In order to improve agricultural production in Africa, it is important to improve the accuracy of weather forecasts available to small scale farmers. Agriculture in Africa is mainly rain fed and accurate rainfall prediction is likely to improve yields by allowing farmers to appropriately time activities such as planting and also help them select appropriate crops to grow.

Goal: In this project we will leverage weather data collected by large networks of sensors to improve weather prediction by linking these data with new artificial intelligence based weather prediction models running on powerful computers. The key goal is to provide accurate medium to long range localised precipitation forecasts.

Partners: This work will bring together Data Science Africa (DSA), the Centre for Data Science and Artificial Intelligence (DSAIL) at Dedan Kimathi University of Technology in Kenya, Amini, a Kenyan climate startup and Communal Shamba Coffee.

About 40% of the global crop production is lost to pests. Sub-Saharan Africa is most vulnerable to the increasing risks of pests and diseases spreading in agriculture. The current methods of disease identification and diagnosis involve experts traveling to disparate parts of the country and visually scoring the plants by looking at the disease symptoms manifested on the leaves.

Goal: The research project investigates a 3-D printed smartphone add-on spectrometer that determines the state of disease in plants before it is visibly symptomatic. Portable devices for the early detection of crop diseases are needed to support the farmers and experts working in the field. The output of this tool is integrated into a smartphone in the form of an app, making it accessible for use in the field in real applications.

Partners: This project will be executed by Data Science Africa (DSA), and ETH Zurich.

The International Committee of the Red Cross (ICRC) seeks to leverage Large Language Models (LLMs) to enhance its humanitarian work. Challenges like the bias of existing models, the underrepresentation of humanitarian contexts in commercial AI training sets, and the sensitivity of data related to conflicts limit the adoption of off-the-shelf AI models.

Goal: To address these constraints, the ICRC will investigate the responsible development of LLMs specifically tailored to its mandate on international humanitarian law and in line with its protection work in favor of people affected by armed conflicts. Ethical and accountability considerations will be at the center of this project, with the development of tools to assess if developed models are safe to be used for humanitarian applications.

Partners: This project will be executed by ICRC, ETH Zurich, and EPFL.

Artificial Intelligence (AI) is transforming the world at an unprecedented pace. However, across Africa, a significant gap exists between AI innovators and the communities intended to benefit from these technologies. While interest in AI is growing, many societies still lack the foundational knowledge, skills, and infrastructure needed to engage meaningfully with it. This disconnect poses a challenge to digital inclusion and risks deepening existing inequalities.

The Busitema University AI and Interdisciplinary Research Group is working to bridge this gap by engaging diverse stakeholders across the education spectrum, from secondary schools and tertiary institutions to universities and policymakers. Through targeted training and collaboration, our initiative aims to empower individuals and institutions to harness AI for education and innovation, ultimately building technologies that address African challenges and improve livelihoods.

The AI Driving License is a gamified, open-source education initiative within ICAIN's Education Pillar that empowers citizens — especially young people — to build essential AI literacy for democratic participation in a rapidly digitalizing world. Through an interactive card game available in four languages, players explore core AI concepts such as machine learning, bias, data ethics, and automation while engaging in real-world scenarios and ethical dilemmas.

By progressing from “AI Novice” to “AI Expert,” participants develop critical thinking, awareness of AI’s societal implications, and the ability to navigate and shape AI-driven information ecosystems. The project includes pilot workshops across Switzerland and a train-the-trainer program to support scalable, responsible AI education.