Humanitarians look to put the AI in aid
AFP
July 9, 2026
From remote-controlled trucks delivering life-saving aid in dangerous settings to mobile phone data analysis flagging mass displacement, humanitarians are eyeing ways in which artificial intelligence can speed up and improve their operations.
There have been plenty of warnings about the dangers of AI for aid agencies, who face growing challenges of securing often extremely sensitive data and swelling misinformation about their operations and beneficiaries.
But at the AI for Good summit in Geneva this week, a handful of humanitarian-focused displays emphasised the technology’s positive potential.
Parked in one corner of a vast hall at the Palexpo conference centre was a giant white SHERP vehicle, resembling a hulking Martian rover, decked out with cameras and sensors and a drone landing-pad on the roof.
Made in Ukraine, SHERPs are amphibious vehicles that can float on water, drive through swamps and flooded rivers with their giant wheels, and climb over obstacles up to one metre (3.3 feet) high.
The UN’s World Food Programme is preparing to begin field-testing a version of the AI-enabled truck that can be steered remotely through the most dangerous and difficult terrain to reach people in need.
“I think this could be a game-changer,” Bernhard Kowatsch, head of WFP’s global accelerator and ventures innovation division, told AFP.
The technology, he said, “should allow us essentially to reach people that otherwise never would have been reachable”.
– Not possible without AI –
WFP already has drivers using SHERPs to deliver aid in Sudan, South Sudan and Uganda.
But after numerous heartbreaking losses of drivers, it tasked the German Aerospace Center (DLR) to help equip the vehicles with AI and other technologies, making it possible to control them remotely through particularly dangerous terrain.
The idea is to set up a shipping container control room in a safe area, where a human can remotely control the vehicle on the last, most treacherous leg of its journey.
Tests have been conducted in Germany, and will be tried out in the field in Uganda in 2028, said Armin Wedler, who is coordinating DLR’s Autonomous Humanitarian Emergency Aid Devices (AHEAD) project.
Standing next to the 2.8-metre high vehicle, he told AFP that the team had used “remote-control technologies which are based on mathematics and old-school… research”, but stressed: “We would not be able to process everything without using also AI”.
It would be possible to make the vehicle fully autonomous, Wedler said, but stressed that in complex humanitarian settings “we have to have a human in the loop”.
“We’re not talking about driving on clear streets with clear lanes. There are no streets,” he said, also describing scenes where aid trucks are suddenly swarmed by desperately hungry people.
“There’s no AI autonomous algorithms ever capable to handle that safely.”
– ‘Life-saving’ –
Among more than 200 exhibitors at the summit — showing off everything from humanoid robots to bionic prosthetics and emotional companions — the other humanitarian displays were more discreet, with pamphlets detailing how AI tools are boosting and streamlining operations.
Among them, the UN refugee agency detailed a new Legal Virtual AI Assistant for lawyers and legal officers representing refugees, enabling them to swiftly determine the rights available within country-specific legal frameworks.
Rebeca Moreno Jimenez, the lead data scientist at UNHCR’s Innovation Service, told AFP that building cases faster and more efficiently can be “life-saving for many refugees”.
Another UN initiative called Data Insights for Social and Humanitarian Action, or DISHA, relies on partnerships with private actors such as Google and McKinsey to provide humanitarian organisations with data and AI models to speed up and improve disaster responses.
One project uses AI analysis of anonymised mobile phone data to spot mass-population movements during disasters, determining where people are fleeing, to help humanitarians better tailor their response.
Another uses AI for rapid analysis of satellite images taken before and after disasters like last month’s earthquakes in Venezuela to determine building damage.
The aim is to give humanitarians “accurate information early enough to make better decisions (and) avoid going to the wrong place when there are people who need you somewhere else”, DISHA product lead Andreas Kortis told AFP.
Global AI industry falls short on safety, think tank warns
AFP
July 8, 2026
US artificial intelligence lab Anthropic scored the highest in a semiannual safety ranking, but globally the industry fails to combat “existential” threats, according to a report released on Tuesday.
Meta moved up two spots to fourth place, while xAI dropped three spots to seventh place, just ahead of China’s DeepSeek and France’s Mistral, which placed last, according to US-based AI safety think tank Future of Life Institute, which ranked nine of the world’s leading AI companies.
Seven researchers and governance experts determined the rankings based on public data and information provided by the companies.
They evaluated efforts across six distinct categories: risk assessment, current harms, safety frameworks, existential safety, governance and accountability, and information sharing.
No company received an “A” in any single category, while Anthropic got the best overall score of “C+.”
Mistral was included on the list for the first time, though when asked by AFP to comment on its last place, the company said the report’s framework isn’t suited for its approach to developing AI models.
The French company develops so-called open models, which allow users to download and modify them. Many of its competitors develop closed AI models — including Anthropic, OpenAI and Google DeepMind, which are also included in the report.
