The war over the peace business

Curated on Posted on by Stefaan Verhulst

Article by Tekendra Parmar: “At the second annual AI+ Expo in Washington, DC, in early June, war is the word of the day.

As a mix of Beltway bureaucrats, military personnel, and Washington’s consultant class peruse the expansive Walter E. Washington Convention Center, a Palantir booth showcases its latest in data-collection suites for “warfighters.” Lockheed Martin touts the many ways it is implementing AI throughout its weaponry systems. On the soundstage, the defense tech darling Mach Industries is selling its newest uncrewed aerial vehicles. “We’re living in a world with great-power competition,” the presenter says. “We can’t rule out the possibility of war — but the best way to prevent a war is deterrence,” he says, flanked by videos of drones flying through what looked like the rugged mountains and valleys of Kandahar.

Hosted by the Special Competitive Studies Project, a think tank led by former Google CEO Eric Schmidt, the expo says it seeks to bridge the gap between Silicon Valley entrepreneurs and Washington policymakers to “strengthen” America and its allies’ “competitiveness in critical technologies.”

One floor below, a startup called Anadyr Horizon is making a very different sales pitch, for software that seeks to prevent war rather than fight it: “Peace tech,” as the company’s cofounder Arvid Bell calls it. Dressed in white khakis and a black pinstripe suit jacket with a dove and olive branch pinned to his lapel (a gift from his husband), the former Harvard political scientist begins by noting that Russia’s all-out invasion of Ukraine had come as a surprise to many political scientists. But his AI software, he says, could predict it.

Long the domain of fantasy and science fiction, the idea of forecasting conflict has now become a serious pursuit. In Isaac Asimov’s 1950s “Foundation” series, the main character develops an algorithm that allows him to predict the decline of the Galactic Empire, angering its rulers and forcing him into exile. During the coronavirus pandemic, the US State Department experimented with AI fed with Twitter data to predict “COVID cases” and “violent events.” In its AI audit two years ago, the State Department revealed that it started training AI on “open-source political, social, and economic datasets” to predict “mass civilian killings.” The UN is also said to have experimented with AI to model the war in Gaza…(More)”… ..See also Kluz Prize for PeaceTech (Applications Open)

AI is supercharging war. Could it also help broker peace?

Curated on Posted on by Stefaan Verhulst

Article by Tina Amirtha: “Can we measure what is in our hearts and minds, and could it help us end wars any sooner? These are the questions that consume entrepreneur Shawn Guttman, a Canadian émigré who recently gave up his yearslong teaching position in Israel to accelerate a path to peace—using an algorithm.

Living some 75 miles north of Tel Aviv, Guttman is no stranger to the uncertainties of conflict. Over the past few months, miscalculated drone strikes and imprecise missile targets—some intended for larger cities—have occasionally landed dangerously close to his town, sending him to bomb shelters more than once.

“When something big happens, we can point to it and say, ‘Right, that happened because five years ago we did A, B, and C, and look at its effect,’” he says over Google Meet from his office, following a recent trip to the shelter. Behind him, souvenirs from the 1979 Egypt-Israel and 1994 Israel-Jordan peace treaties are visible. “I’m tired of that perspective.”

The startup he cofounded, Didi, is taking a different approach. Its aim is to analyze data across news outlets, political discourse, and social media to identify opportune moments to broker peace. Inspired by political scientist I. William Zartman’s “ripeness” theory, the algorithm—called the Ripeness Index—is designed to tell negotiators, organizers, diplomats, and nongovernmental organizations (NGOs) exactly when conditions are “ripe” to initiate peace negotiations, build coalitions, or launch grassroots campaigns.

During ongoing U.S.-led negotiations over the war in Gaza, both Israel and Hamas have entrenched themselves in opposing bargaining positions. Meanwhile, Israel’s traditional allies, including the U.S., have expressed growing frustration over the war and the dire humanitarian conditions in the enclave, where the threat of famine looms.

In Israel, Didi’s data is already informing grassroots organizations as they strategize which media outlets to target and how to time public actions, such as protests, in coordination with coalition partners. Guttman and his collaborators hope that eventually negotiators will use the model’s insights to help broker lasting peace.

Guttman’s project is part of a rising wave of so-called PeaceTech—a movement using technology to make negotiations more inclusive and data-driven. This includes AI from Hala Systems, which uses satellite imagery and data fusion to monitor ceasefires in Yemen and Ukraine. Another AI startup, Remesh, has been active across the Middle East, helping organizations of all sizes canvas key stakeholders. Its algorithm clusters similar opinions, giving policymakers and mediators a clearer view of public sentiment and division.

