Curated findings and actionable knowledge at the intersection of technology, innovation, and governance
A Sankey diagram developed by AI World and Hugging Face:”… illustrating the flow from top open-source datasets through AI organizations to their derivative models, showcasing the collaborative nature of AI development…(More)”.
Paper by Riccardo Gallotti, Davide Maniscalco, Marc Barthelemy & Manlio De Domenico: “The description of human mobility is at the core of many fundamental applications ranging from urbanism and transportation to epidemics containment. Data about human movements, once scarce, is now widely available thanks to new sources such as phone call detail records, GPS devices, or Smartphone apps. Nevertheless, it is still common to rely on a single dataset by implicitly assuming that the statistical properties observed are robust regardless of data gathering and processing techniques. Here, we test this assumption on a broad scale by comparing human mobility datasets obtained from 7 different data-sources, tracing 500+ millions individuals in 145 countries. We report wide quantifiable differences in the resulting mobility networks and in the displacement distribution. These variations impact processes taking place on these networks like epidemic spreading. Our results point to the need for disclosing the data processing and, overall, to follow good practices to ensure robust and reproducible results…(More)”
Paper by Alireza Ghafarollahi, and Markus J. Buehler: “A key challenge in artificial intelligence (AI) is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, SciAgents, an approach that leverages three core concepts is presented: (1) large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses human research methods. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. By integrating these capabilities in a modular fashion, the system yields material discoveries, critiques and improves existing hypotheses, retrieves up-to-date data about existing research, and highlights strengths and limitations. This is achieved by harnessing a “swarm of intelligence” similar to biological systems, providing new avenues for discovery. How this model accelerates the development of advanced materials by unlocking Nature’s design principles, resulting in a new biocomposite with enhanced mechanical properties and improved sustainability through energy-efficient production is shown…(More)”.
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)”.
Article by Kashmir Hill: “Darcy Bullock, a civil engineering professor at Purdue University, turns to his computer screen to get information about how fast cars are traveling on Interstate 65, which runs 887 miles from Lake Michigan to the Gulf of Mexico. It’s midafternoon on a Monday, and his screen is mostly filled with green dots indicating that traffic is moving along nicely. But near an exit on the outskirts of Indianapolis, an angry red streak shows that cars have stopped moving.
A traffic camera nearby reveals the cause: A car has spun out, causing gridlock.
In recent years, vehicles that have wireless connectivity have become a critical source of information for transportation departments and for academics who study traffic patterns. The data these vehicles emit — including speed, how hard they brake and accelerate, and even if their windshield wipers are on — can offer insights into dangerous road conditions, congestion or poorly timed traffic signals.
“Our cars know more about our roads than agencies do,” said Dr. Bullock, who regularly works with the Indiana Department of Transportation to conduct studies on how to reduce traffic congestion and increase road safety. He credits connected-car data with detecting hazards that would have taken years — and many accidents — to find in the past.
The data comes primarily from commercial trucks and from cars made by General Motors that are enrolled in OnStar, G.M.’s internet-connected service. (Drivers know OnStar as the service that allows them to lock their vehicles from a smartphone app or find them if they have been stolen.) Federal safety guidelines require commercial truck drivers to be routinely monitored, but people driving G.M. vehicles may be surprised to know that their data is being collected, though it is indicated in the fine print of the company’s privacy policy…(More)”.
Essay by Aaron Horvath: “Foundations make grants conditional on demonstrable results. Charities tout the evidentiary basis of their work. And impact consultants play both sides: assisting funders in their pursuit of rational beneficence and helping grantees translate the jumble of reality into orderly, spreadsheet-ready metrics.
Measurable impact has crept into everyday understandings of charity as well. There’s the extensive (often fawning) news coverage of data-crazed billionaire philanthropists, so-called thought leaders exhorting followers to rethink their contributions to charity, and popular books counseling that intuition and sentiment are poor guides for making the world a better place. Putting ideas into action, charity evaluators promote research-backed listings of the most impactful nonprofits. Why give to your local food bank when there’s one in Somerville, Massachusetts, with a better rating?
Over the past thirty years, amid a larger crisis of civic engagement, social isolation, and political alienation, measurable impact has seeped into our civic imagination and become one of the guiding ideals for public-spirited beneficence. And while its proponents do not always agree on how best to achieve or measure the extent of that impact, they have collectively recast civic engagement as objective, pragmatic, and above the fray of politics—a triumph of the head over the heart. But how did we get here? And what happens to our capacity for meaningful collective action when we think of civic life in such depersonalized and quantified terms?…(More)”.
Essay by Alexander Hartley: “For an idea of the scale of the prize, it’s worth remembering that 90 percent of recent U.S. economic growth, and 65 percent of the value of its largest 500 companies, is already accounted for by intellectual property. By any estimate, AI will vastly increase the speed and scale at which new intellectual products can be minted. The provision of AI services themselves is estimated to become a trillion-dollar market by 2032, but the value of the intellectual property created by those services—all the drug and technology patents; all the images, films, stories, virtual personalities—will eclipse that sum. It is possible that the products of AI may, within my lifetime, come to represent a substantial portion of all the world’s financial value.
