Recommendations for Better Sharing of Climate Data

Curated on Posted on by Stefaan Verhulst

Creative Commons: “…the culmination of a nine-month research initiative from our Open Climate Data project. These guidelines are a result of collaboration between Creative Commons, government agencies and intergovernmental organizations. They mark a significant milestone in our ongoing effort to enhance the accessibility, sharing, and reuse of open climate data to address the climate crisis. Our goal is to share strategies that align with existing data sharing principles and pave the way for a more interconnected and accessible future for climate data.

Our recommendations offer practical steps and best practices, crafted in collaboration with key stakeholders and organizations dedicated to advancing open practices in climate data. We provide recommendations for 1) legal and licensing terms, 2) using metadata values for attribution and provenance, and 3) management and governance for better sharing.

Opening climate data requires an examination of the public’s legal rights to access and use the climate data, often dictated by copyright and licensing. This legal detail is sometimes missing from climate data sharing and legal interoperability conversations. Our recommendations suggest two options: Option A: CC0 + Attribution Request, in order to maximize reuse by dedicating climate data to the public domain, plus a request for attribution; and Option B: CC BY 4.0, for retaining data ownership and legal enforcement of attribution. We address how to navigate license stacking and attribution stacking for climate data hosts and for users working with multiple climate data sources.

We also propose standardized human- and machine-readable metadata values that enhance transparency, reduce guesswork, and ensure broader accessibility to climate data. We built upon existing model metadata schemas and standards, including those that address license and attribution information. These recommendations address a gap and provide metadata schema that standardize the inclusion of upfront, clear values related to attribution, licensing and provenance.

Lastly, we highlight four key aspects of effective climate data management: designating a dedicated technical managing steward, designating a legal and/or policy steward, encouraging collaborative data sharing, and regularly revisiting and updating data sharing policies in accordance with parallel open data policies and standards…(More)”.

It’s just distributed computing: Rethinking AI governance

Curated on Posted on by Stefaan Verhulst

Paper by Milton L. Mueller: “What we now lump under the unitary label “artificial intelligence” is not a single technology, but a highly varied set of machine learning applications enabled and supported by a globally ubiquitous system of distributed computing. The paper introduces a 4 part conceptual framework for analyzing the structure of that system, which it labels the digital ecosystem. What we now call “AI” is then shown to be a general functionality of distributed computing. “AI” has been present in primitive forms from the origins of digital computing in the 1950s. Three short case studies show that large-scale machine learning applications have been present in the digital ecosystem ever since the rise of the Internet. and provoked the same public policy concerns that we now associate with “AI.” The governance problems of “AI” are really caused by the development of this digital ecosystem, not by LLMs or other recent applications of machine learning. The paper then examines five recent proposals to “govern AI” and maps them to the constituent elements of the digital ecosystem model. This mapping shows that real-world attempts to assert governance authority over AI capabilities requires systemic control of all four elements of the digital ecosystem: data, computing power, networks and software. “Governing AI,” in other words, means total control of distributed computing. A better alternative is to focus governance and regulation upon specific applications of machine learning. An application-specific approach to governance allows for a more decentralized, freer and more effective method of solving policy conflicts…(More)”

Network architecture for global AI policy

Curated on Posted on by Stefaan Verhulst

Article by Cameron F. Kerry, Joshua P. Meltzer, Andrea Renda, and Andrew W. Wyckoff: “We see efforts to consolidate international AI governance as premature and ill-suited to respond to the immense, complex, novel, challenges of governing advanced AI, and the current diverse and decentralized efforts as beneficial and the best fit for this complex and rapidly developing technology.

Exploring the vast terra incognita of AI, realizing its opportunities, and managing its risks requires governance that can adapt and respond rapidly to AI risks as they emerge, develop deep understanding of the technology and its implications, and mobilize diverse resources and initiatives to address the growing global demand for access to AI. No one government or body will have the capacity to take on these challenges without building multiple coalitions and working closely with experts and institutions in industry, philanthropy, civil society, and the academy.

