By Tanatsiwa Dambuza, Zimbabwe.
African giving has long been a communal affair with neighbors pooling resources, kinship networks caring for the sick, and shared proverbs reminding everyone that “it takes a village.” Today, the metaphor of the village is at risk of becoming entirely digital. As African NGOs and startups continue to bring machine learning, big data, and mobile apps to charities and aid programs, we are left to ask ourselves, can the soul of African philanthropy, its ethos of harambee, ubuntu, and teranga, survive in a world of algorithms? Will AI tools strengthen those traditions or transform them into something unrecognizable?
Traditionally, African philanthropy has been rooted in collective values. In Kenya, harambee (“all pull together”) drives villages to fund schools or clinics through joint effort, while ubuntu in Southern Africa (“I am because you are”) celebrates our shared humanity. In Senegal, Teranga, often translated as hospitality, really means a living ethos of generosity and solidarity. Across the continent, philanthropy has grown out of village gatherings, tontines, and communal rituals rather than solely relying on top-down grants. The Nairobi street-food vendor who contributes to a neighbor’s funeral, the Ghanaian susu lending circle, or the Eritrean woman whose home (the Mbongi or Shir) welcomed neighbors in need, all spring from the same well: the African community care.
Even as traditional forms of giving endure, artificial intelligence is beginning to reshape how philanthropic need is identified and addressed in Africa. One example is GiveDirectly, an international NGO operating on the continent, piloting an AI-enhanced system that analyses satellite imagery and flood forecasts to trigger anticipatory cash transfers to Nigerian farming communities before disasters strike, allowing support to reach families earlier and at scale. At the same time, youth-led digital mobilisation, such as the Feminist Coalition’s online fundraising during Nigeria’s #EndSARS protests, reflects the broader shift toward technology-mediated giving in which AI is increasingly being layered, raising early questions about data control, accountability, and whose priorities are embedded in these emerging systems.
Startups are also emerging to plug technology into local needs. In Zimbabwe, Vambo AI is using artificial intelligence to surmount Africa’s language barriers. Its founder, Tsitsi Dzinotyiweyi, is of the view that, while global tech firms “often focus on access, immediacy and convenience,” Vambo takes it a step further. It harnesses the power of AI to build connections and break communication barriers.” It translates and generates text in dozens of African languages (from Swahili to Wolof), allowing schoolchildren to learn in their mother tongue and farmers to access crucial advice without needing to understand English. Further, philanthropists are investing in youth-led tech projects across Africa. For instance, the Mastercard Foundation is sponsoring girls to train in AI and robotics, aiming to “give back” by inspiring thousands more young women into tech.
Other models mix big NGO funding with local innovation. UNICEF’s Venture Fund just backed a cohort of African startups solving “data trust” problems. Among them is AgentsIA from Mali, an AI fact-checker that verifies news articles in Bambara and Fula, and Signvrse from Kenya, which uses AI avatars to interpret sign language for deaf children. These projects show how external grants are seeding African tech solutions, including those aimed at information accessibility through local languages.
Despite this creative energy, many experts sound a warning bell about the risks of a tech-led future. They invoke the idea of the “coloniality of data”: the concern that big outsiders could colonise African information. For instance, critics like Titilope Ajeboriogbon note that “tech companies are building data centres and laying fibre-optic cables to move data generated in Africa to servers located in Silicon Valley.” In other words, Africans’ personal information, through artificial intelligence, is being extracted like raw materials before being sent abroad.
For instance, when African data is stored and processed on foreign servers, the economic value it generates rarely returns to the communities that produced it. Consider a Kenyan farmer who uses a mobile agriculture app to check crop prices or receive farming advice. Each interaction generates valuable data about soil conditions, yields, and market behaviour. That data is aggregated, analysed, and monetised by platform owners abroad, improving proprietary algorithms, attracting investors, and refining products, while the farmer receives only the basic service, not a share of the value created. In effect, African users supply the raw digital material, while profits and decision-making power remain overseas. This dynamic closely mirrors older extractive models, where resources left the continent but wealth did not remain. Artificial intelligence intensifies this pattern by making data easier to harvest, package, and commercialise at scale, turning everyday digital participation into a source of value for foreign tech firms rather than for local communities.
This dynamic can easily entrench existing inequalities. Research by Kofi Yeboah warns that if AI in Africa is left unchecked, it “will reproduce existing power dynamics,” magnifying the privilege of the few. If training data reflects only urban or affluent lives, the poor and marginalised are simply omitted. The result is issues such as “allocative harms”, where whole groups of people get passed over for jobs, loans, or services. Already, many AI systems show bias: for example, facial recognition tools often perform poorly on darker skin tones, and credit algorithms can penalise informal traders. If an aid programme’s computer automatically decides who is “most in need”, it could override local judgements, like a village council’s wisdom being replaced by a black-box decision.
