When Science Meets Power

Curated on Posted on by Stefaan Verhulst

Book by Geoff Mulgan: “Science and politics have collaborated throughout human history, and science is repeatedly invoked today in political debates, from pandemic management to climate change. But the relationship between the two is muddled and muddied.

Leading policy analyst Geoff Mulgan here calls attention to the growing frictions caused by the expanding authority of science, which sometimes helps politics but often challenges it.

He dissects the complex history of states’ use of science for conquest, glory and economic growth and shows the challenges of governing risk – from nuclear weapons to genetic modification, artificial intelligence to synthetic biology. He shows why the governance of science has become one of the biggest challenges of the twenty-first century, ever more prominent in daily politics and policy.

Whereas science is ordered around what we know and what is, politics engages what we feel and what matters. How can we reconcile the two, so that crucial decisions are both well informed and legitimate?

The book proposes new ways to organize democracy and government, both within nations and at a global scale, to better shape science and technology so that we can reap more of the benefits and fewer of the harms…(More)”.

Transmission Versus Truth, Imitation Versus Innovation: What Children Can Do That Large Language and Language-and-Vision Models Cannot (Yet)

Curated on Posted on by Stefaan Verhulst

Paper by Eunice Yiu, Eliza Kosoy, and Alison Gopnik: “Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. First, we argue that these artificial intelligence (AI) models are cultural technologies that enhance cultural transmission and are efficient and powerful imitation engines. Second, we explore what AI models can tell us about imitation and innovation by testing whether they can be used to discover new tools and novel causal structures and contrasting their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skills, can be derived from particular learning techniques and data. In particular, we explore which kinds of cognitive capacities can be enabled by statistical analysis of large-scale linguistic data. Critically, our findings suggest that machines may need more than large-scale language and image data to allow the kinds of innovation that a small child can produce…(More)”.

Elon Musk is now taking applications for data to study X — but only EU risk researchers need apply…

Curated on Posted on by Stefaan Verhulst

Article by Natasha Lomas: “Lawmakers take note: Elon Musk-owned X appears to have quietly complied with a hard legal requirement in the European Union that requires larger platforms (aka VLOPs) to provide researchers with data access in order to study systemic risks arising from use of their services — risks such as disinformation, child safety issues, gender-based violence and mental heath concerns.

X (or Twitter as it was still called at the time) was designated a VLOP under the EU’s Digital Services Act (DSA) back in April after the bloc’s regulators confirmed it meets their criteria for an extra layer of rules to kick in that are intended to drive algorithmic accountability via applying transparency measures on larger platforms.

Researchers intending to study systemic risks in the EU now appear to at least be able to apply for access to study X’s data by accessing a web form through a button which appears at the bottom of this page on its developer platform. (Note researchers can be based in the EU but don’t have to be to meet the criteria; they just need to intend to study systemic risks in the EU.)…(More)”.

The Oligopoly’s Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges 

Curated on Posted on by Stefaan Verhulst

Paper by Leigh-Ann Butler et al: “This study aims to estimate the total amount of article processing charges (APCs) paid to publish open access (OA) in journals controlled by the five large commercial publishers Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley between 2015 and 2018. Using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets and historical fees retrieved via the Internet Archive Wayback Machine, we estimate that globally authors paid $1.06 billion in publication fees to these publishers from 2015–2018. Revenue from gold OA amounted to $612.5 million, while $448.3 million was obtained for publishing OA in hybrid journals. Among the five publishers, Springer-Nature made the most revenue from OA ($589.7 million), followed by Elsevier ($221.4 million), Wiley ($114.3 million), Taylor & Francis ($76.8 million) and Sage ($31.6 million). With Elsevier and Wiley making most of APC revenue from hybrid fees and others focusing on gold, different OA strategies could be observed between publishers…(More)”.This study aims to estimate the total amount of article processing charges (APCs) paid to publish open access (OA) in journals controlled by the five large commercial publishers Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley between 2015 and 2018. Using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets and historical fees retrieved via the Internet Archive Wayback Machine, we estimate that globally authors paid $1.06 billion in publication fees to these publishers from 2015–2018. Revenue from gold OA amounted to $612.5 million, while $448.3 million was obtained for publishing OA in hybrid journals. Among the five publishers, Springer-Nature made the most revenue from OA ($589.7 million), followed by Elsevier ($221.4 million), Wiley ($114.3 million), Taylor & Francis ($76.8 million) and Sage ($31.6 million). With Elsevier and Wiley making most of APC revenue from hybrid fees and others focusing on gold, different OA strategies could be observed between publishers.

