Without environmental data, communities lack the power to challenge harmful conditions or hold polluters accountable. This reality reflects a broader pattern across Africa and the Global South: environmental monitoring systems remain expensive, centralized, and controlled by institutions that often have little incentive to document harm. The absence of accessible environmental data isn’t just a technical gap. It represents a justice issue that determines who can participate in environmental governance and whose health concerns authorities take seriously.
The Traditional Model: Expensive, Centralized, Extractive
Alicia Olago, an environmental scientist and the senior product manager of sensors.AFRICA at Code for Africa, recalls the moment this challenge crystallized into action. Traditional environmental monitoring requires specialized equipment, trained technicians, and laboratory facilities. In many African contexts, these resources concentrate in capital cities or remain controlled by government agencies with limited capacity and competing priorities.
When monitoring does occur in marginalized communities, it often follows an extractive pattern. External researchers arrive, collect samples, conduct analysis elsewhere, and may or may not share findings with the people most affected. Communities become research subjects rather than participants in understanding their own environments.
This model creates information asymmetry that reinforces existing power imbalances. Industrial facilities and wealthy neighborhoods can commission private environmental assessments. Low-income communities, informal settlements, and rural areas cannot. When residents report pollution concerns, authorities can dismiss them as anecdotal. Without documented evidence, communities have no standing to file complaints, demand remediation, or advocate for policy change.
sensors.AFRICA: Inverting the Data Power Dynamic
Code for Africa’s sensors.AFRICA initiative, launched in 2016, addresses this imbalance by placing environmental monitoring capacity directly in the hands of affected communities. Rather than importing expensive infrastructure or maintaining centralized control, the initiative enables communities to deploy, maintain, and own their environmental data.
The technical approach centers on affordability and accessibility. sensors.AFRICA uses low-cost air quality sensors that measure particulate matter (PM2.5), temperature, and humidity. These devices connect through cellular IoT networks and run on solar power, eliminating dependence on unreliable electricity grids. The system sends data to open-access platforms where anyone can view real-time and historical measurements.
But the innovation extends beyond hardware specifications. sensors.AFRICA fundamentally reconceives who controls environmental monitoring and how communities participate in the process.
Participatory Mapping: Communities Define What Matters
The initiative begins with what Code for Africa calls “entry through champions”, identifying community leaders, NGOs, or passionate residents who serve as bridges between the technical team and the broader community. These champions help organize participatory mapping sessions where community members gather around printed maps of their neighborhoods, created using open-source OpenStreetMap data.
During these sessions, residents identify pollution sources, vulnerable areas, and priority concerns. The process reveals perspectives that outside experts would never capture. Women highlight different pollution sources than men. Elderly residents point to health impacts accumulating over decades. Youth identify environmental changes over time. The participatory approach ensures that sensor placement reflects community knowledge rather than external assumptions about where monitoring matters most.
This methodology recognizes environmental data collection as inherently political. Decisions about where to place sensors, what to measure, and how to interpret findings all involve value judgments. sensors.AFRICA makes these decisions transparent and community-driven rather than technical and opaque.
Technical Design Choices as Justice Choices
The system’s technical specifications respond directly to infrastructure realities and community needs. Solar power addresses unreliable electricity supply in informal settlements and rural areas. Multi-network SIM cards ensure connectivity even in marginalized neighborhoods where single-provider coverage proves inconsistent. SMS alerts allow sensor hosts without smartphones to receive notifications about concerning readings.
These design choices reflect more than practical problem-solving. They represent decisions about who can participate in environmental monitoring. A system requiring stable electricity, reliable internet, and smartphone access would exclude the communities most vulnerable to environmental harm. By designing for the constraints that characterize marginalized contexts, sensors.AFRICA ensures that data collection capacity reaches those who need it most.
The platform also prioritizes data openness through APIs, visual dashboards, and downloadable reports. Anyone can access sensor readings, track pollution patterns, or integrate the data into research and advocacy. This transparency contrasts sharply with proprietary monitoring systems where access requires payment or institutional affiliation.