A range of NGOs and academic researchers have also developed digital tools for peacebuilding. The nonprofit Computational Democracy Project created Pol.is, an open-source platform that enables citizens to crowdsource outcomes to public debates. Meanwhile, the Futures Lab at the Center for Strategic and International Studies built a peace agreement simulator, complete with a chart to track how well each stakeholder’s needs are met.

Guttman knows it’s an uphill battle. In addition to the ethical and privacy concerns of using AI to interpret public sentiment, PeaceTech also faces financial hurdles. These companies must find ways to sustain themselves amid shrinking public funding and a transatlantic surge in defense spending, which has pulled resources away from peacebuilding initiatives.

Still, Guttman and his investors remain undeterred. One way to view the opportunity for PeaceTech is by looking at the economic toll of war. In its Global Peace Index 2024, the Institute for Economics and Peace’s Vision of Humanity platform estimated that economic disruption due to violence and the fear of violence cost the world $19.1 trillion in 2023, or about 13 percent of global GDP. Guttman sees plenty of commercial potential in times of peace as well.

“Can we make billions of dollars,” Guttman asks, “and save the world—and create peace?” ..(More)”….See also Kluz Prize for PeaceTech (Applications Open)

The Technopolar Paradox

Curated on Posted on by Stefaan Verhulst

Article by Ian Bremmer: “In February 2022, as Russian forces advanced on Kyiv, Ukraine’s government faced a critical vulnerability: with its Internet and communication networks under attack, its troops and leaders would soon be in the dark. Elon Musk—the de facto head of Tesla, SpaceX, X (formerly Twitter), xAI, the Boring Company, and Neuralink—stepped in. Within days, SpaceX had deployed thousands of Starlink terminals to Ukraine and activated satellite Internet service at no cost. Having kept the country online, Musk was hailed as a hero.

But the centibillionaire’s personal intervention—and Kyiv’s reliance on it—came with risks. Months later, Ukraine asked SpaceX to extend Starlink’s coverage to Russian-occupied Crimea, to enable a submarine drone strike that Kyiv wanted to carry out against Russian naval assets. Musk refused—worried, he said, that this would cause a major escalation in the war. Even the Pentagon’s entreaties on behalf of Ukraine failed to convince him. An unelected, unaccountable private citizen had unilaterally thwarted a military operation in an active war zone while exposing the fact that governments had remarkably little control over crucial decisions affecting their citizens and national security.

This was “technopolarity” in action: a technology leader not only driving stock market returns but also controlling aspects of civil society, politics, and international affairs that have been traditionally the exclusive preserve of nation-states. Over the past decade, the rise of such individuals and the firms they control has transformed the global order, which had been defined by states since the Peace of Westphalia enshrined them as the building blocks of geopolitics nearly 400 years ago. For most of this time, the structure of that order could be described as unipolar, bipolar, or multipolar, depending on how power was distributed among countries. The world, however, has since entered a “technopolar moment,” a term I used in Foreign Affairs in 2021 to describe an emerging order in which “a handful of large technology companies rival [states] for geopolitical influence.” Major tech firms have become powerful geopolitical actors, exercising a form of sovereignty over digital space and, increasingly, the physical world that potentially rivals that of states…(More)”.

Decision Making under Deep Uncertainty and the Great Acceleration

Curated on Posted on by Stefaan Verhulst

Paper by Robert J. Lempert: “Seventy-five years into the Great Acceleration—a period marked by unprecedented growth in human activity and its effects on the planet—some type of societal transformation is inevitable. Successfully navigating these tumultuous times requires scientific, evidence-based information as an input into society’s value-laden decisions at all levels and scales. The methods and tools most commonly used to bring such expert knowledge to policy discussions employ predictions of the future, which under the existing conditions of complexity and deep uncertainty can often undermine trust and hinder good decisions. How, then, should experts best inform society’s attempts to navigate when both experts and decisionmakers are sure to be surprised? Decision Making under Deep Uncertainty (DMDU) offers an answer to this question. With its focus on model pluralism, learning, and robust solutions coproduced in a participatory process of deliberation with analysis, DMDU can repair the fractured conversations among policy experts, decisionmakers, and the public. In this paper, the author explores how DMDU can reshape policy analysis to better align with the demands of a rapidly evolving world and offers insights into the roles and opportunities for experts to inform societal debates and actions toward more-desirable futures…(More)”.

For sale: Data on US servicemembers — and lots of it

Curated on Posted on by Stefaan Verhulst

Article by Alfred Ng: “Active-duty members of the U.S. military are vulnerable to having their personal information collected, packaged and sold to overseas companies without any vetting, according to a new report funded by the U.S. Military Academy at West Point.

