A US-run system alerts the world to famines. It’s gone dark after Trump slashed foreign aid

Curated on Posted on by Stefaan Verhulst

Article by Lauren Kent: “A vital, US-run monitoring system focused on spotting food crises before they turn into famines has gone dark after the Trump administration slashed foreign aid.

The Famine Early Warning Systems Network (FEWS NET) monitors drought, crop production, food prices and other indicators in order to forecast food insecurity in more than 30 countries…Now, its work to prevent hunger in Sudan, South Sudan, Somalia, Yemen, Ethiopia, Afghanistan and many other nations has been stopped amid the Trump administration’s effort to dismantle the US Agency for International Development (USAID).

“These are the most acutely food insecure countries around the globe,” said Tanya Boudreau, the former manager of the project.

Amid the aid freeze, FEWS NET has no funding to pay staff in Washington or those working on the ground. The website is down. And its treasure trove of data that underpinned global analysis on food security – used by researchers around the world – has been pulled offline.

FEWS NET is considered the gold-standard in the sector, and it publishes more frequent updates than other global monitoring efforts. Those frequent reports and projections are key, experts say, because food crises evolve over time, meaning early interventions save lives and save money…The team at the University of Colorado Boulder has built a model to forecast water demand in Kenya, which feeds some data into the FEWS NET project but also relies on FEWS NET data provided by other research teams.

The data is layered and complex. And scientists say pulling the data hosted by the US disrupts other research and famine-prevention work conducted by universities and governments across the globe.

“It compromises our models, and our ability to be able to provide accurate forecasts of ground water use,” Denis Muthike, a Kenyan scientist and assistant research professor at UC Boulder, told CNN, adding: “You cannot talk about food security without water security as well.”

“Imagine that that data is available to regions like Africa and has been utilized for years and years – decades – to help inform divisions that mitigate catastrophic impacts from weather and climate events, and you’re taking that away from the region,” Muthike said. He cautioned that it would take many years to build another monitoring service that could reach the same level…(More)”.

Funding the Future: Grantmakers Strategies in AI Investment

Curated on Posted on by Stefaan Verhulst

Report by Project Evident: “…looks at how philanthropic funders are approaching requests to fund the use of AI… there was common recognition of AI’s importance and the tension between the need to learn more and to act quickly to meet the pace of innovation, adoption, and use of AI tools.

This research builds on the work of a February 2024 Project Evident and Stanford Institute for Human-Centered Artificial Intelligence working paper, Inspiring Action: Identifying the Social Sector AI Opportunity Gap. That paper reported that more practitioners than funders (by over a third) claimed their organization utilized AI. 

“From our earlier research, as well as in conversations with funders and nonprofits, it’s clear there’s a mismatch in the understanding and desire for AI tools and the funding of AI tools,” said Sarah Di Troia, Managing Director of Project Evident’s OutcomesAI practice and author of the report. “Grantmakers have an opportunity to quickly upskill their understanding – to help nonprofits improve their efficiency and impact, of course, but especially to shape the role of AI in civil society.”

The report offers a number of recommendations to the philanthropic sector. For example, funders and practitioners should ensure that community voice is included in the implementation of new AI initiatives to build trust and help reduce bias. Grantmakers should consider funding that allows for flexibility and innovation so that the social and education sectors can experiment with approaches. Most importantly, funders should increase their capacity and confidence in assessing AI implementation requests along both technical and ethical criteria…(More)”.

Human Development and the Data Revolution

Curated on Posted on by Stefaan Verhulst

Book edited by Sanna Ojanperä, Eduardo López, and Mark Graham: “…explores the uses of large-scale data in the contexts of development, in particular, what techniques, data sources, and possibilities exist for harnessing large datasets and new online data to address persistent concerns regarding human development, inequality, exclusion, and participation.

