What Good AI Governance for Public Health Actually Requires

What Good AI Governance for Public Health Actually Requires

🎙️ Episode Title What Good AI Governance for Public Health Actually Requires --- 🧠 Episode Summary Anne Makena has spent nearly a decade building equitable research partnerships between Oxford and African institutions, co-creating the African AI Governance Index, and launching Research Alpha to strengthen the evidence base behind AI investment in health. In this episode she brings all of that experience to bear on a single question: what does it actually take to deploy AI responsibly in public health? Her answer is grounded and unsparing. The governance conversation is already happening across Africa — the problem is whether anyone is listening. AI tools are being deployed without asking what problem they are solving, whether the data reflects the context, or who is accountable when things go wrong. And the innovation ecosystem, she argues, is being built on a borrowed mattress in a rented house: without a strong research and evidence base, no tool will sustain. The episode closes with a message of cautious optimism. The resources, the frameworks, and the talent exist. What is needed now is the humility to ask the right questions — starting with the people the tools are meant to serve. --- 💬 Guest Anne Makena is founder and CEO of Research Alpha, a platform providing scientific advisory and technical due diligence for Africa-focused capital allocators. She is Director of Partnerships and Development at the University of Global Health Equity in Rwanda, and Senior Adviser to the Africa Oxford Initiative at the University of Oxford, where she spent nearly a decade as Co-Director building equitable research and innovation partnerships across more than 30 African countries. She is co-creator of the African AI Governance Index, which tracks AI governance readiness across all 55 African Union member states. --- 🌐 Resources and References - Research Alpha: https://researchalpha.ai/ - Research Alpha RAIN Fellowship Programme**:** https://researchalpha.ai/research-industry-collaborations - University of Global Health Equity (UGHE): https://ughe.org/ - Africa Oxford Initiative (AfOx): https://www.afox.ox.ac.uk/ - Partners in Health: https://www.pih.org/ - Institute of Development Studies, Sussex: https://www.ids.ac.uk/ - STANDING Together (data diversity standards): https://www.datadiversity.org/ - World Health Organization regulatory maturity (Global Benchmarking Tool): https://www.who.int/ - African AI Governance Index: https://africanaigovernanceindex.com - Africa's deep tech boom rests on a fragile research ecosystem: https://www.nature.com/articles/d44148-026-00016-z - Africa AI Council: https://digitalhealth-africa.org/smart-africa-endorses-africa-ai-council/ - Real world trials of AI-enabled clinical decisions support tools in Africa: https://www.nature.com/articles/s41591-026-04503-6 --- 🎵 Music Credits Intro and outro music from Async Stock Audio. Track: 'Nairobi Nights'. License code: JKVN8JFYPINF4EKW. --- ⚠️ Disclaimer This podcast is produced by the World Health Organization (WHO) as part of the Pandemic and Epidemic Intelligence Innovation Forum initiative: https://pandemichub.who.int/news-room/innovation-forum. The views expressed by guests are their own and do not necessarily represent those of WHO or its affiliates. Content is intended for informational purposes only and does not constitute professional medical advice. --- 📲 Listen and Subscribe The Innovation Forum AI Podcast is available on YouTube, Spotify, Apple Podcasts, and Amazon Music. Follow, rate, and share to help us reach more public health professionals exploring the future of AI.

From Genomes to Governance: Lessons from India on Building AI-Ready Public Health Systems