The report highlights a significant American security risk, according to military officials, lawmakers and the experts who conducted the research, and who say the data available on servicemembers exposes them to blackmail based on their jobs and habits.

It also casts a spotlight on the practices of data brokers, a set of firms that specialize in scraping and packaging people’s digital records such as health conditions and credit ratings.

“It’s really a case of being able to target people based on specific vulnerabilities,” said Maj. Jessica Dawson, a research scientist at the Army Cyber Institute at West Point who initiated the study.

Data brokers gather government files, publicly available information and financial records into packages they can sell to marketers and other interested companies. As the practice has grown into a $214 billion industry, it has raised privacy concerns and come under scrutiny from lawmakers in Congress and state capitals.

Worried it could also present a risk to national security, the U.S. Military Academy at West Point funded the study from Duke University to see how servicemembers’ information might be packaged and sold.

Posing as buyers in the U.S. and Singapore, Duke researchers contacted multiple data-broker firms who listed datasets about active-duty servicemembers for sale. Three agreed and sold datasets to the researchers while two declined, saying the requests came from companies that didn’t meet their verification standards.

In total, the datasets contained information on nearly 30,000 active-duty military personnel. They also purchased a dataset on an additional 5,000 friends and family members of military personnel…(More)”

AI models could help negotiators secure peace deals

Curated on Posted on by Stefaan Verhulst

The Economist: “In a messy age of grinding wars and multiplying tariffs, negotiators are as busy as the stakes are high. Alliances are shifting and political leaders are adjusting—if not reversing—positions. The resulting tumult is giving even seasoned negotiators trouble keeping up with their superiors back home. Artificial-intelligence (AI) models may be able to lend a hand.

Some such models are already under development. One of the most advanced projects, dubbed Strategic Headwinds, aims to help Western diplomats in talks on Ukraine. Work began during the Biden administration in America, with officials on the White House’s National Security Council (NSC) offering guidance to the Centre for Strategic and International Studies (CSIS), a think-tank in Washington that runs the project. With peace talks under way, CSIS has speeded up its effort. Other outfits are doing similar work.

The CSIS programme is led by a unit called the Futures Lab. This team developed an AI language model using software from Scale AI, a firm based in San Francisco, and unique training data. The lab designed a tabletop strategy game called “Hetman’s Shadow” in which Russia, Ukraine and their allies hammer out deals. Data from 45 experts who played the game were fed into the model. So were media analyses of issues at stake in the Russia-Ukraine war, as well as answers provided by specialists to a questionnaire about the relative values of potential negotiation trade-offs. A database of 374 peace agreements and ceasefires was also poured in.

Thus was born, in late February, the first iteration of the Ukraine-Russia Peace Agreement Simulator. Users enter preferences for outcomes grouped under four rubrics: territory and sovereignty; security arrangements; justice and accountability; and economic conditions. The AI model then cranks out a draft agreement. The software also scores, on a scale of one to ten, the likelihood that each of its components would be satisfactory, negotiable or unacceptable to Russia, Ukraine, America and Europe. The model was provided to government negotiators from those last three territories, but a limited “dashboard” version of the software can be run online by interested members of the public…(More)”.

The Age of AI in the Life Sciences: Benefits and Biosecurity Considerations

Curated on Posted on by Stefaan Verhulst

Report by the National Academies of Sciences, Engineering, and Medicine: “Artificial intelligence (AI) applications in the life sciences have the potential to enable advances in biological discovery and design at a faster pace and efficiency than is possible with classical experimental approaches alone. At the same time, AI-enabled biological tools developed for beneficial applications could potentially be misused for harmful purposes. Although the creation of biological weapons is not a new concept or risk, the potential for AI-enabled biological tools to affect this risk has raised concerns during the past decade.

This report, as requested by the Department of Defense, assesses how AI-enabled biological tools could uniquely impact biosecurity risk, and how advancements in such tools could also be used to mitigate these risks. The Age of AI in the Life Sciences reviews the capabilities of AI-enabled biological tools and can be used in conjunction with the 2018 National Academies report, Biodefense in the Age of Synthetic Biology, which sets out a framework for identifying the different risk factors associated with synthetic biology capabilities…(More)”

Government reform starts with data, evidence

Curated on Posted on by Stefaan Verhulst

Article by Kshemendra Paul: “It’s time to strengthen the use of data, evidence and transparency to stop driving with mud on the windshield and to steer the government toward improving management of its programs and operations.