Employing a global perspective to explore the latest advances at the intersection of big data analysis and human development, this volume brings together pioneering voices from academia, development practice, civil society organizations, government, and the private sector. With a two-pronged focus on theoretical and practical research on big data and computational approaches in human development, the volume covers such themes as data acquisition, data management, data mining and statistical analysis, network science, visual analytics, and geographic information systems and discusses them in terms of practical applications in development projects and initiatives. Ethical considerations surrounding these topics are visited throughout, highlighting the tradeoffs between benefitting and harming those who are the subjects of these new approaches…(More)”

Extending the CARE Principles: managing data for vulnerable communities in wartime and humanitarian crises

Curated on Posted on by Stefaan Verhulst

Essay by Yana Suchikova & Serhii Nazarovets: “The CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) were developed to ensure ethical stewardship of Indigenous data. However, their adaptability makes them an ideal framework for managing data related to vulnerable populations affected by armed conflicts. This essay explores the application of CARE principles to wartime contexts, with a particular focus on internally displaced persons (IDPs) and civilians living under occupation. These groups face significant risks of data misuse, ranging from privacy violations to targeted repression. By adapting CARE, data governance can prioritize safety, dignity, and empowerment while ensuring that data serves the collective welfare of affected communities. Drawing on examples from Indigenous data governance, open science initiatives, and wartime humanitarian challenges, this essay argues for extending CARE principles beyond their original scope. Such an adaptation highlights CARE’s potential as a universal standard for addressing the ethical complexities of data management in humanitarian crises and conflict-affected environments…(More)”.

Data, waves and wind to be counted in the economy

Curated on Posted on by Stefaan Verhulst

Article by Robert Cuffe: “Wind and waves are set to be included in calculations of the size of countries’ economies for the first time, as part of changes approved at the United Nations.

Assets like oilfields were already factored in under the rules – last updated in 2008.

This update aims to capture areas that have grown since then, such as the cost of using up natural resources and the value of data.

The changes come into force in 2030, and could mean an increase in estimates of the size of the UK economy making promises to spend a fixed share of the economy on defence or aid more expensive.

The economic value of wind and waves can be estimated from the price of all the energy that can be generated from the turbines in a country.

The update also treats data as an asset in its own right on top of the assets that house it like servers and cables.

Governments use a common rule book for measuring the size of their economies and how they grow over time.

These changes to the rule book are “tweaks, rather than a rewrite”, according to Prof Diane Coyle of the University of Cambridge.

Ben Zaranko of the Institute for Fiscal Studies (IFS) calls it an “accounting” change, rather than a real change. He explains: “We’d be no better off in a material sense, and tax revenues would be no higher.”

But it could make economies look bigger, creating a possible future spending headache for the UK government…(More)”.

Bridging the Data Provenance Gap Across Text, Speech and Video

Curated on Posted on by Stefaan Verhulst

Paper by Shayne Longpre et al: “Progress in AI is driven largely by the scale and quality of training data. Despite this, there is a deficit of empirical analysis examining the attributes of well-established datasets beyond text. In this work we conduct the largest and first-of-its-kind longitudinal audit across modalities–popular text, speech, and video datasets–from their detailed sourcing trends and use restrictions to their geographical and linguistic representation. Our manual analysis covers nearly 4000 public datasets between 1990-2024, spanning 608 languages, 798 sources, 659 organizations, and 67 countries. We find that multimodal machine learning applications have overwhelmingly turned to web-crawled, synthetic, and social media platforms, such as YouTube, for their training sets, eclipsing all other sources since 2019. Secondly, tracing the chain of dataset derivations we find that while less than 33% of datasets are restrictively licensed, over 80% of the source content in widely-used text, speech, and video datasets, carry non-commercial restrictions. Finally, counter to the rising number of languages and geographies represented in public AI training datasets, our audit demonstrates measures of relative geographical and multilingual representation have failed to significantly improve their coverage since 2013. We believe the breadth of our audit enables us to empirically examine trends in data sourcing, restrictions, and Western-centricity at an ecosystem-level, and that visibility into these questions are essential to progress in responsible AI. As a contribution to ongoing improvements in dataset transparency and responsible use, we release our entire multimodal audit, allowing practitioners to trace data provenance across text, speech, and video…(More)”.

Artificial intelligence for modelling infectious disease epidemics

Curated on Posted on by Stefaan Verhulst

Paper by Moritz U. G. Kraemer et al: “Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI…(More)”.

A Roadmap to Accessing Mobile Network Data for Statistics

Curated on Posted on by Stefaan Verhulst

Guide by Global Partnership for Sustainable Development Data: “… introduces milestones on the path to mobile network data access. While it is aimed at stakeholders in national statistical systems and across national governments in general, the lessons should resonate with others seeking to take this route. The steps in this guide are written in the order in which they should be taken, and some readers who have already embarked on this journey may find they have completed some of these steps. 