Meta is giving researchers more access to Facebook and Instagram data

Curated on Posted on by Stefaan Verhulst

Article by Tate Ryan-Mosley: “Meta is releasing a new transparency product called the Meta Content Library and API, according to an announcement from the company today. The new tools will allow select researchers to access publicly available data on Facebook and Instagram in an effort to give a more overarching view of what’s happening on the platforms. 

The move comes as social media companies are facing public and regulatory pressure to increase transparency about how their products—specifically recommendation algorithms—work and what impact they have. Academic researchers have long been calling for better access to data from social media platforms, including Meta. This new library is a step toward increased visibility about what is happening on its platforms and the effect that Meta’s products have on online conversations, politics, and society at large. 

In an interview, Meta’s president of global affairs, Nick Clegg, said the tools “are really quite important” in that they provide, in a lot of ways, “the most comprehensive access to publicly available content across Facebook and Instagram of anything that we’ve built to date.” The Content Library will also help the company meet new regulatory requirements and obligations on data sharing and transparency, as the company notes in a blog post Tuesday. 

The library and associated API were first released as a beta version several months ago and allow researchers to access near-real-time data about pages, posts, groups, and events on Facebook and creator and business accounts on Instagram, as well as the associated numbers of reactions, shares, comments, and post view counts. While all this data is publicly available—as in, anyone can see public posts, reactions, and comments on Facebook—the new library makes it easier for researchers to search and analyze this content at scale…(More)”.

Hypotheses devised by AI could find ‘blind spots’ in research

Curated on Posted on by Stefaan Verhulst

Article by Matthew Hutson: “One approach is to use AI to help scientists brainstorm. This is a task that large language models — AI systems trained on large amounts of text to produce new text — are well suited for, says Yolanda Gil, a computer scientist at the University of Southern California in Los Angeles who has worked on AI scientists. Language models can produce inaccurate information and present it as real, but this ‘hallucination’ isn’t necessarily bad, Mullainathan says. It signifies, he says, “‘here’s a kind of thing that looks true’. That’s exactly what a hypothesis is.”

Blind spots are where AI might prove most useful. James Evans, a sociologist at the University of Chicago, has pushed AI to make ‘alien’ hypotheses — those that a human would be unlikely to make. In a paper published earlier this year in Nature Human Behaviour4, he and his colleague Jamshid Sourati built knowledge graphs containing not just materials and properties, but also researchers. Evans and Sourati’s algorithm traversed these networks, looking for hidden shortcuts between materials and properties. The aim was to maximize the plausibility of AI-devised hypotheses being true while minimizing the chances that researchers would hit on them naturally. For instance, if scientists who are studying a particular drug are only distantly connected to those studying a disease that it might cure, then the drug’s potential would ordinarily take much longer to discover.

When Evans and Sourati fed data published up to 2001 to their AI, they found that about 30% of its predictions about drug repurposing and the electrical properties of materials had been uncovered by researchers, roughly six to ten years later. The system can be tuned to make predictions that are more likely to be correct but also less of a leap, on the basis of concurrent findings and collaborations, Evans says. But “if we’re predicting what people are going to do next year, that just feels like a scoop machine”, he adds. He’s more interested in how the technology can take science in entirely new directions….(More)”

Science and the State 

Curated on Posted on by Stefaan Verhulst

Introduction to Special Issue by Alondra Nelson et al: “…Current events have thrown these debates into high relief. Pressing issues from the pandemic to anthropogenic climate change, and the new and old inequalities they exacerbate, have intensified calls to critique but also imagine otherwise the relationship between scientific and state authority. Many of the subjects and communities whose well-being these authorities claim to promote have resisted, doubted, and mistrusted technoscientific experts and government officials. How might our understanding of the relationship change if the perspectives and needs of those most at risk from state and/or scientific violence or neglect were to be centered? Likewise, the pandemic and climate change have reminded scientists and state officials that relations among states matter at home and in the world systems that support supply chains, fuel technology, and undergird capitalism and migration. How does our understanding of the relationship between science and the state change if we eschew the nationalist framing of the classic Mertonian formulation and instead account for states in different parts of the world, as well as trans-state relationships?
This special issue began as a yearlong seminar on Science and the State convened by Alondra Nelson and Charis Thompson at the Institute for Advanced Study in Princeton, New Jersey. During the 2020–21 academic year, seventeen scholars from four continents met on a biweekly basis to read, discuss, and interrogate historical and contemporary scholarship on the origins, transformations, and sociopolitical
consequences of different configurations of science, technology, and governance. Our group consisted of scholars from different disciplines, including sociology, anthropology, philosophy, economics, history, political science, and geography. Examining technoscientific expertise and political authority while experiencing the conditions of the pandemic exerted a heightened sense of the stakes concerned and forced us to rethink easy critiques of scientific knowledge and state power. Our affective and lived experiences of the pandemic posed questions about what good science and good statecraft could be. How do we move beyond a presumption of isomorphism between “good” states and “good” science to understand and study the uneven experiences and sometimes exploitative practices of different configurations of science and the state?…(More)”.

