GAO Report: “Cities across the nation are using “smart city” technologies like traffic cameras and gunshot detectors to improve public services. In this technology assessment, we looked at their use in transportation and law enforcement.
Experts and city officials reported multiple benefits. For example, Houston uses cameras and Bluetooth sensors to measure traffic flow and adjust signal timing. Other cities use license plate readers to find stolen vehicles.
But the technologies can be costly and the benefits unclear. The data they collect may be sold, raising privacy and civil liberties concerns. We offer three policy options to address such challenges…(More)”.
Report by National Academies of Sciences, Engineering, and Medicine: “Our current information ecosystem makes it easier for misinformation about science to spread and harder for people to figure out what is scientifically accurate. Proactive solutions are needed to address misinformation about science, an issue of public concern given its potential to cause harm at individual, community, and societal levels. Improving access to high-quality scientific information can fill information voids that exist for topics of interest to people, reducing the likelihood of exposure to and uptake of misinformation about science. Misinformation is commonly perceived as a matter of bad actors maliciously misleading the public, but misinformation about science arises both intentionally and inadvertently and from a wide range of sources…(More)”.
A project by the Institute for Progress: “In January 2025, President Trump tasked the Office of Science and Technology Policy with creating an AI Action Plan to promote American AI Leadership. The government requested input from the public, and received 10,068 submissions. The database below summarizes specific recommendations from these submissions. … We used AI to extract recommendations from each submission, and to tag them with relevant information. Click on a recommendation to learn more about it. See our analysis of common themes and ideas across these recommendations…(More)”.
Report by Siddhi Pal, Catherine Schneider and Ruggero Marino Lazzaroni: “… introduces a novel three-tiered classification system for global AI talent that addresses significant methodological limitations in existing workforce analyses, by distinguishing between different skill categories within the existing AI talent pool. By distinguishing between non-technical roles (Category 0), technical software development (Category 1), and advanced deep learning specialization (Category 2), our framework enables precise examination of AI workforce dynamics at a pivotal moment in global AI policy.
Through our analysis of a sample of 1.6 million individuals in the AI talent pool across 31 countries, we’ve uncovered clear patterns in technical talent distribution that significantly impact Europe’s AI ambitions. Asian nations hold an advantage in specialized AI expertise, with South Korea (27%), Israel (23%), and Japan (20%) maintaining the highest proportions of Category 2 talent. Within Europe, Poland and Germany stand out as leaders in specialized AI talent. This may be connected to their initiatives to attract tech companies and investments in elite research institutions, though further research is needed to confirm these relationships.
Our data also reveals a shifting landscape of global talent flows. Research shows that countries employing points-based immigration systems attract 1.5 times more high-skilled migrants than those using demand-led approaches. This finding takes on new significance in light of recent geopolitical developments affecting scientific research globally. As restrictive policies and funding cuts create uncertainty for researchers in the United States, one of the big destinations for European AI talent, the way nations position their regulatory environments, scientific freedoms, and research infrastructure will increasingly determine their ability to attract and retain specialized AI talent.
The gender analysis in our study illuminates another dimension of competitive advantage. Contrary to the overall AI talent pool, EU countries lead in female representation in highly technical roles (Category 2), occupying seven of the top ten global rankings. Finland, Czechia, and Italy have the highest proportion of female representation in Category 2 roles globally (39%, 31%, and 28%, respectively). This gender diversity represents not merely a social achievement but a potential strategic asset in AI innovation, particularly as global coalitions increasingly emphasize the importance of diverse perspectives in AI development…(More)”
Paper by Sarah C. Risley, Melissa L. Britsch, Joshua S. Stoll & Heather M. Leslie: “Coastal marine social–ecological systems are experiencing rapid change. Yet, many coastal communities are challenged by incomplete data to inform collaborative research and stewardship. We investigated the role of participatory mapping of local knowledge in addressing these challenges. We used participatory mapping and semi-structured interviews to document local knowledge in two focal social–ecological systems in Maine, USA. By co-producing fine-scale characterizations of coastal marine social–ecological systems, highlighting local questions and needs, and generating locally relevant hypotheses on system change, our research demonstrates how participatory mapping and local knowledge can enhance decision-making capacity in collaborative research and stewardship. The results of this study directly informed a collaborative research project to document changes in multiple shellfish species, shellfish predators, and shellfish harvester behavior and other human activities. This research demonstrates that local knowledge can be a keystone component of collaborative social–ecological systems research and community-lead environmental stewardship…(More)”.