“I was very disappointed to find that they came last, especially since Europe has really…been a leader in AI safety,” Max Tegmark, an MIT professor and Future of Life president, told AFP.
“We reached out many, many times” but Mistral did not respond to the organization’s survey, Tegmark continued.
Alibaba, xAI and DeepSeek did not respond to its survey either, the organization said.
Three Chinese developers included in the report also produce open models and landed in the bottom half of the ranking: DeepSeek (fifth), Alibaba Cloud (sixth) and Z.ai (eighth).
– ‘Questionable’ practices –
The report noted that several companies that previously banned their technology from military uses have “gradually reversed course,” including Anthropic, which the report criticized for having “questionable military engagements.”
The US government used Anthropic’s technology in military operations in Venezuela and Iran over the past year, according to various media reports — though the company was subject to a recent ban by the Pentagon over disagreements on AI safety.
All nine companies are failing when it comes to combating “existential” threats such as pursuing models that reach human-level intelligence, known as “artificial general intelligence” or AGI, the report said.
Although “constructive attempts exist,” efforts across the board are “entirely inadequate.”
Other risks include the possible misuse of a model to carry out a cyberattack or perform tasks potentially harmful to humans.
Anthropic was thrust into the spotlight recently after it released its most powerful model yet, called Mythos.
In early April, the San Francisco-based company released Mythos only to a handful of trusted organizations due to its abilities to expose cyber safety vulnerabilities to bad actors.
However, by June 12 the US government blocked Anthropic from releasing Mythos to foreigners on national security grounds.
The Trump administration eventually lifted the ban a couple of weeks later on June 30.
To defend your software, first teach AI to break it
From the lab to the field and back, researchers and a former doctoral student develop artificial intelligence-driven tools to expose software vulnerabilities.
When Ying Zhang '23 was a doctoral student at Virginia Tech, she spent years learning to think like an attacker — probing software for the hidden weaknesses that developers miss and malicious actors exploit.
Now an assistant professor at Wake Forest University, Zhang has come back to that work and to the people who shaped it. She and her former doctoral advisors in the Department of Computer Science, Associate Professor Na Meng and Professor Danfeng "Daphne" Yao, have continued publishing influential research together, this time with a new cohort of Ph.D. students working alongside them.
The result is new research that posits that the best way for the cybersecurity community to guard against software vulnerabilities is by teaching artificial intelligence (AI) to identify and attack them. Their study about their approach will be presented on July 7 in Montreal, Canada, at the ACM International Conference on the Foundations of Software Engineering, one of the field's top-tier venues.
Finding the exploit in the haystack
At the heart of their work is a problem most people never see but that affects everyone: software vulnerabilities. Every time a person uses an app, buys something online, or interacts with a digital service, their data travels through invisible connectors called application programming interfaces, or APIs.
"You can view an API as a communication channel between two pieces of software," Zhang said. "If the API accepts malicious or unexpected inputs without performing proper validation and security checks, attackers can exploit those weaknesses to trigger vulnerabilities, compromise systems, or carry out successful cyberattacks."
These weak points are everywhere. Modern applications are built in layers, stacking third-party libraries and external code on top of one another in ways that make it nearly impossible for a developer to track every potential risk. A flaw buried deep in one layer can ripple upward, quietly exposing systems that developers didn't even know were at risk.
The problem isn't just technical. It's human. Developers, under constant pressure to ship working software, tend to treat security as an afterthought.
"Most of the time, security is considered a second-class citizen," Meng said. "Developers often prioritize functionality over security, especially when the risks are not immediately visible."
Raising awareness to protect systems
Meng and Yao's approach to closing that gap is as clever as it is counterintuitive: to help defend software, they teach artificial intelligence to attack it.
Working with Zhang and the current Ph.D. students in computer science, the team developed a system that uses large language models — the same underlying technology that powers tools like ChatGPT — to automatically generate what researchers call “proof-of-concept exploits.” These are step-by-step demonstrations showing exactly how a real attacker could take advantage of a known flaw.
The logic is simple but powerful. When a developer sees an abstract warning that their software has a vulnerability, they may ignore it or put off fixing it.
"If developers need a strong motivation, they say, 'Show me the exploit,'" Zhang said. "Then they will make the changes."
In testing, the system successfully generated proof-of-concept exploits with a high level of reliability. That’s a meaningful leap forward in solving a problem that has long resisted automated solutions. The goal, Zhang said, is entirely defensive: to help developers understand and address risks before malicious actors find them first.
The novelty of the approach has gotten some high-level attention. OpenAI has showed interest in the project, an early sign that it is resonating beyond academic circles.
