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IEEE Humanitarian Technologies (HT) is a consortium of programs and initiatives-supported by a global network of volunteers and technical professionals-working together to apply technology to solve the world's most pressing problems.

A farming family in Bangladesh receives word that a severe weather event is coming. They have hours, not days, to prepar...
06/01/2026

A farming family in Bangladesh receives word that a severe weather event is coming. They have hours, not days, to prepare. Without reliable guidance on what to do — whether to harvest early, move livestock, or protect their seedlings — the decision falls entirely on them. For families with no labor protection, no financial safety net, and no access to timely information, the cost of getting it wrong creates a cascade effect: lost income, disrupted access to healthcare, and a recovery that takes years.

This is the reality that drives the CHP Team and the problem at the heart of our final
finalist spotlight. Dr. Shadi Saleh and Ramzi Halabi have spent years working at this intersection, and they're not building from theory.

At the American University of Beirut's Global Health Institute, Dr. Shadi and Ramzi have spent years at the intersection of climate and health equity. Through their Cli-Health Program, they've seen firsthand how climate shocks affect harvests and the people who tend them. Climate risk, as they put it, is not only an environmental issue. It is a major determinant of health. And the populations most exposed to it are the least equipped to respond.

That framing sets the CHP Team apart. They're building for Bangladesh, but their lens is broader: a world where climate data is abundant, yet the families who need it most are still making high-stakes decisions in the dark.

Their tool would translate complex climate data into hyper-local, actionable guidance grounded in official national guidelines, delivered through lightweight chat interfaces over familiar channels, and designed to work in low-bandwidth environments with simple, step-by-step language. Every response would include clear low-confidence warnings for uncertain forecasts, so that farmers know exactly how much to trust what they're reading.

For the CHP Team, success would mean farmers and agricultural workers are receiving timely guidance, adjusting their practices, and reducing losses from extreme weather events. It would mean the solution has been adopted by regional partners and is reaching more people across more countries. And it would mean farmers trust it because it proved itself when it mattered most.

The winner announcement is coming! Follow the series and stay updated on the Challenge: https://bit.ly/4njszau

Smallholder farmers produce up to 80% of the food in Lesotho, yet nearly half the country's population still lives below...
05/25/2026

Smallholder farmers produce up to 80% of the food in Lesotho, yet nearly half the country's population still lives below the food poverty line. For Reitumetse, T'sepo, and the AgriConnect team, those two facts have always lived side by side—and the distance between them is precisely what they're trying to close.

Continuing the finalist spotlight series, this week we turn to the third and final team building for the Agriculture use case in Lesotho.

Reitumetse Sehloho and T'sepo Thamae grew up in and work within the country they're building for. They've watched farmers lose crops to unpredictable weather, pest outbreaks, and the absence of timely guidance — losses that, as they put it, are often preventable. The difference between a successful harvest and a failed one frequently comes down to a single thing: access to the right information at the right time. That proximity to the problem is the foundation of their work.

Climate variability, rising food prices, and active disease outbreaks, including Foot-and-Mouth Disease and Lumpy Skin Disease, are intensifying pressure on the farmers who already have the least access to decision-support tools.

AgriConnect is working to build a generative AI tool designed for the realities farmers actually face: limited connectivity, low-cost devices, and varying levels of digital literacy. The tool would evolve based on farmer feedback, remaining relevant as conditions on the ground change. As they put it: "Technology should adapt to people, not expect people to adapt to technology."

For AgriConnect, success would mean farmers across the country confidently using AI-powered tools to guide their decisions. They'd also hope to see young innovators across Lesotho inspired to explore how generative AI can solve real national challenges.

If they get this right, it won't just strengthen harvests. It will demonstrate that the most meaningful technological progress is the kind built by the people closest to the problem, for the people who need it most.

Next week, we shift to the teams working on the Extreme Weather Early Warning System use case in Bangladesh, where the data exists, and the challenge is getting it to the people who need it before it's too late. Stay tuned.

Follow the series and stay updated on the Challenge: https://bit.ly/4njszau

Bangladesh is one of the most climate-vulnerable countries in the world. Left unaddressed, climate change could cost the...
05/25/2026

Bangladesh is one of the most climate-vulnerable countries in the world. Left unaddressed, climate change could cost the country's agricultural sector up to $7.7 billion per year — a crisis felt most acutely by the more than 45% of the population whose livelihoods depend on it. Bruno Galdos and José Pastor know that story well — not from Bangladesh, but from home.