In this light, the question of ownership takes on its true scale, revealing itself as a version of Bertolt Brecht’s famous query: To whom does the world belong?
Questions of AI authorship and ownership can be divided into two broad types. One concerns the vast troves of human-authored material fed into AI models as part of their “training” (the process by which their algorithms “learn” from data). The other concerns ownership of what AIs produce. Call these, respectively, the input and output problems.
So far, attention—and lawsuits—have clustered around the input problem. The basic business model for LLMs relies on the mass appropriation of human-written text, and there simply isn’t anywhere near enough in the public domain. OpenAI hasn’t been very forthcoming about its training data, but GPT-4 was reportedly trained on around thirteen trillion “tokens,” roughly the equivalent of ten trillion words. This text is drawn in large part from online repositories known as “crawls,” which scrape the internet for troves of text from news sites, forums, and other sources. Fully aware that vast data scraping is legally untested—to say the least—developers charged ahead anyway, resigning themselves to litigating the issue in retrospect. Lawyer Peter Schoppert has called the training of LLMs without permission the industry’s “original sin”—to be added, we might say, to the technology’s mind-boggling consumption of energy and water in an overheating planet. (In September, Bloomberg reported that plans for new gas-fired power plants have exploded as energy companies are “racing to meet a surge in demand from power-hungry AI data centers.”)…(More)”.
Book edited by Mira Lane and Arathi Sethumadhavan: “…The book delves deeply into the dynamic interplay between theory and practice, shedding light on the transformative potential and complexities of AI. For practitioners deeply immersed in the world of AI, Lane and Sethumadhavan offer firsthand accounts and insights from technologists, academics, and thought leaders, as well as a series of compelling case studies, ranging from AI’s impact on artistry to its role in addressing societal challenges like modern slavery and wildlife conservation.
As the global AI market burgeons, this book enables collaboration, knowledge sharing, and interdisciplinary dialogue. It caters not only to the practitioners shaping the AI landscape but also to policymakers striving to navigate the intricate relationship between humans and machines, as well as academics. Divided into two parts, the first half of the book offers readers a comprehensive understanding of AI’s historical context, its influence on power dynamics, human-AI interaction, and the critical role of audits in governing AI systems. The second half unfolds a series of eight case studies, unraveling AI’s impact on fields as varied as healthcare, vehicular safety, conservation, human rights, and the metaverse. Each chapter in this book paints a vivid picture of AI’s triumphs and challenges, providing a panoramic view of how it is reshaping our world…(More)”
New Report by Sara Marcucci, Andrew J. Zahuranec, and Stefaan Verhulst: “In an increasingly data-driven world, organizations across sectors are recognizing the potential of non-traditional data—data generated from sources outside conventional databases, such as social media, satellite imagery, and mobile usage—to provide insights into societal trends and challenges. When harnessed thoughtfully, this data can improve decision-making and bolster public interest projects in areas as varied as disaster response, healthcare, and environmental protection. However, with these new data streams come heightened ethical, legal, and operational risks that organizations need to manage responsibly. That’s where due diligence comes in, helping to ensure that data initiatives are beneficial and ethical.
The report, Trust but Verify: A Guide to Conducting Due Diligence When Leveraging Non-Traditional Data in the Public Interest, co-authored by Sara Marcucci, Andrew J. Zahuranec, and Stefaan Verhulst, offers a comprehensive framework to guide organizations in responsible data partnerships. Whether you’re a public agency or a private enterprise, this report provides a six-step process to ensure due diligence and maintain accountability, integrity, and trust in data initiatives…(More) (Blog)”.
Article by Stefaan Verhulst and Peter Addo: “In theory, consent offers a mechanism to reduce power imbalances. In reality, existing consent mechanisms are limited and, in many respects, archaic, based on binary distinctions – typically presented in check-the-box forms that most websites use to ask you to register for marketing e-mails – that fail to appreciate the nuance and context-sensitive nature of data reuse. Consent today generally means individual consent, a notion that overlooks the broader needs of communities and groups.
While we understand the need to safeguard information about an individual such as, say, their health status, this information can help address or even prevent societal health crises. Individualised notions of consent fail to consider the potential public good of reusing individual data responsibly. This makes them particularly problematic in societies that have more collective orientations, where prioritising individual choices could disrupt the social fabric.
The notion of a social licence, which has its roots in the 1990s within the extractive industries, refers to the collective acceptance of an activity, such as data reuse, based on its perceived alignment with community values and interests. Social licences go beyond the priorities of individuals and help balance the risks of data misuse and missed use (for example, the risks of violating privacy vs. neglecting to use private data for public good). Social licences permit a broader notion of consent that is dynamic, multifaceted and context-sensitive.
Policymakers, citizens, health providers, think tanks, interest groups and private industry must accept the concept of a social licence before it can be established. The goal for all stakeholders is to establish widespread consensus on community norms and an acceptable balance of social risk and opportunity.
Community engagement can create a consensus-based foundation for preferences and expectations concerning data reuse. Engagement could take place via dedicated “data assemblies” or community deliberations about data reuse for particular purposes under particular conditions. The process would need to involve voices as representative as possible of the different parties involved, and include those that are traditionally marginalised or silenced…(More)”.