Overcoming the Challenges of Using Automated Technologies for Public Health Evidence Synthesis

Curated on Posted on by Stefaan Verhulst

Article by Lucy Hocking et al: “Many organisations struggle to keep pace with public health evidence due to the volume of published literature and length of time it takes to conduct literature reviews. New technologies that help automate parts of the evidence synthesis process can help conduct reviews more quickly and efficiently to better provide up-to-date evidence for public health decision making. To date, automated approaches have seldom been used in public health due to significant barriers to their adoption. In this Perspective, we reflect on the findings of a study exploring experiences of adopting automated technologies to conduct evidence reviews within the public health sector. The study, funded by the European Centre for Disease Prevention and Control, consisted of a literature review and qualitative data collection from public health organisations and researchers in the field. We specifically focus on outlining the challenges associated with the adoption of automated approaches and potential solutions and actions that can be taken to mitigate these. We explore these in relation to actions that can be taken by tool developers (e.g. improving tool performance and transparency), public health organisations (e.g. developing staff skills, encouraging collaboration) and funding bodies/the wider research system (e.g. researchers, funding bodies, academic publishers and scholarly journals)…(More)”

Matchmaking Research To Policy: Introducing Britain’s Areas Of Research Interest Database

Curated on Posted on by Stefaan Verhulst

Article by Kathryn Oliver: “Areas of research interest (ARIs) were originally recommended in the 2015 Nurse Review, which argued that if government stated what it needed to know more clearly and more regularly, then it would be easier for policy-relevant research to be produced.

During our time in government, myself and Annette Boaz worked to develop these areas of research interest, mobilize experts and produce evidence syntheses and other outputs addressing them, largely in response to the COVID pandemic. As readers of this blog will know, we have learned a lot about what it takes to mobilize evidence – the hard, and often hidden labor of creating and sustaining relationships, being part of transient teams, managing group dynamics, and honing listening and diplomatic skills.

Some of the challenges we encountered include the oft-cited, cultural gap between research and policy, the relevance of evidence, and the difficulty in resourcing knowledge mobilization and evidence synthesis require systemic responses. However, one challenge, the information gap noted by Nurse, between researchers and what government departments actually want to know offered a simpler solution.

Up until September 2023, departmental ARIs were published on gov.uk, in pdf or html format. Although a good start, we felt that having all the ARIs in one searchable database would make them more interactive and accessible. So, working with Overton, we developed the new ARI database. The primary benefit of the database will be to raise awareness of ARIs (through email alerts about new ARIs) and accessibility (by holding all ARIs in one place which is easily searchable)…(More)”.

Does the sun rise for ChatGPT? Scientific discovery in the age of generative AI

Curated on Posted on by Stefaan Verhulst

Paper by David Leslie: “In the current hype-laden climate surrounding the rapid proliferation of foundation models and generative AI systems like ChatGPT, it is becoming increasingly important for societal stakeholders to reach sound understandings of their limitations and potential transformative effects. This is especially true in the natural and applied sciences, where magical thinking among some scientists about the take-off of “artificial general intelligence” has arisen simultaneously as the growing use of these technologies is putting longstanding norms, policies, and standards of good research practice under pressure. In this analysis, I argue that a deflationary understanding of foundation models and generative AI systems can help us sense check our expectations of what role they can play in processes of scientific exploration, sense-making, and discovery. I claim that a more sober, tool-based understanding of generative AI systems as computational instruments embedded in warm-blooded research processes can serve several salutary functions. It can play a crucial bubble-bursting role that mitigates some of the most serious threats to the ethos of modern science posed by an unreflective overreliance on these technologies. It can also strengthen the epistemic and normative footing of contemporary science by helping researchers circumscribe the part to be played by machine-led prediction in communicative contexts of scientific discovery while concurrently prodding them to recognise that such contexts are principal sites for human empowerment, democratic agency, and creativity. Finally, it can help spur ever richer approaches to collaborative experimental design, theory-construction, and scientific world-making by encouraging researchers to deploy these kinds of computational tools to heuristically probe unbounded search spaces and patterns in high-dimensional biophysical data that would otherwise be inaccessible to human-scale examination and inference…(More)”.