From Data to Advocacy: Real Impact on Environmental Justice
The initiative’s value becomes clearest through documented cases where communities used sensor data to challenge harmful conditions and influence policy.
In Syokimau, residents used sensors.AFRICA data documenting dangerous air quality levels to support their advocacy. The evidence contributed to a 32-minute feature on Kenya’s Citizen TV, bringing national attention to pollution from industrial facilities near residential areas. Community members presented sensor data to Kenya’s National Environmental Tribunal, transforming anecdotal complaints into documented violations that authorities could not ignore.
In Mukuru, another Nairobi informal settlement, sensor data revealed persistent air quality levels that exceeded World Health Organization guidelines. Health workers at local clinics documented that approximately 60 patients per month required treatment for asthma-related complications. The correlation between sensor readings and health impacts provided evidence for targeted interventions and policy discussions about industrial zoning.
In rural Tanzania, fishing communities used sensors.AFRICA for different but equally important purposes. Weather pattern changes affected fishing seasons and safety. The system’s AI-powered early warning capabilities, analyzing historical data to predict pollution events or weather conditions, gave communities advance notice that could inform livelihood decisions and safety measures.
AI That Amplifies Rather Than Replaces
sensors.AFRICA integrates artificial intelligence, but in ways that support rather than substitute for community knowledge. AI algorithms analyze sensor data to identify patterns, predict pollution events, and generate alerts when readings exceed safety thresholds. Machine learning helps fill gaps in monitoring coverage by extrapolating data across geographic areas where sensor density remains limited.
Critically, the AI component enhances community capacity rather than centralizing control with technical experts. Visual outputs display patterns in accessible formats. Alerts go directly to sensor hosts and community members. The system prioritizes explainability over sophistication, residents need to understand why they receive warnings and what the data indicates.
This approach reflects broader principles about AI deployment in communities. Technology should augment local knowledge, not displace it. Technical systems should make expertise more accessible, not create new forms of dependence on outside specialists. When community members understand how AI generates predictions, they can contextualize findings with lived experience and environmental knowledge that sensors cannot capture.
Ethical Challenges: Privacy and Protection
Documenting environmental harm involves risks, particularly when pollution sources have economic or political power. Sensor hosts, community members who agree to have devices installed at their homes or facilities, can face pressure or retaliation from industrial actors or local authorities who prefer that pollution remain undocumented.
sensors.AFRICA addresses these concerns through careful attention to privacy and anonymization. The system protects sensor host identities, especially when documenting pollution from powerful industrial actors. Data collection protocols require informed consent. Community members control whether and how to share findings.
These safeguards recognize that environmental data collection in marginalized communities isn’t neutral scientific observation. It represents political action that challenges existing power structures. The initiative’s ethical framework ensures that communities can document harm without exposing vulnerable individuals to additional risk.
The Human Rights Framework: Beyond Ethics to Accountability
sensors.AFRICA demonstrates what distinguishes human rights-based approaches from abstract ethical principles. Human rights provide legally binding frameworks with specific obligations and enforcement mechanisms. When environmental monitoring connects to the right to health, the right to information, or the right to participate in environmental governance, it moves from voluntary best practices to accountable standards.
The African AI & Equality Toolbox, which documents the sensors.AFRICA case study, integrates Human Rights Impact Assessment (HRIA) throughout the AI lifecycle. This methodology requires considering human rights implications at every stage, from initial objective-setting through deployment and ongoing monitoring.
For sensors.AFRICA, this meant:
Stage 1 (Objective + Team): Environmental justice recognized as a human rights issue from the outset. Community needs, not external donor priorities, defined objectives. Sensor hosts, local NGOs, and residents participated as core stakeholders with genuine decision-making power.
Stage 2 (System Requirements): Technical specifications emerged from participatory mapping rather than external assumptions. Requirements included off-grid capability, anonymization for sensor host safety, and multiple data access methods to ensure communities could actually use the information.
Stage 3 (Data Discovery): The process validated community knowledge as equal to technical measurements. Participatory mapping identified pollution sources through lived experience. Communities defined what data mattered and how to interpret it. All documentation and findings were shared transparently.