🎙️ Episode Title From Genomes to Governance: Lessons from India on Building AI-Ready Public Health Systems --- 🧠 Episode Summary In this episode of The Innovation Forum AI Podcast, Oliver Morgan speaks with Anurag Agrawal, Professor and Dean of Biosciences and Health Research at Ashoka University in New Delhi. A physician-scientist with deep experience in respiratory biology, genomics, and data-driven health governance, Anurag draws on his leadership of India's National Genomic Surveillance Network during COVID-19 and his role as Vice President of Science Policy at the Indian National Science Academy to reflect on what it takes to build public health intelligence systems that are technically robust and institutionally grounded. The conversation opens with the Delta wave: how the focus on tracking Alpha left Delta undetected until clinical impacts had begun, exposing the gap between having genomic data and integrating it with clinical information in real time. Anurag traces India's evolution from fragmented sequencing efforts to a coordinated national consortium, and is candid about what remains missing: integrated electronic health records and cohort data linked to genomic surveillance. On AI, he is precise about both the promise and the limits. Foundation models hold real potential for mutation impact prediction, but fail at the tails of the distribution — exactly where novel pathogens emerge. His response is architectural: keep sensitivity high, route alerts to domain experts as humans on the loop rather than merely in it, and build institutional capacity to act under uncertainty. The episode also covers the dual-use risks of AI in biology, world models as an alternative to pure pattern recognition, and India's governance framework built on risk-calibrated innovation. Anurag closes with a deliberately grounded answer: the most important investment is getting the small things right, and rebuilding global cooperation. --- 💬 Guest Anurag Agrawal is Professor and Dean of Biosciences and Health Research at Ashoka University in New Delhi. A physician-scientist trained in respiratory biology and genomics, he led India's National Genomic Surveillance Network (INSACOG) during the COVID-19 pandemic and chaired the WHO advisory group that developed the global framework for evaluating new SARS-CoV-2 variants. He currently serves as Vice President of Science Policy at the Indian National Science Academy. His work spans pathogen genomics, data infrastructure for public health, AI governance, and the responsible integration of AI into health systems in low- and middle-income country settings. He has been involved in initiatives including the Lancet Commission on Data Solidarity and India's national AI for health strategy. --- 🌐 Resources and References - Ashoka University, Biosciences and Health Research: https://www.ashoka.edu.in/ - INSACOG: https://dbt.gov.in/insacog/. I believe this is the right link but can´t seem to access it, please confirm which one is the right one. - Indian National Science Academy (INSA): https://www.insaindia.res.in/ - Ayushman Bharat Digital Mission: https://abdm.gov.in/ --- 🎵 Music Credits Intro and outro music from Async Stock Audio. Track: 'Nairobi Nights'. License code: DBHAXH7OM6OTIDCQ. --- ⚠️ Disclaimer This podcast is produced by the World Health Organization (WHO) as part of the Pandemic and Epidemic Intelligence Innovation Forum initiative: https://pandemichub.who.int/news-room/innovation-forum. The views expressed by guests are their own and do not necessarily represent those of WHO or its affiliates. Content is intended for informational purposes only and does not constitute professional medical advice. --- 📲 Listen and Subscribe The Innovation Forum AI Podcast is available on YouTube, Spotify, Apple Podcasts, and Amazon Music. You can find a written summary of this episode here: https://substack.com/@omorgan? Follow, rate, and share.

African Data for African Health: Building the AI Foundations the Continent Needs

🎙️ Episode Title African Data for African Health: Building the AI Foundations the Continent Needs --- 🧠 Episode Summary In this episode, Oliver Morgan speaks with Agnes Kiragga, Head of the Data Science Program at the African Population and Health Research Center in Nairobi, Kenya. Drawing on over two decades of experience building data systems and leading large-scale multi-country initiatives across the continent, Agnes reflects on what it takes to make AI work in African public health — not as a concept, but in practice, inside real institutions with real constraints. The conversation spans the full range of Agnes's work: from no-code AI platforms that bring real-time analytics to researchers without coding backgrounds, to AI-powered blood stock management systems in Cameroonian hospitals, to federated analysis platforms that allow data to be analyzed across borders without ever leaving its home institution. The episode addresses one of the most consequential gaps in global health AI: the underrepresentation of African datasets in the models being built for epidemic intelligence. Agnes argues that tools not trained on African data cannot capture the comorbidity burden, the linguistic diversity, or the epidemiological reality of African populations — and that the consequences range from missed diagnoses to wrong predictions during outbreaks. She also makes the case for a new generation of evaluation frameworks for large language models in African health contexts, where multi-turn, multilingual interactions are the norm, not the exception. Throughout, Agnes is clear-eyed about what is still missing — governance structures, infrastructure, skilled personnel — while pointing to the momentum building across the continent in training, tooling, and institutional capacity. Her core argument: AI for African health cannot be imported. It has to be built, evaluated, and owned from within. --- 💬 Guest Agnes Kiragga is Head of the Data Science Program at the APHRC in Nairobi, Kenya. With over 20 years of experience working with large and diverse health datasets across Africa, she leads the Data Science Without Borders project. She also leads the INSPIRE Network, a consortium of approximately 25 longitudinal Health and Demographic Surveillance System sites across Africa. Her work spans data harmonization, federated analysis, AI governance, and the responsible evaluation of AI tools in low- and middle-income country settings. She is a delivery partner for the Evidence for AI in Health initiative, which generates rigorous evidence on whether AI tools actually work in the contexts where they are deployed. --- 🌐 Resources and References - APHRC: https://aphrc.org/ - DSWB: https://dswb.africa; https://doi.org/10.3389/fpubh.2025.1695907 - INSPIRE Network: https://www.inspiredata.network/; https://aphrc.org/inspire/; https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2024.1329630/full - DataSHIELD: https://assets-eu.researchsquare.com/files/rs-9149238/v1/30a60413-de69-4c32-9237-577e7690944c.pdf?c=1776244497 - EVAH: https://www.povertyactionlab.org/initiative/evidence-ai-health-evah-initiative - AfriMed-QA: https://afrimedqa.com/ - OHDSI common data model: https://www.ohdsi.org/ - AIMS: https://aims.ac.za/ - Data Science Africa: https://www.datascienceafrica.org/ - Deep Learning Indaba: https://deeplearningindaba.com/ - IDRC AI for Development: https://www.idrc.ca/en/initiative/artificial-intelligence-development --- 🎵 Music Credits Track: ‘Nairobi Nights’. License code: WPJKGDX60OHV0I2I. --- ⚠️ Disclaimer This podcast is produced by the World Health Organization (WHO) as part of the Pandemic and Epidemic Intelligence Innovation Forum initiative. The views expressed by guests are their own and do not necessarily represent those of WHO or its affiliates. Content is intended for informational purposes only and does not constitute professional medical advice.