And then there’s the digital divide itself. Only a fraction of Africans have steady electricity, let alone broadband. According to Nathaniel Allen, “limited access to the internet and smartphones can lead to unequal representation of marginalised groups from benefiting from AI.” In practice, this means any tech-driven giving risks missing the poorest or most remote communities.
Thus, AI-driven philanthropy is not only posing a future risk of bifurcating African philanthropy; in many cases, that divide is already visible. A highly digitised, data-intensive and donor-driven model is gaining ground, favouring organisations and platforms that can generate predictive analytics, satellite data and performance dashboards. Data-enabled initiatives such as Apollo Agriculture and FarmDrive, which use machine learning to assess risk and direct resources to smallholder farmers, have attracted significant donor and investor support precisely because their impacts are legible, scalable, and measurable. By contrast, grassroots, informal and community-based giving systems like burial societies, rotating savings clubs, faith-based networks and neighbourhood mutual-aid groups- remain largely excluded, not because they lack impact, but because they do not produce the kinds of data that AI-optimised funding increasingly rewards. Well-connected NGOs and platforms with digital infrastructure, English-language reporting capacity, and international networks are therefore better positioned to attract resources, while long-standing local safety nets risk being rendered invisible.
According to Everisto Benyera, “this fast-evolving technology and its consequences created two sets of global citizens – those with technology and those without technology…These can be called digi-privileged and the digi-deprived. The lives of the digi-haves or digiprivileged will obviously be enhanced by the Fourth Industrial Revolution (4IR), while the opposite is true for the digi-prived. Roughly mapped, the digi-privileged are in the Global North while the digi-prived are in the Global South.” Hence, if this is left unaddressed, the trajectory privileges scale over context, efficiency over empathy, and metrics over meaning, gradually shifting African philanthropy away from collective responsibility and toward algorithmic optimisation.
Another critical issue is agenda-setting. AI systems are not neutral; they reflect the assumptions and priorities of their creators. When philanthropic AI tools are developed in Silicon Valley or European research labs, they often embody Western notions of impact, accountability, and success. For example, AI models may prioritise interventions that produce quick, measurable outcomes, such as school attendance or vaccination rates. While these are important, they may overlook slower, culturally embedded processes like community healing, intergenerational knowledge transfer, or social cohesion—areas where African philanthropy has traditionally played a vital role in maintaining.
Moreover, the increasing involvement of big tech firms in social impact spaces raises concerns about influence. Technology companies that provide AI tools to philanthropic organisations may shape funding priorities in subtle ways, steering attention toward problems that align with their platforms or data interests.
In response to these challenges, African scholars, activists, and technologists are calling for a decolonial approach to data and AI. Decoloniality of data emphasises local ownership, contextual knowledge, ethical governance, and community consent. Applied to philanthropy, this approach asks not only how AI can be used, but who controls it and for what purpose.
Still, voices from the continent are trying to steer things in a different direction. Tech entrepreneurs and scholars argue that Africa must take the lead in shaping its own AI agenda. Chido Dzinotyiwei, founder of Vambo AI, illustrates how African innovation can be both technologically advanced and deeply people-centred. She explains that the platform was inspired by early experiences with language barriers and is designed to improve communication by bridging linguistic gaps. Vambo AI uses artificial intelligence to build connections and remove communication barriers for African users, demonstrating how locally driven AI can reflect African realities and values.
However, a central question remains: can Ubuntu-centred philanthropy coexist with AI? Ubuntu emphasises relationality: “I am because we are.” At first glance, this may seem incompatible with algorithmic systems that reduce people to data points. Yet technology itself is not the enemy; rather, it is how it is used. AI could, for example, support community-led decision-making by analysing locally generated data under community control. It could help map informal support networks, identify overlooked vulnerabilities, or enhance transparency in youth-led fundraising initiatives. When guided by African ethical frameworks, AI could amplify, rather than replace, communal values. However, this requires intentional choices. African philanthropic actors must be involved not only as users but as designers, regulators, and owners of AI systems. Youth, in particular, have a critical role to play not just as beneficiaries of philanthropy, but as innovators shaping its future.
African youth are already demonstrating how this might look. At tech conferences and startup incubators across Lagos, Nairobi, or Johannesburg, teams celebrate breakthroughs with music, dance, and food – a reminder that technology has a culture. They insist that apps respect Ubuntu: one emerging motto is that any system deployed in Africa must first serve the community. This spirit of innovation-without-alienation is crucial.
Ultimately, whether philanthropy “remains African” will come down to who writes the code and who is invited into those rooms in the first place. If African leaders, activists, and technologists who understand not only AI but also the realities of digital privilege and data decoloniality have a genuine voice, these tools can enhance giving without erasing ubuntu. Such voices recognise that access to data, connectivity, and technical power is uneven, and that technology can deepen inequality if those gaps are ignored. But if decisions are made far away, by algorithms built on foreign servers and designed without sensitivity to local contexts, the communal essence of African giving may fray. Only by rooting digital tools in shared values, the same values embodied in harambee and ubuntu, and by consciously accounting for digital privilege can AI become a force for African solidarity rather than its undoing.