A distributed network of networks can more effectively address the challenges and opportunities of AI governance than a centralized system. Like the architecture of the interconnected information technology systems on which AI depends, such a decentralized system can bring to bear redundancy, resiliency, and diversity by channeling the functions of AI governance toward the most timely and effective pathways in iterative and diversified processes, providing agility against setbacks or failures at any single point. These multiple centers of effort can harness the benefit of network effects and parallel processing.

We explore this model of distributed and iterative AI governance below…(More)”.

Citizens’ assemblies in fragile and conflict-affected settings

Curated on Posted on by Stefaan Verhulst

Article by Nicole Curato, Lucy J Parry, and Melisa Ross: “Citizens’ assemblies have become a popular form of citizen engagement to address complex issues like climate change, electoral reform, and assisted dying. These assemblies bring together randomly selected citizens to learn about an issue, consider diverse perspectives, and develop collective recommendations. Growing evidence highlights their ability to depolarise views, enhance political efficacy, and rebuild trust in institutions. However, the story of citizens’ assemblies is more complicated on closer look. This demanding form of political participation is increasingly critiqued for its limited impact, susceptibility to elite influence, and rigid design features unsuitable to local contexts. These challenges are especially pronounced in fragile and conflict-affected settings, where trust is low, expectations for action are high, and local ownership is critical. Well-designed assemblies can foster civic trust and dialogue across difference, but poorly implemented ones risk exacerbating tensions.

This article offers a framework to examine citizens’ assemblies in fragile and conflict-affected settings, focusing on three dimensions: deliberative design, deliberative integrity, and deliberative sustainability. We apply this framework to cases in Bosnia and France to illustrate both the transformative potential and the challenges of citizens’ assemblies when held amidst or in the aftermath of political conflict. This article argues that citizens’ assemblies can be vital mechanisms to manage intractable conflict, provided they are designed with intentionality, administered deliberatively, and oriented towards sustainability…(More)”.

So You’ve Decided To Carry Your Brain Around

Curated on Posted on by Stefaan Verhulst

Article by Nicholas Clairmont: “If the worry during the Enlightenment, as mathematician Isaac Milner wrote in 1794, was that ‘the great and high’ have ‘forgotten that they have souls,’ then today the worry is that many of us have forgotten that we have bodies.” So writes Christine Rosen, senior fellow at the American Enterprise Institute and senior editor of this journal, in her new book, The Extinction of Experience: Being Human in a Disembodied World.

A sharp articulation of the problem, attributed to Thomas Edison, is that “the chief function of the body is to carry the brain around.” Today, the “brain” can be cast virtually into text or voice communication with just about anyone on Earth, and information and entertainment can be delivered almost immediately to wherever a brain happens to be carried around. But we forget how recently this became possible.

Can it really be less than two decades ago that life started to be revolutionized by the smartphone, the technology that made it possible for people of Edison’s persuasion to render the body seemingly redundant? The iPhone was released in 2007. But even by 2009, according to Pew Research, only a third of American adults “had at some point used the internet on their mobile device.” It wasn’t until 2012 that half did so at least occasionally. And then there is that other technology that took off over the same time period: Facebook and Twitter and Instagram and TikTok and the rest of the social networks that allow us to e-commune and that induce us to see everything we do in light of how it might look to others online.

For such a drastic and recent change, it is one we have largely accepted as just a fact. All the public hand-wringing about it has arguably not made a dent in our actual habits. And maybe that’s because we have underestimated the problem with how it has changed us…(More)”.