Beyond the Genome: Tracking Climate-Driven Epidemic Risk with AI

🎙️ Episode Title Beyond the Genome: Tracking Climate-Driven Epidemic Risk with AI --- 🧠 Episode Summary In this episode of The Innovation Forum AI Podcast, Oliver Morgan speaks with Houriiyah Tegally, Associate Professor and Head of Data Science at the Centre for Epidemic Response and Innovation (CERI), Stellenbosch University. Drawing on her work tracking viral pathogens across Africa — including a pivotal role in identifying the Beta and Omicron variants of concern during the COVID-19 pandemic — Houriiyah reflects on what it takes to move from genomic data to actionable epidemic intelligence, and why that challenge is becoming more urgent as the climate changes. The conversation traces how her team has built a global consortium to forecast the risk of climate-amplified diseases like dengue and chikungunya, and how AI is helping them integrate the disparate data streams — genomic, ecological, climatic, epidemiological — that this work requires. Houriiyah explains what phylogenetics and molecular clocks tell us about outbreaks, how machine learning is accelerating that analysis, and where it is introducing new risks: overfitting, unexplainable outputs, and models that perform well in one context and fail in another. The episode is frank about the limits of what AI can currently deliver. Houriiyah describes firsthand experiences with large language model hallucinations in scientific data curation, and argues that AI tools in epidemic intelligence are still best understood as a junior research assistant, not a domain expert. She makes the case that the most important investments are not in the technology itself, but in the ecosystem around it: infrastructure, local capacity, and the co-development of tools with the public health decision-makers who need to act on their outputs. --- 💬 Guest Houriiyah Tegally is an Associate Professor and Head of Data Science at the Centre for Epidemic Response and Innovation (CERI), Stellenbosch University. Her research focuses on the genomic epidemiology and evolution of emerging viral pathogens in South Africa and across the African continent. She uses genomic surveillance, phylodynamics, geospatial data, and machine learning to track and predict infectious disease outbreaks. During the COVID-19 pandemic, she led the phylodynamic analysis of SARS-CoV-2 genomes from multiple African countries, work that was pivotal in the identification of the Beta and Omicron variants of concern. She co-leads CLIMADE, an international consortium forecasting the transmission risks and outbreak potential of climate-amplified pathogens, and is involved in the African Institute for Mathematical Sciences (AIMS) AI for Science programme, which trains mathematicians across Africa to apply AI methods to real epidemic research problems. --- 🌐 Resources and References - Visit CERI: https://ceri.africa/ - CLIMADE — Climate Amplified Diseases and Epidemics: https://climade.health/ - CERI Data Science: https://ceri.africa/data-science/ - The GEM Newsletter: https://ceri.africa/the-gem/ - Alignment-free viral sequence classification at scale: https://link.springer.com/article/10.1186/s12864-025-11554-5 - Craft: a machine learning approach to dengue subtyping: https://academic.oup.com/bioinformaticsadvances/article/5/1/vbaf224/8275733 - African Institute for Mathematical Sciences (AIMS) AI for science program: https://ai.aims.ac.za/ --- 🎵 Music Credits Intro and outro music from Podcastle Stock Audio. Track: ‘Nairobi Nights’. License code: 8HLBOKOIASQ8R7GE. --- ⚠️ Disclaimer This podcast is produced by the World Health Organization (WHO) as part of the Pandemic and Epidemic Intelligence Innovation Forum initiative: https://pandemichub.who.int/news-room/innovation-forum. The views expressed by guests are their own and do not necessarily represent those of WHO or its affiliates. Content is intended for informational purposes only and does not constitute professional medical advice.

