
Launch continental AI literacy campaigns that use existing educational and civil society infrastructure to begin building public understanding of AI. This includes teacher training, development of curriculum materials, and public education through radio, television, and digital platforms.
Coordinate an African position for UN AI governance negotiations that explicitly centers:
The right to data sovereignty
Requirements for algorithmic transparency and accountability
Protection against AI systems that embed colonial biases
Support for building African AI development capacity as a development priority
Begin mapping AI deployment across Africa—creating a database of where AI systems are being used, by whom, for what purposes, and under what regulatory frameworks. This is basic intelligence that's currently lacking.
Medium-term (2026-2028): Capacity Building
Invest in African AI research infrastructure, including:
Regional centers of excellence in AI research at African universities
Compute capacity specifically allocated to African research priorities
Funding mechanisms that allow African AI researchers to work on African problems while remaining in Africa
Partnerships with African diaspora AI researchers that create knowledge exchange without permanent brain drain
Develop African AI safety benchmarks and auditing tools that can evaluate AI systems for bias, fairness, and appropriateness in African contexts. Make these tools open-source and available to African governments, civil society, and researchers.
Harmonize African digital and AI regulations through AfCFTA's digital trade protocols, creating a unified African regulatory framework that gives African countries collective bargaining power with global AI companies.
Build strategic partnerships with other Global South actors, particularly through BRICS+ and G77+China, to advance shared positions on AI governance that challenge Western dominance of global AI development.
Long-term (2028-2030): Leadership and Innovation
Launch African language model initiatives that create AI systems specifically designed for African languages, contexts, and applications. This should be funded through a combination of African government investment, diaspora capital, and development finance—explicitly not through Big Tech partnerships that would subordinate African AI development to foreign corporate interests.
Establish African AI governance as a model for other Global South regions, demonstrating that alternative frameworks to Western techno-libertarianism and Chinese state control are possible—frameworks that center community rights, democratic accountability, and postcolonial justice.
Create mechanisms for technology transfer that require foreign AI companies operating in Africa to build local capacity, train African developers, and share technical knowledge as conditions for market access.
Position Africa as a leader in AI ethics and safety, particularly around questions of algorithmic bias, surveillance limitations, and the relationship between AI and democratic governance. African experiences with technology used for control could inform global standards that prevent AI from becoming a tool of oppression.
Digital Sovereignty as Liberation
The argument for an African AI safety agenda isn't just about preventing harm—though preventing harm matters enormously. It's about recognizing that AI is infrastructure for the 21st century in the same way that railroads, electricity grids, and telecommunications networks were infrastructure for earlier eras. And the question of who controls that infrastructure is always a question of power.
Africa has lived through what happens when infrastructure is controlled by foreign interests. Colonial railroads connected mines to ports, extracting resources without developing African economies. Telecommunications networks built during the Cold War served surveillance and control more than connection. Structural adjustment's infrastructure privatizations transferred public goods to foreign corporations, enriching investors while degrading services for African populations.
AI could follow the same pattern: technology developed elsewhere, deployed in Africa to extract data and profit, controlled by foreign corporations and states, used for surveillance and control, with African populations bearing costs while benefits accrue elsewhere. Or AI could be different—if Africans demand that it be different and build the capacity to make those demands matter.
This is why AI safety, from a Black Levers perspective, isn't a technical concern separate from liberation politics—it's central to those politics. The question of who controls the intelligence systems that increasingly mediate African life is inseparable from questions of sovereignty, dignity, and freedom. And answering that question in favor of African agency requires treating AI governance as a site of political struggle, not just technical policy-making.
The global AI governance conversation is happening now. The frameworks being built now will shape technology development for decades. Africa can participate in that conversation as a supplicant hoping for consideration, or it can enter as a political force demanding that AI development serve African interests and reflect African values.
But that requires seeing this moment clearly: not as a question of whether Africa can catch up to Western AI development, but whether Africa can define its own AI future. Not whether African countries can adopt European regulations or American standards, but whether Africa can build governance frameworks rooted in its own experiences, priorities, and visions of justice.
The computing revolution didn't liberate Africa—it created new forms of dependency and control. The mobile revolution created opportunities but also new vulnerabilities. The AI revolution will be no different unless Africans actively shape it toward liberatory ends. And that work begins with acknowledging that AI safety isn't about making existing systems slightly less harmful—it's about demanding fundamentally different systems designed to serve different interests.
Africa has been told repeatedly to wait its turn in technological development, to adopt technologies designed elsewhere, to accept that innovation happens in Silicon Valley and Shenzhen while Africa adapts and adopts. That narrative serves those who benefit from African technological dependency. It doesn't serve Africans.
An African AI safety agenda rejects that narrative. It asserts that Africans have the right to shape the technologies that shape their lives, that African contexts and priorities should drive AI development rather than being afterthoughts, and that digital sovereignty is both possible and necessary.
The question isn't whether Africa can afford to build AI governance capacity. The question is whether Africa can afford not to—whether it can accept another generation of infrastructure built by others, for others' purposes, imposed on African populations with little regard for African agency.
The answer, for anyone committed to African liberation, is clear. Now comes the work of making that answer real.