GAO Report: “Generative artificial intelligence (AI) could revolutionize entire industries. In the nearer term, it may dramatically increase productivity and transform daily tasks in many sectors. However, both its benefits and risks, including its environmental and human effects, are unknown or unclear.
Generative AI uses significant energy and water resources, but companies are generally not reporting details of these uses. Most estimates of environmental effects of generative AI technologies have focused on quantifying the energy consumed, and carbon emissions associated with generating that energy, required to train the generative AI model. Estimates of water consumption by generative AI are limited. Generative AI is expected to be a driving force for data center demand, but what portion of data center electricity consumption is related to generative AI is unclear. According to the International Energy Agency, U.S. data center electricity consumption was approximately 4 percent of U.S. electricity demand in 2022 and could be 6 percent of demand in 2026.
While generative AI may bring beneficial effects for people, GAO highlights five risks and challenges that could result in negative human effects on society, culture, and people from generative AI (see figure). For example, unsafe systems may produce outputs that compromise safety, such as inaccurate information, undesirable content, or the enabling of malicious behavior. However, definitive statements about these risks and challenges are difficult to make because generative AI is rapidly evolving, and private developers do not disclose some key technical information.
Selected generative artificial antelligence risks and challenges that could result in human effects
GAO identified policy options to consider that could enhance the benefits or address the challenges of environmental and human effects of generative AI. These policy options identify possible actions by policymakers, which include Congress, federal agencies, state and local governments, academic and research institutions, and industry. In addition, policymakers could choose to maintain the status quo, whereby they would not take additional action beyond current efforts. See below for details on the policy options…(More)”.
Paper by Christopher Walker and Sally Washington: “… presents a process model to guide the production of quality policy advice. The work draws on engagement with both public sector practitioners and academics to design a process model for the development of policy advice that works in practice (can be used by policy professionals in their day-to-day work) and aligns with theory (can be taught as part of explaining the dynamics of a wider policy advisory system). The 5D Model defines five key domains of inquiry: understanding Demand, being open to Discovery, undertaking Design, identifying critical Decision points, and shaping advice to enable Delivery. Our goal is a ‘repeatable, scalable’ model for supporting policy practitioners to provide quality advice to decision makers. The model was developed and tested through an extensive process of engagement with senior policy practitioners who noted the heuristic gave structure to practices that determine how policy advice is organized and formulated. Academic colleagues confirmed the utility of the model for explaining and teaching how policy is designed and delivered within the context of a wider policy advisory system (PAS). A unique aspect of this work was the collaboration and shared interest amongst academics and practitioners to define a model that is ‘useful for teaching’ and ‘useful for doing’…(More)”.
Article by Jim Fruchterman and Steve Francis: “What happens when a nonprofit program or an entire organization needs to shut down? The communities being served, and often society as a whole, are the losers. What if it were possible to mitigate some of that damage by sharing valuable intellectual property assets of the closing effort for longer term benefit? Organizations in these tough circumstances must give serious thought to a responsible exit for their intangible assets.
At the present moment of unparalleled disruption, the entire nonprofit sector is rethinking everything: language to describe their work, funding sources, partnerships, and even their continued existence. Nonprofit programs and entire charities will be closing, or being merged out of existence. Difficult choices are being made. Who will fill the role of witness and archivist to preserve the knowledge of these organizations, their writings, media, software, and data, for those who carry on, either now or in the future?