Existing Government Accountability Office and agency inspectors general reports identify thousands of specific evidence-based recommendations to improve efficiency, economy and effectiveness, and reduce fraud, waste and abuse. Many of these recommendations aim at program design and requirements, highlighting specific instances of overlap, redundancy and duplication. Others describe inadequate internal controls to balance program integrity with the experience of the customer, contractor or grantee. While progress is being reported in part due to stronger partnerships with IGs, much remains to be done. Indeed, GAO’s 2023 High Risk List, which it has produced going back to 1990, shows surprisingly slow progress of efforts to reduce risk to government programs and operations.

Here are a few examples:

  • GAO estimates recent annual fraud of between $233 billion to $521 billion, or about 3% to 7% of federal spending. On the other hand, identified fraud with high-risk Recovery Act spending was held under 1% using data, transparency and partnerships with Offices of Inspectors General.
  • GAO and IGs have collectively identified hundreds of billions in potential cost savings or improvements not yet addressed by federal agencies.
  • GAO has recently described shortcomings with the government’s efforts to build evidence. While federal policymakers need good information to inform their decisions, the Commission on Evidence-Based Policymaking previously said, “too little evidence is produced to meet this need.”

One of the main reasons for agency sluggishness is the lack of agency and governmentwide use of synchronized, authoritative and shared data to support how the government manages itself.

For example, the Energy Department IG found that, “[t]he department often lacks the data necessary to make critical decisions, evaluate and effectively manage risks, or gain visibility into program results.” It is past time for the government to commit itself to move away from its widespread use of data calls, the error-prone, costly and manual aggregation of data used to support policy analysis and decision-making. Efforts to embrace data-informed approaches to manage government programs and operations are stymied by lack of basic agency and governmentwide data hygiene. While bright pockets exist, management gaps, as DOE OIG stated, “create blind spots in the universe of data that, if captured, could be used to more efficiently identify, track and respond to risks…”

The proposed approach starts with current agency operating models, then drives into management process integration to tackle root causes of dysfunction from the bottom up. It recognizes that inefficiency, fraud and other challenges are diffused, deeply embedded and have non-obvious interrelationships within the federal complex…(More)”

Protecting civilians in a data-driven and digitalized battlespace: Towards a minimum basic technology infrastructure

Curated on Posted on by Stefaan Verhulst

Paper by Ann Fitz-Gerald and Jenn Hennebry: “This article examines the realities of modern day warfare, including a rising trend in hybrid threats and irregular warfare which employ emerging technologies supported by digital and data-driven processes. The way in which these technologies become applied generates a widened battlefield and leads to a greater number of civilians being caught up in conflict. Humanitarian groups mandated to protect civilians have adapted their approaches to the use of new emerging technologies. However, the lack of international consensus on the use of data, the public and private nature of the actors involved in conflict, the transnational aspects of the widened battlefield, and the heightened security risks in the conflict space pose enormous challenges for the protection of civilians agenda. Based on the dual-usage aspect of emerging technologies, the challenges associated with regulation and the need for those affected by conflict to demonstrate resilience towards, and knowledge of, digital media literacy, this paper proposes the development of guidance for a “minimum basic technology infrastructure” which is supported by technology, regulation, and public awareness and education…(More)”.

What is ‘sovereign AI’ and why is the concept so appealing (and fraught)?

Curated on Posted on by Stefaan Verhulst

Article by John Letzing: “Denmark unveiled its own artificial intelligence supercomputer last month, funded by the proceeds of wildly popular Danish weight-loss drugs like Ozempic. It’s now one of several sovereign AI initiatives underway, which one CEO believes can “codify” a country’s culture, history, and collective intelligence – and become “the bedrock of modern economies.”

That particular CEO, Jensen Huang, happens to run a company selling the sort of chips needed to pursue sovereign AI – that is, to construct a domestic vintage of the technology, informed by troves of homegrown data and powered by the computing infrastructure necessary to turn that data into a strategic reserve of intellect…

It’s not surprising that countries are forging expansive plans to put their own stamp on AI. But big-ticket supercomputers and other costly resources aren’t feasible everywhere.

Training a large language model has gotten a lot more expensive lately; the funds required for the necessary hardware, energy, and staff may soon top $1 billion. Meanwhile, geopolitical friction over access to the advanced chips necessary for powerful AI systems could further warp the global playing field.

Even for countries with abundant resources and access, there are “sovereignty traps” to consider. Governments pushing ahead on sovereign AI could risk undermining global cooperation meant to ensure the technology is put to use in transparent and equitable ways. That might make it a lot less safe for everyone.

An example: a place using AI systems trained on a local set of values for its security may readily flag behaviour out of sync with those values as a threat…(More)”.