Public Policy Evaluation

Curated on Posted on by Stefaan Verhulst

​Implementation Toolkit by the OECD: “…offers practical guidance for government officials and evaluators seeking to improve their evaluation capacities and systems, by enabling a deeper understanding of their strengths and weaknesses and learning from OECD member country experiences and trends. The toolkit supports the practical implementation of the principles contained in the 2022 OECD Recommendation on Public Policy Evaluation, which is the first international standard aimed at driving the establishment of robust institutions and practices that promote the use of public policy evaluations. Together, the Recommendation and this accompanying toolkit seek to help governments build a culture of continuous learning and evidence-informed policymaking, potentially leading to more impactful policies and greater trust in government action.​..(More)”.

Data Stewardship Decoded: Mapping Its Diverse Manifestations and Emerging Relevance at a time of AI

Curated on Posted on by Stefaan Verhulst

Paper by Stefaan Verhulst: “Data stewardship has become a critical component of modern data governance, especially with the growing use of artificial intelligence (AI). Despite its increasing importance, the concept of data stewardship remains ambiguous and varies in its application. This paper explores four distinct manifestations of data stewardship to clarify its emerging position in the data governance landscape. These manifestations include a) data stewardship as a set of competencies and skills, b) a function or role within organizations, c) an intermediary organization facilitating collaborations, and d) a set of guiding principles. 

The paper subsequently outlines the core competencies required for effective data stewardship, explains the distinction between data stewards and Chief Data Officers (CDOs), and details the intermediary role of stewards in bridging gaps between data holders and external stakeholders. It also explores key principles aligned with the FAIR framework (Findable, Accessible, Interoperable, Reusable) and introduces the emerging principle of AI readiness to ensure data meets the ethical and technical requirements of AI systems. 

The paper emphasizes the importance of data stewardship in enhancing data collaboration, fostering public value, and managing data reuse responsibly, particularly in the era of AI. It concludes by identifying challenges and opportunities for advancing data stewardship, including the need for standardized definitions, capacity building efforts, and the creation of a professional association for data stewardship…(More)”

Enhancing Access to and Sharing of Data in the Age of Artificial Intelligence

Curated on Posted on by Stefaan Verhulst


OECD Report: “Artificial intelligence (AI) is transforming economies and societies, but its full potential is hindered by poor access to quality data and models. Based on comprehensive country examples, the OECD report “Enhancing Access to and Sharing of Data in the Age of AI” highlights how governments can enhance access to and sharing of data and certain AI models, while ensuring privacy and other rights and interests such as intellectual property rights. It highlights the OECD Recommendation on Enhancing Access to and Sharing of Data, which provides principles to balance openness while ensuring effective legal, technical and organisational safeguards. This policy brief highlights the key findings of the report and their relevance for stakeholders seeking to promote trustworthy AI through better policies for data and AI models that drive trust, investment, innovation, and well-being….(More)”

Tech tycoons have got the economics of AI wrong

Curated on Posted on by Stefaan Verhulst

The Economist: “…The Jevons paradox—the idea that efficiency leads to more use of a resource, not less—has in recent days provided comfort to Silicon Valley titans worried about the impact of DeepSeek, the maker of a cheap and efficient Chinese chatbot, which threatens the more powerful but energy-guzzling American varieties. Satya Nadella, the boss of Microsoft, posted on X, a social-media platform, that “Jevons paradox strikes again! As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can’t get enough of,” along with a link to the Wikipedia page for the economic principle. Under this logic, DeepSeek’s progress will mean more demand for data centres, Nvidia chips and even the nuclear reactors that the hyperscalers were, prior to the unveiling of DeepSeek, paying to restart. Nothing to worry about if the price falls, Microsoft can make it up on volume.

The logic, however self-serving, has a ring of truth to it. Jevons’s paradox is real and observable in a range of other markets. Consider the example of lighting. William Nordhaus, a Nobel-prizewinning economist, has calculated that a Babylonian oil lamp, powered by sesame oil, produced about 0.06 lumens of light per watt of energy. That compares with up to 110 lumens for a modern light-emitting diode. The world has not responded to this dramatic improvement in energy efficiency by enjoying the same amount of light as a Babylonian at lower cost. Instead, it has banished darkness completely, whether through more bedroom lamps than could have been imagined in ancient Mesopotamia or the Las Vegas sphere, which provides passersby with the chance to see a 112-metre-tall incandescent emoji. Urban light is now so cheap and so abundant that many consider it to be a pollutant.