As we move into the final use case of our finalist spotlight series, we turn to Polisense AI — one of three teams building the Extreme Weather Early Warning System in Bangladesh.

Bruno and José come from Peru, a country with a long agricultural tradition repeatedly disrupted by El Niño events. In the past, those disruptions were difficult to predict and nearly impossible to prepare for. What changed is the data. The tools to model the environment and act preventively now exist — satellite imagery, geospatial layers, and climate models. What hasn't kept pace is translating that data into guidance that reaches the people who need it most before the damage is done. That gap is what brought them to the Challenge.

Polisense AI is working to build a generative AI tool that would draw on existing geospatial and climate data to generate actionable early warning guidance for farmers, delivered through accessible channels, and designed to reach people with varying levels of digital literacy in time to act. Their approach would build on open-source infrastructure and existing research, so that whatever they develop can be carried forward by local institutions and partners beyond the initial deployment.

"We want to build on top of existing research and open-source stack," they write, "so that all of our contributions rest on institutions and teams that can take it forward."

For Polisense AI, doing this right means accuracy, impact, and prevention — in that order. Every response would be grounded in verified data, with responsible AI principles and human-in-the-loop mechanisms built in. Technology, as they put it, is not an end in itself. The human is.

Success for Polisense AI would mean their solution is being used across multiple countries. It would mean hearing firsthand from people whose lives and livelihoods changed because an early warning reached them in time.

If they get this right, it won't just protect harvests. It will demonstrate that the data the world already has, pointed in the right direction, is enough to change outcomes.

Follow the series and stay updated on the Challenge: https://bit.ly/4njszau

Attention IEEE Young Professionals: join the new Tech for Impact Community! A space to connect with peers, exchange idea...
05/22/2026

Attention IEEE Young Professionals: join the new Tech for Impact Community! A space to connect with peers, exchange ideas, and collaborate on tech‑driven solutions that create real‑world impact.

Scan the QR code to get involved and help shape the future of technology for social impact.

We are pleased to share the 2025 IEEE Humanitarian Technologies Annual Report: A Year of Momentum. https://ieeeht.org/im...
05/19/2026

We are pleased to share the 2025 IEEE Humanitarian Technologies Annual Report: A Year of Momentum.

https://ieeeht.org/impact/

IEEE HT is not simply a funding body or a network of good intentions. It is an organization that asks the harder questions early, that sits with engineers and communities and implementation partners together, and that treats deployment not as a finish line but as a beginning.

The communities served, and the thousands of volunteers, engineers, and partners who show up for them, deserve to have their story told with the same rigor brought to the work itself. That conviction shapes everything you’ll find in this Annual Report.

To our volunteers, partners, program leaders, and supporters: thank you for all that you do!

In the Barind region of Bangladesh, the traditional signs farmers have relied on for generations to decide when to plant...
05/18/2026

In the Barind region of Bangladesh, the traditional signs farmers have relied on for generations to decide when to plant and when to irrigate are no longer reliable. The weather has become a guessing game, and in a drought-prone region where a single missed irrigation window can mean a total crop loss, that uncertainty carries real consequences.

This is the reality that drew Andri Pranolo and the Barind-Copilot team to the —and the problem they're working to solve.

Satellite data exists. National datasets exist. But as the team puts it, that data is often trapped in technical formats that don't help farmers. By the time a traditional advisory reaches a rural area, the window to save a crop may have already closed.

Barind-Copilot is working to build a generative AI tool that would translate high-level climate data into simple, actionable guidance—delivered through WhatsApp and audio summaries, designed for fragmented connectivity and varying levels of digital literacy. The kind of tool that answers the question a farmer actually needs answered: "Should I irrigate this week?"

Their approach would ground every response in verified agronomic knowledge and real-time satellite data, with human-in-the-loop safeguards for high-risk queries. Trust is built through transparency, reliability, and the involvement of local extension officers who review critical recommendations before they reach farmers.

"Seeing a farmer make confident decisions about their crops amidst the unpredictability of the weather is what makes this work meaningful."

Success for Barind-Copilot would look like farmers regularly using the system to make better irrigation, planting, and crop-protection decisions.

If they get this right, it won't just help farmers navigate an unpredictable season. It will give them back something climate change has taken: the confidence to plan ahead.