We believe leaders in these tough days should consider a model we’re calling Exit to Open (E2O) and related exit concepts to safeguard these assets going forward…
Exit to Open (E2O) exploits three elements:
We are in an era where the cost of digital preservation is low; storing a few more bytes for a long time is cheap.
It’s far more effective for an organization’s staff to isolate and archive critical content than an outsider with limited knowledge attempting to do so later.
These resources are of greatest use if there is a human available to interpret them, and a deliberate archival process allows for the identification of these potential interpreters…(More)”.
Article by Frank Langfitt: “A survey of more than 500 political scientists finds that the vast majority think the United States is moving swiftly from liberal democracy toward some form of authoritarianism.
In the benchmark survey, known as Bright Line Watch, U.S.-based professors rate the performance of American democracy on a scale from zero (complete dictatorship) to 100 (perfect democracy). After President Trump’s election in November, scholars gave American democracy a rating of 67. Several weeks into Trump’s second term, that figure plummeted to 55.
“That’s a precipitous drop,” says John Carey, a professor of government at Dartmouth and co-director of Bright Line Watch. “There’s certainly consensus: We’re moving in the wrong direction.”…Not all political scientists view Trump with alarm, but many like Carey who focus on democracy and authoritarianism are deeply troubled by Trump’s attempts to expand executive power over his first several months in office.
“We’ve slid into some form of authoritarianism,” says Steven Levitsky, a professor of government at Harvard, and co-author of How Democracies Die. “It is relatively mild compared to some others. It is certainly reversible, but we are no longer living in a liberal democracy.”…Kim Lane Scheppele, a Princeton sociologist who has spent years tracking Hungary, is also deeply concerned: “We are on a very fast slide into what’s called competitive authoritarianism.”
When these scholars use the term “authoritarianism,” they aren’t talking about a system like China’s, a one-party state with no meaningful elections. Instead, they are referring to something called “competitive authoritarianism,” the kind scholars say they see in countries such as Hungary and Turkey.
In a competitive authoritarian system, a leader comes to power democratically and then erodes the system of checks and balances. Typically, the executive fills the civil service and key appointments — including the prosecutor’s office and judiciary — with loyalists. He or she then attacks the media, universities and nongovernmental organizations to blunt public criticism and tilt the electoral playing field in the ruling party’s favor…(More)”.
Article by Lisa Margonelli: “Only a few months into 2025, the scientific enterprise is reeling from a series of shocks—mass firings of the scientific workforce across federal agencies, cuts to federal research budgets, threats to indirect costs for university research, proposals to tax endowments, termination of federal science advisory committees, and research funds to prominent universities held hostage over political conditions. Amid all this, the public has not shown much outrage at—or even interest in—the dismantling of the national research project that they’ve been bankrolling for the past 75 years.
Some evidence of a disconnect from the scientific establishment was visible in confirmation hearings of administration appointees. During his Senate nomination hearing to head the department of Health and Human Services, Robert F. Kennedy Jr. promised a reorientation of research from infectious disease toward chronic conditions, along with “radical transparency” to rebuild trust in science. While his fans applauded, he insisted that he was not anti-vaccine, declaring, “I am pro-safety.”
But lack of public reaction to funding cuts need not be pinned on distrust of science; it could simply be that few citizens see the $200-billion-per-year, envy-of-the-world scientific enterprise as their own. On March 15, Alabama meteorologist James Spann took to Facebook to narrate the approach of 16 tornadoes in the state, taking note that people didn’t seem to care about the president’s threat to close the National Weather Service. “People say, ‘Well, if they shut it down, I’ll just use my app,’” Spann told Inside Climate News. “Well, where do you think the information on your app comes from? It comes from computer model output that’s run by the National Weather Service.” The public has paid for those models for generations, but only a die-hard weather nerd can find the acronyms for the weather models that signal that investment on these apps…(More)”.