Likewise, more efficient chatbots could mean that AI finds new uses (some no doubt similarly obnoxious). The ability of DeepSeek’s model to perform about as well as more compute-hungry American AI shows that data centres are more productive than previously thought, rather than less. Expect, the logic goes, more investment in data centres and so on than you did before.

Although this idea should provide tech tycoons with some solace, they still ought to worry. The Jevons paradox is a form of a broader phenomenon known as “rebound effects”. These are typically not large enough to fully offset savings from improved efficiency….Basing the bull case for AI on the Jevons paradox is, therefore, a bet not on the efficiency of the technology but on the level of demand. If adoption is being held back by price then efficiency gains will indeed lead to greater use. If technological progress raises expectations rather than reduces costs, as is typical in health care, then chatbots will make up an ever larger proportion of spending. At the moment, that looks unlikely. America’s Census Bureau finds that only 5% of American firms currently use AI and 7% have plans to adopt it in the future. Many others find the tech difficult to use or irrelevant to their line of business…(More)”.

Randomize NIH grant giving

Curated on Posted on by Stefaan Verhulst

Article by Vinay Prasad: “A pause in NIH study sections has been met with fear and anxiety from researchers. At many universities, including mine, professors live on soft money. No grants? If you are assistant professor, you can be asked to pack your desk. If you are a full professor, the university slowly cuts your pay until you see yourself out. Everyone talks about you afterwards, calling you a failed researcher. They laugh, a little too long, and then blink back tears as they wonder if they are next. Of course, your salary doubles in the new job and you are happier, but you are still bitter and gossiped about.

In order to apply for NIH grants, you have to write a lot of bullshit. You write specific aims and methods, collect bios from faculty and more. There is a section where you talk about how great your department and team is— this is the pinnacle of the proverbial expression, ‘to polish a turd.’ You invite people to work on your grant if they have a lot of papers or grants or both, and they agree to be on your grant even though they don’t want to talk to you ever again.

You submit your grant and they hire someone to handle your section. They find three people to review it. Ideally, they pick people who have no idea what you are doing or why it is important, and are not as successful as you, so they can hate read your proposal. If, despite that, they give you a good score, you might be discussed at study section.

The study section assembles scientists to discuss your grant. As kids who were picked last in kindergarten basketball, they focus on the minutiae. They love to nitpick small things. If someone on study section doesn’t like you, they can tank you. In contrast, if someone loves you, they can’t really single handedly fund you.

You might wonder if study section leaders are the best scientists. Rest assured. They aren’t. They are typically mid career, mediocre scientists. (This is not just a joke, data support this claim see www.drvinayprasad.com). They rarely have written extremely influential papers.

Finally, your proposal gets a percentile score. Here is the chance of funding by percentile. You might get a chance to revise your grant if you just fall short….Given that the current system is onerous and likely flawed, you would imagine that NIH leadership has repeatedly tested whether the current method is superior than say a modified lottery, aka having an initial screen and then randomly giving out the money.

Of course not. Self important people giving out someone else’s money rarely study their own processes. If study sections are no better than lottery, that would mean a lot of NIH study section officers would no longer need to work hard from home half the day, freeing up money for one more grant.

Let’s say we take $200 million and randomize it. Half of it is allocated to being given out in the traditional method, and the other half is allocated to a modified lottery. If an application is from a US University and passes a minimum screen, it is enrolled in the lottery.

Then we follow these two arms into the future. We measure publications, citations, h index, the average impact factor of journals in which the papers are published, and more. We even take a subset of the projects and blind reviewers to score the output. Can they tell which came from study section?…(More)”.