Automating security
A second major thread of the team’s research targets the software supply chain. This complex web of dependencies connects modern applications. The team develops automated tools to identify which specific APIs within a piece of software are vulnerable, information that is often missing or incomplete and leaves developers without a clear picture of where to focus their security efforts.
"It's hard for developers to conduct code inspections or vulnerability localization, missing this information," Zhang said. "So we try to automatically derive it."
As software grows more interconnected, that precision becomes more important. Knowing a vulnerability exists somewhere in a system is far less useful than knowing exactly where it is and what it would take to exploit it.
Full circle
For the doctoral students on the team, working alongside Zhang carries a particular resonance. She was once exactly where they are now, learning the craft of cybersecurity research in the same labs, under the same advisors, asking the same kinds of questions.
“Working with Professor Zhang has taught me that good software engineering and security research takes patience and care,” computer science doctoral student and co-author Zhengjie Ji said. “She helped me see that results need to be solid enough for us to stand behind, and that writing is where we make the logic of the work clear to others.”
Wenjia Song '24 also worked on the project while a doctoral student. She went on to work at Google, Meng said.
For Meng and Yao, the continued collaboration with Zhang is a reminder of how sustained mentorship produces colleagues who return to push the work further.
"Dr. Zhang is a whisperer for software developers," Yao said. “She cares deeply about their day-to-day challenges. Her focus on creating innovative solutions for real-world needs is inspiring."
This work has been supported by the National Science Foundation, Office of Naval Research, and the Commonwealth Cyber Initiative. Yao is affiliated faculty in the Sanghani Center for Artificial Intelligence and Data Analytics.
Why governance lets Thomson Reuters
move faster on AI
David Potter
July 2, 2026
There’s pressure to get AI working fast, and governance often gets left for later. Build the pilot, prove it works, sort out who can see what data afterward.
Caitlin Halferty has seen where that leads. Teams get the pilot running, and then a problem forces the governance work they skipped, like finding people can see data they shouldn’t.
“They’ll build a really great data capability, perhaps a pilot, and then they’ll go to bolt on the governance later,” says Halferty, head of data and analytics at Thomson Reuters, in an interview with Digital Journal.
Thomson Reuters is a Toronto-based company whose customers are lawyers, accountants, and tax and audit professionals, the kind of work where being wrong carries a cost. Halferty’s argument is to govern the data first and then build AI on top of it.
“Governance for us has enabled us to de-risk and accelerate our AI transformation,” she said on stage at a Snowflake conference in June.
Getting every system to agree on what words mean
Good data isn’t enough on its own.
A company can connect every source and lock down who sees what, and its systems can still disagree on something as basic as how many customers it has.
“Unless you also invest in a semantic capability that really makes sense of that data, you still find yourself where you’ll arrive at conflicting answers on number of customers or best performing products in market,” says Halferty.
A semantic layer acts as a shared business dictionary, giving every system the same definition of terms like “customer” or “best-selling product,” so reports and AI tools don’t arrive at different answers.
In Thomson Reuters’ case, Halferty puts the system underneath at roughly 37,000 governed tables and 350 data sources feeding one secured source of truth.
The work began with finance and the team expanded from there. The reason? If finance will file its external reports off a data system, it vouches for the system everywhere else.
She said more than 1,500 people across finance and the business use that semantic layer for everyday decisions.
Work that took months now runs near real time, and a finance task that once meant waiting on Excel files that took 90 minutes to load is gone.
The agreed-upon data also enables AI agents to move faster. When every source already means the same thing by “customer,” an agent doesn’t stop to sort out the difference.
“Because we focused on it early, we’re able to move faster,” says Halferty.
Curated, governed data, she adds, is “sort of a gold mine for AI agents.”
Her test for whether the work is real is whether it has reached production.
“This is finance-validated metrics embedded in our key workflows,” she said.
A standard set by what the customer can’t get wrong
The standard for the data and the AI built on it is set by the people who buy from Thomson Reuters. Its customers are lawyers, accountants, and tax and audit professionals.
“Their name and reputation is on the line, it can’t be wrong,” says Halferty.
She calls the bar “fiduciary standard”. It means more than 175 years of curated content, thousands of subject-matter experts checking outputs, citations built into the product so users see where an answer came from, and a promise not to train models on customer data.
It also means testing before anything ships.
Halferty says every AI capability runs through the company’s responsible-AI practice, checked for hallucination and bias, with the security team testing for prompt injection, before a customer ever uses it.
Where to start without the scale
Thomson Reuters has scale, budget, and a long head start on clean data. Canada is pushing businesses to adopt AI, and boards are increasingly asking where to begin. For smaller companies, where’s the right place to start?
Halferty suggests starting by learning what your peers are doing first. Bring in outside help if the skill isn’t on staff. Then find where your own data lives.Thomson Reuters had customer data alone spread across 23 sources before pulling it into a single customer master.