AI, qualitative data, and the case for statistical rigour

🎙️ Episode Title AI, qualitative data, and the case for statistical rigour --- 🧠 Episode Summary In this episode of The Innovation Forum AI Podcast, Oliver Morgan speaks with Adam Kucharski, Professor of Infectious Disease Epidemiology at the London School of Hygiene & Tropical Medicine and Co-Founder of WholeSum. Drawing on decades of experience working with messy, incomplete data — from contact surveys and outbreak investigations to real-time modelling during major epidemics — Adam reflects on a persistent frustration in public health: there is often far more information available than can be rigorously analysed. The conversation explores what AI tools now make possible for qualitative public health data — community narratives, open-ended survey responses, field reports — and what rigour actually requires before those outputs can be trusted. Adam explains key concepts including labelled training data and ground truth, and unpacks why collapsing human disagreement into a single consensus label can quietly undermine a model's usefulness. He also discusses the hidden assumptions embedded in AI models when they are applied in contexts different from where they were trained, and why understanding those assumptions matters as much as understanding the model's performance metrics. The episode closes with Adam's vision for tools that can go deeper into qualitative signals — not just classifying broad topics or sentiments, but extracting the underlying structures that link scattered observations to meaningful public health insights — while maintaining the reproducibility and statistical accountability that the field demands. --- 💬 Guest Adam Kucharski is a Professor of Infectious Disease Epidemiology at the London School of Hygiene & Tropical Medicine, where his research focuses on developing statistical and computational methods to extract reliable insights from incomplete and noisy data. He has advised multiple governments during outbreaks including Ebola and COVID-19, and has contributed to large-scale studies of social behaviour, population immunity, and transmission dynamics. He is also Co-Founder of WholeSum, a startup developing hybrid AI tools to bring statistical rigour to the analysis of qualitative text data at scale. --- 🌐 Resources and References - WholeSum: https://www.wholesum.tech/ - WholeSum pre-seed announcement: https://tech.eu/2026/01/05/wholesum-raises-730k-to-advance-qualitative-data-analysis-platform/ - When is the 'ground truth' not quite the whole truth?: https://kucharski.substack.com/p/when-is-the-ground-truth-not-quite - Inference of epidemic dynamics in the COVID-19 era and beyond (Cori & Kucharski, Epidemics, 2024): https://www.sciencedirect.com/science/article/pii/S1755436524000458 - How our concepts of what we can prove are shifting: https://www.theguardian.com/books/2025/mar/29/epidemiologist-adam-kucharski-proof-the-uncertain-science-of-uncertainty - Adam Kucharski's Substack — Understanding the Unseen: https://kucharski.substack.com --- 🎵 Music Credits Intro and outro music from Podcastle Stock Audio. Track: ‘Nairobi Nights’. License code: FJAMWGPDVHFKSGC2. --- ⚠️ Disclaimer This podcast is produced by the World Health Organization (WHO) as part of the Pandemic and Epidemic Intelligence Innovation Forum initiative: https://pandemichub.who.int/news-room/innovation-forum. The views expressed by guests are their own and do not necessarily represent those of WHO or its affiliates. Content is intended for informational purposes only and does not constitute professional medical advice. --- 📲 Listen and Subscribe The Innovation Forum AI Podcast is available on YouTube, Spotify, Apple Podcasts, and Amazon Music. You can find a written summary of this episode here: https://substack.com/@omorgan? Follow, rate, and share to help us reach more public health professionals exploring the future of AI.