A growing movement in Africa is leveraging artificial intelligence (AI) to address longstanding inequities in global health, aiming to shift power and ownership to those most affected by health challenges on the continent.
This grassroots effort, led by African researchers and data scientists, is not only developing innovative solutions to local health problems but also challenging the traditional power dynamics in global health research and funding.
Key Points:
- African-led organizations like Data Science Africa (DSA) are developing AI solutions to address local health challenges, emphasizing community involvement and collaboration.
- These initiatives aim to shift power dynamics in global health research, traditionally dominated by high-income countries.
- AI projects in Africa focus on end-to-end solutions, involving local communities from problem identification to implementation.
- Examples include BakiShule in Tanzania, which uses machine learning to improve education outcomes, and AirQo in Uganda, an AI-powered air quality management system.
- While AI alone cannot resolve all health inequalities, it can provide valuable tools when developed by and for African communities.
- Sustained funding and support for African-led AI initiatives are crucial for long-term impact and true decolonization of global health
Challenging Global Health Inequities Through AI
The field of global health has long been criticized for perpetuating colonial-era power imbalances. More than 70% of global health leaders are nationals from high-income countries, and major funders like the U.S. Agency for International Development (USAID) and Wellcome Trust primarily award grants to researchers and institutions in their own countries or other high-income nations.
This imbalance has led to calls for “decolonizing global health” by addressing the unequal power dynamics between those most affected by health problems and those who dictate research priorities and lead the field.
In response to these challenges, a movement is rising in Africa to use AI as a tool for decolonization in healthcare. Grassroots organizations, led by Africans for Africans, are developing AI solutions that address local health problems while emphasizing community involvement and collaboration. This approach not only produces more relevant and effective solutions but also helps shift power and ownership to those most affected by health issues in Africa.
Data Science Africa: A Model for African-Led Innovation
Data Science Africa (DSA), launched in 2015, exemplifies this grassroots approach to AI development in healthcare. DSA aims to build capacity and create local solutions to African problems using data science methods like machine learning and AI. The organization hosts annual events that bring together researchers from across the continent, creating a space for knowledge sharing and community building.
DSA’s approach emphasizes several principles that the global health field could emulate:
- Community involvement: AI solutions are developed with input and participation from local community members throughout the entire process.
- Collaboration: DSA partners with local universities and similar initiatives like Deep Learning Indaba to foster a collaborative ecosystem.
- Valuing African expertise: The organization recognizes and elevates the expertise of African researchers and community members in addressing local health challenges.
AI Solutions Addressing African Health Challenges
Several projects emerging from the DSA community demonstrate the potential of AI to address health-related issues in Africa:
BakiShule: Developed by researchers at the Nelson Mandela African Institution of Science and Technology in Tanzania, this machine learning solution aims to improve education outcomes—a major determinant of health. BakiShule uses data on common causes of school dropouts to inform teachers and parents when a student is at risk, with input from local stakeholders on the tool’s design and usability.
AirQo: Researchers at Makerere University in Uganda created this end-to-end air quality management system to address air pollution, a significant environmental health risk. Launched in Kampala in 2018 and now implemented in eight African cities, AirQo involves extensive community engagement, including public-friendly data platforms and awareness sessions conducted in local languages.
These projects showcase how AI solutions developed by and for African communities can effectively address local health challenges while ensuring community buy-in and support.
Challenges and the Path Forward
While AI offers promising tools for addressing health inequalities in Africa, it is not a panacea. The root causes of health inequities, including unjust distribution of resources and power, require broader systemic changes.
However, when developed by the right experts with appropriate resources, AI can provide valuable solutions to problems that shape health outcomes in African communities.
To sustain and expand the impact of African-led AI initiatives in healthcare, several key steps are necessary:
Long-term investment: Funders should ensure sustained support for human capacity building and computational infrastructure in African institutions.
Community trust: Organizations like DSA have developed social capital and community trust, which should be leveraged and expanded.
Empowering local institutions: Partnerships with local institutions should support and respect existing programs rather than undermining them.
As more African youth gravitate towards AI solutions, now is the critical time to support organizations focused on advancing equitable solutions and shifting power to those most affected by health inequities. By doing so, AI can be used not to maintain existing power imbalances in global health but to advance true health equity across the African continent.
Frequently Asked Questions (FAQ)
African-led organizations are developing AI solutions that address local health challenges while emphasizing community involvement and shifting power dynamics in global health research.
DSA is a grassroots organization launched in 2015 to develop capacity and local solutions to African problems using data science methods like machine learning and AI.
Examples include BakiShule in Tanzania, which uses machine learning to improve education outcomes, and AirQo in Uganda, an AI-powered air quality management system.
Challenges include the need for sustained funding, building local capacity, and addressing the root causes of health inequities beyond technological solutions.
Long-term investment in human capacity building and computational infrastructure, leveraging community trust, and empowering local institutions are key to sustaining these initiatives.