Follow the series and stay updated on the Challenge: https://bit.ly/4njszau

The deadline to submit an entry for the Response Quest™ Challenge is coming soon!Don’t miss out on your chance to make...
05/13/2026

The deadline to submit an entry for the Response Quest™ Challenge is coming soon!

Don’t miss out on your chance to make a real-world impact by helping to advance emergency intelligence.

Submit your concept by 29 May: https://bit.ly/4eeJZTu

"The stakes of getting AI-driven agricultural advice wrong are not abstract—they are life and death."For Manali, Ayush, ...
05/04/2026

"The stakes of getting AI-driven agricultural advice wrong are not abstract—they are life and death."

For Manali, Ayush, and Buvi, those words come from experience.

This week's spotlight turns to Inko, the second of three teams building for the Agriculture use case in Lesotho.

While building a voice bot for farmers in Uttar Pradesh, India, the Inko team encountered something that reframed everything. The farmer su***de rates in the region were alarmingly high—a misidentified crop disease, a wrong remedy, an entire harvest lost.

For a family with no alternative income, that loss can be catastrophic. That experience shaped not just their technical standards, but their understanding of what this work is actually about. As they put it, the urgency is not just technical. It is deeply human.

Each team member brings something essential to that commitment. Manali leads solution architecture and data pipeline design, with deep experience testing for hallucinations and ensuring output quality. Ayush owns the technical pipeline, focusing on scalability, tool selection, and implementation strategies that keep answers accurate and well-grounded. Buvi handles the critical groundwork of structuring and segregating datasets so that when data is vectorized for their RAG pipelines, the AI has minimal room for interpretation or guesswork.

In agriculture, inaccurate advice doesn't produce a bad user experience. It can destroy a harvest and devastate a family.

Inko is working to build a generative AI advisory tool for Lesotho that would give farmers access to timely, reliable guidance—from understanding upcoming weather patterns to planning crop cycles—through low-tech channels like SMS and voice, designed for low-bandwidth environments and limited connectivity. Every response would be grounded in verified, structured data, with human oversight built into the workflow from the start.

"Trust in AI must be earned, not assumed," they write. Their system would be designed with verification at every layer, giving agricultural advisors the ability to validate responses before they reach farmers, and ensuring users always trust their own judgment above an AI-generated answer.

The impact that would make them proud would be the moment a farmer in Lesotho gets the right advice at the right time and makes a decision that protects their harvest, their family, and their future.

If they get this right, it won't just change what farmers know. It will change what becomes possible for the families who depend on them.

Follow the series and stay updated on the Challenge: https://bit.ly/4njszau

In agriculture, a single wrong piece of AI-generated advice can destroy months of labor and devastate a family's livelih...
04/27/2026

In agriculture, a single wrong piece of AI-generated advice can destroy months of labor and devastate a family's livelihood."

For Alice D**g, that's the north star that guides her team.

As part of the , we've been asking each of the nine finalist teams what drew them to this work. This week, we turn to the teams building for the Agriculture use case in Lesotho, where Alice and her co-lead Paul Hill lead the AgriPivot team.

Alice is an agricultural Extension specialist in rural Colorado and an AI/ML engineer—two worlds she has spent her career trying to bring together. Every week she watches producers carry significant risk on thin margins, and every week she thinks about the people who are usually last in line for innovation. The GenAI for Good Challenge gave her a unique chance to act on both.

What crystallized the urgency was a number: 1 to 4,000. That's one Extension Advisor for every 4,000 farmers in regions like Lesotho. Advisors are the human link between official knowledge and the field. Farmers rely on them for guidance on everything from crop disease to weather patterns, and at that ratio, most farmers never get the guidance they need in time.

AgriPivot is working to build a generative AI tool for Lesotho designed to give advisors fast access to accurate, officially vetted answers so they spend less time searching and more time in the field. The vision is for the AI to be largely invisible to the end farmer, yet profoundly impactful to the people serving them.

Their commitment to accuracy would be absolute. Their system would be engineered to say "I don't know" rather than guess, and to always keep a human expert in the loop. In a context where a wrong recommendation could cost a family their harvest, anything less isn't responsible.

For AgriPivot, success wouldn't be a launch. It would be hearing an advisor in Lesotho say: "This tool saves us time and helps us give better advice on maize and beans"—and eventually seeing that advisor teach others how to use it without AgriPivot's involvement.

If they get this right, their solution will give the people who serve them back something they've been running out of: time.

Follow the series and stay updated on the Challenge: https://bit.ly/4njszau

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