Stage 4 (Model Development): AI development prioritized explainability over technical sophistication. Visual outputs used local languages. Models enhanced rather than replaced community environmental knowledge. Development remained iterative with ongoing community input.
Stage 5 (Testing & Interpretation): Testing occurred in real community contexts. Training materials used local languages and visual aids. Community members interpreted findings and decided how to act on them. Success metrics included empowerment and agency, not just technical performance.
Stage 6 (Deployment & Monitoring): Communities retained power to delay or modify deployment. The system included safety protocols for sensor hosts and continuous adaptation based on community feedback. Long-term sustainability depended on local capacity rather than external technical dependence.
Lessons for Environmental Monitoring and AI Deployment
The sensors.AFRICA initiative offers five documented lessons for organizations working at the intersection of technology, environment, and justice:
Data ownership determines data justice. Open data alone doesn’t ensure justice. When communities control collection, interpretation, and use, data becomes a tool for empowerment rather than extraction. Technical openness means little if communities lack capacity to access or act on information.
Participation must be genuine, not performative. Consultation differs from co-creation. sensors.AFRICA succeeded because communities participated in decisions from objective-setting through deployment. Participatory mapping gave residents real influence over what to measure and where to monitor. Tokenistic engagement would have produced a system that served external priorities rather than community needs.
Technical choices are political choices. Decisions about solar power, connectivity, SMS alerts, and anonymization reflected commitments to reach marginalized communities and protect vulnerable sensor hosts. These weren’t neutral technical specifications. They determined who could participate and how much risk communities faced.
AI should amplify, not replace. Machine learning added value by predicting pollution events and filling monitoring gaps. But the system positioned AI as supporting community knowledge, not substituting for it. Residents interpreted findings, decided how to act, and maintained ultimate authority over environmental governance.
Environmental data is infrastructure for justice. Like roads or electricity, environmental monitoring capacity should reach everyone, particularly communities facing the greatest exposure. Making this infrastructure accessible, affordable, and community-controlled enables marginalized populations to participate in environmental governance that affects their daily lives.
Beyond Monitoring: Data as a Foundation for Dignity
Environmental data collection might seem far removed from questions of human dignity and rights. But for communities whose health concerns authorities dismiss without evidence, for residents whose children develop respiratory illnesses from undocumented pollution, for populations whose environmental knowledge experts ignore, the ability to generate credible data about one’s own environment represents something fundamental.
sensors.AFRICA demonstrates that when communities control environmental monitoring, data collection becomes more than technical measurement. It becomes a tool for agency, a foundation for advocacy, and evidence that transforms lived experience into documented reality that institutions must acknowledge.
The initiative proves that building AI systems grounded in human rights and community ownership isn’t just possible, it’s practical. The technology works. Communities use it. The data supports concrete advocacy and policy change. Real people benefit from improved environmental governance.
This model offers an alternative to both technological solutionism and cynicism about technology’s role in marginalized communities. sensors.AFRICA shows that AI can serve environmental justice when communities shape the process from the beginning, when technical design responds to real constraints, and when the goal is empowerment rather than extraction.
For organizations, policymakers, and technologists working on environmental monitoring in Africa and beyond, the lesson is clear: environmental data becomes a tool for justice only when communities can access, understand, control, and use it. That requires more than open data platforms or low-cost sensors. It demands genuine participation, technical choices that prioritize accessibility, ethical safeguards that protect vulnerable populations, and recognition that environmental monitoring is never neutral—it either challenges or reinforces existing power structures.
sensors.AFRICA chose to challenge those structures by putting environmental data where it belongs: in the hands of the communities whose health, safety, and futures depend on it.
This article is based on the second webinar of the Africa AI & Equality Toolbox, a collaboration between the AI & Equality Initiative and the African Centre for Technology Studies (ACTS) in Kenya. The African Toolbox builds upon the methodology of the Global AI & Equality Human Rights Toolbox, an initiative of Women At the Table in collaboration with the United Nations Office of the High Commissioner for Human Rights (OHCHR).