This roadmap is meant to be followed in steps, and readers may start, stop, and return to points on the path at any point. 

The path to mobile network data access has three milestones:

  1. Evaluating the opportunity – setting clear goals for the desired impact of data innovation.
  2. Engaging with stakeholders – getting critical stakeholders to support your cause.
  3. Executing collaboration agreements – signing a written agreement among partners…(More)”

Moving Toward the FAIR-R principles: Advancing AI-Ready Data

Curated on Posted on by Stefaan Verhulst

Paper by Stefaan Verhulst, Andrew Zahuranec and Hannah Chafetz: “In today’s rapidly evolving AI ecosystem, making data ready for AI-optimized for training, fine-tuning, and augmentation-is more critical than ever. While the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) have guided data management and open science, they do not inherently address AI-specific needs. Expanding FAIR to FAIR-R, incorporating Readiness for AI, could accelerate the responsible use of open data in AI applications that serve the public interest. This paper introduces the FAIR-R framework and identifies current efforts for enhancing AI-ready data through improved data labeling, provenance tracking, and new data standards. However, key challenges remain: How can data be structured for AI without compromising ethics? What governance models ensure equitable access? How can AI itself be leveraged to improve data quality? Answering these questions is essential for unlocking the full potential of AI-driven innovation while ensuring responsible and transparent data use…(More)”.

Elon Musk Also Has a Problem with Wikipedia

Curated on Posted on by Stefaan Verhulst

Article by Margaret Talbot: “If you have spent time on Wikipedia—and especially if you’ve delved at all into the online encyclopedia’s inner workings—you will know that it is, in almost every aspect, the inverse of Trumpism. That’s not a statement about its politics. The thousands of volunteer editors who write, edit, and fact-check the site manage to adhere remarkably well, over all, to one of its core values: the neutral point of view. Like many of Wikipedia’s s principles and procedures, the neutral point of view is the subject of a practical but sophisticated epistemological essay posted on Wikipedia. Among other things, the essay explains, N.P.O.V. means not stating opinions as facts, and also, just as important, not stating facts as opinions. (So, for example, the third sentence of the entry titled “Climate change” states, with no equivocation, that “the current rise in global temperatures is driven by human activities, especially fossil fuel burning since the Industrial Revolution.”)…So maybe it should come as no surprise that Elon Musk has lately taken time from his busy schedule of dismantling the federal government, along with many of its sources of reliable information, to attack Wikipedia. On January 21st, after the site updated its page on Musk to include a reference to the much-debated stiff-armed salute he made at a Trump inaugural event, he posted on X that “since legacy media propaganda is considered a ‘valid’ source by Wikipedia, it naturally simply becomes an extension of legacy media propaganda!” He urged people not to donate to the site: “Defund Wikipedia until balance is restored!” It’s worth taking a look at how the incident is described on Musk’s page, quite far down, and judging for yourself. What I see is a paragraph that first describes the physical gesture (“Musk thumped his right hand over his heart, fingers spread wide, and then extended his right arm out, emphatically, at an upward angle, palm down and fingers together”), goes on to say that “some” viewed it as a Nazi or a Roman salute, then quotes Musk disparaging those claims as “politicized,” while noting that he did not explicitly deny them. (There is also now a separate Wikipedia article, “Elon Musk salute controversy,” that goes into detail about the full range of reactions.)

This is not the first time Musk has gone after the site. In December, he posted on X, “Stop donating to Wokepedia.” And that wasn’t even his first bad Wikipedia pun. “I will give them a billion dollars if they change their name to Dickipedia,” he wrote, in an October, 2023, post. It seemed to be an ego thing at first. Musk objected to being described on his page as an “early investor” in Tesla, rather than as a founder, which is how he prefers to be identified, and seemed frustrated that he couldn’t just buy the site. But lately Musk’s beef has merged with a general conviction on the right that Wikipedia—which, like all encyclopedias, is a tertiary source that relies on original reporting and research done by other media and scholars—is biased against conservatives.

The Heritage Foundation, the think tank behind the Project 2025 policy blueprint, has plans to unmask Wikipedia editors who maintain their privacy using pseudonyms (these usernames are displayed in the article history but don’t necessarily make it easy to identify the people behind them) and whose contributions on Israel it deems antisemitic…(More)”.