“Figuring out how to pull that together across those different data sources, and leveraging a data platform and consulting capability, to identify what are you going after as an organization, what are therefore the data domains I need to prioritize,” says Halferty.
Get the data that matters most governed first, and the speed comes from there.
Final shotsCheck whether your systems agree on basic definitions before adding more data or AI on top. If two reports count customers differently, that gets worse with AI, not better.
Build access rules and governance into a project from the start. Adding them after a working pilot is where teams find people can see data they shouldn’t.
Find where your data actually lives before anything else. Halferty’s was spread across 23 systems, and pulling it into one place came before the AI work.
Revolutionizing material synthesis with artificial intelligence
Collaboration between the startup alqem AI and MPI CPfS
Max Planck Institute for Chemical Physics of Solids
image:
Hanh Nguyen (CEO, left) and Julia Krez (VP Operations, right) examine an arc furnace. Julia Krez, a chemist, earned her Ph.D. under Prof. Claudia Felser and is also a member of the Board of Trustees of the MPI for Chemical Physics of Solids.
view moreCredit: © alqem AI GmbH
Collaboration with the Max Planck Society: The Max Planck Society and its institutes have long-standing research partnerships in the field of quantum materials and engage in active collaboration with industry and startups.
Innovative AI platform: Alqem AI's platform combines extensive databases of material predictions with high-quality training data and laboratory synthesis to turn digital predictions into real materials.
Partnership with Alqem AI: Alqem AI is launching a project to identify new magnetic materials that do not contain rare earth elements. The company is receiving support for this subproject from Prof. Claudia Felser, director at the Max Planck Institute for Chemical Physics of Solids.
Bronze, iron, steel, silicon: Every major technological era has been shaped by new materials. However, only a fraction of the theoretically possible crystalline compounds have ever been synthesized. Hundreds of millions of possibilities thus remain undiscovered. At the same time, the supply of critical raw materials is increasingly falling into the hands of just a few countries. High-performance permanent magnets – which are indispensable for electric vehicles, wind turbines, robotics, and defense systems, among other applications – are produced in China at a rate of about 90 percent. Recent export restrictions have made the supply of materials a key geopolitical issue.
The MPG and its institutes, which are active in the fields of solid-state research and related areas, have long been addressing this key issue and conducting research on so-called quantum materials. There are numerous scientific collaborations and agreements on these topics between the Max Planck Society and other scientific partners. The MPG has also long pursued scientific exchange between Max Planck Institutes and industry or startups. The most recent example is the scientific support provided to Alqem AI, a DEEPTech startup, by the Max Planck Institute for Chemical Physics of Solids in Dresden in the field of next-generation magnetic materials, led by Claudia Felser, director of the institute and vice president of the Max Planck Society.
Alqem AI, which has just completed a pre-seed round of financing totaling 8 million euros, is pursuing an approach that uses AI to identify suitable materials. The company was founded by the team behind Alexandria, the world’s leading open materials database, which is used by numerous universities and companies in AI-driven materials research. At the core of the AI platform are two proprietary data foundations: a database of predicted materials on an unprecedented scale, as well as high-quality training data for material properties that did not previously exist in this form. In-house laboratory capabilities for synthesis ensure that digital predictions are turned into real materials.
“We’re starting where the need is greatest: with rare-earth-free magnets – a product the world urgently needs, since rare earths have remained irreplaceable for decades. But the AI platform we’re building isn’t limited to a single class of materials,” says Hanh Nguyen, CEO of Alqem AI.
Claudia Felser’s team has been working in the field of synthesizing and characterizing rare-earth-free magnets for quite some time. Claudia Felser is committed to this startup out of conviction: “Discovering a truly new permanent magnet is one of the most difficult problems in materials science. The last real breakthrough was over forty years ago. ”What makes alqem’s approach special is that it doesn’t stop at prediction. In our collaboration, we’re putting precisely those rare-earth-free candidates through experimental testing that researchers have long been waiting for. And it’s the combination of large-scale computer-aided screening and systematic synthesis that convinces me this can work.”
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The research at the Max Planck Institute for Chemical Physics of Solids (MPI CPfS) in Dresden, Germany, aims to discover and understand new materials with unusual properties.
In close cooperation, chemists and physicists (including chemists working on synthesis, experimentalists and theoreticians) use the most modern tools and methods to examine how the chemical composition and arrangement of atoms, as well as external forces, affect the magnetic, electronic and chemical properties of the compounds. New quantum materials, physical phenomena and materials for energy conversion are the result of this interdisciplinary collaboration.
The MPI CPfS (www.cpfs.mpg.de) is part of the Max Planck Society and was founded in 1995 in Dresden. It is divided into four departments and four independent research groups. The Institute employs around 280 staff, of whom about 180 are scientists including 70 doctoral students from more than 20 nations.
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