For chemists, the AI revolution has yet to happen

Curated on Posted on by Stefaan Verhulst

Editorial Team at Nature: “Many people are expressing fears that artificial intelligence (AI) has gone too far — or risks doing so. Take Geoffrey Hinton, a prominent figure in AI, who recently resigned from his position at Google, citing the desire to speak out about the technology’s potential risks to society and human well-being.

But against those big-picture concerns, in many areas of science you will hear a different frustration being expressed more quietly: that AI has not yet gone far enough. One of those areas is chemistry, for which machine-learning tools promise a revolution in the way researchers seek and synthesize useful new substances. But a wholesale revolution has yet to happen — because of the lack of data available to feed hungry AI systems.

Any AI system is only as good as the data it is trained on. These systems rely on what are called neural networks, which their developers teach using training data sets that must be large, reliable and free of bias. If chemists want to harness the full potential of generative-AI tools, they need to help to establish such training data sets. More data are needed — both experimental and simulated — including historical data and otherwise obscure knowledge, such as that from unsuccessful experiments. And researchers must ensure that the resulting information is accessible. This task is still very much a work in progress…(More)”.

Digital Anthropology Meets Data Science

Curated on Posted on by Stefaan Verhulst

Article by Katie Hillier: “Analyzing online ecosystems in real time, teams of anthropologists and data scientists can begin to understand rapid social changes as they happen.

Ask not what data science can do for anthropology, but what anthropology can do for data science. —Anders Kristian Munk, Why the World Needs Anthropologists Symposium 2022

In the last decade, emerging technologies, such as AI, immersive realities, and new and more addictive social networks, have permeated almost every aspect of our lives. These innovations are influencing how we form identities and belief systems. Social media influences the rise of subcultures on TikTok, the communications of extremist communities on Telegram, and the rapid spread of conspiracy theories that bounce around various online echo chambers. 

People with shared values or experiences can connect and form online cultures at unprecedented scales and speeds. But these new cultures are evolving and shifting faster than our current ability to understand them. 

To keep up with the depth and speed of online transformations, digital anthropologists are teaming up with data scientists to develop interdisciplinary methods and tools to bring the deep cultural context of anthropology to scales available only through data science—producing a surge in innovative methodologies for more effectively decoding online cultures in real time…(More)”.

Enhancing Trust in Science and Democracy in an Age of Misinformation 

Curated on Posted on by Stefaan Verhulst

Article by Marcia McNutt and Michael Crow: “Therefore, we believe the scientific community must more fully embrace its vital role in producing and disseminating knowledge in democratic societies. In Science in a Democratic Society, philosopher Philip Kitcher reminds us that “science should be shaped to promote democratic ideals.” To produce outcomes that advance the public good, scientists must also assess the moral bases of their pursuits. Although the United States has implemented the democratically driven, publicly engaged, scientific culture that Vannevar Bush outlined in Science, the Endless Frontier in 1945, Kitcher’s moral message remains relevant to both conducting science and communicating the results to the public, which pays for much of the enterprise of scientific discovery and technological innovation. It’s on scientists to articulate the moral and public values of the knowledge that they produce in ways that can be understood by citizens and decisionmakers.

However, by organizing themselves largely into groups that rarely reach beyond their own disciplines and by becoming somewhat disconnected from their fellow citizens and from the values of society, many scientists have become less effective than will be necessary in the future. Scientific culture has often left informing or educating the public to other parties such as science teachers, journalists, storytellers, and filmmakers. Instead, scientists principally share the results of their research within the narrow confines of academic and disciplinary journals…(More)”.

Behavioral Economics: Policy Impact and Future Directions

Curated on Posted on by Stefaan Verhulst

Report from the National Academies of Sciences, Engineering, and Medicine: “Behavioral economics – a field based in collaborations among economists and psychologists – focuses on integrating a nuanced understanding of behavior into models of decision-making. Since the mid-20th century, this growing field has produced research in numerous domains and has influenced policymaking, research, and marketing. However, little has been done to assess these contributions and review evidence of their use in the policy arena.

Behavioral Economics: Policy Impact and Future Directions examines the evidence for behavioral economics and its application in six public policy domains: health, retirement benefits, climate change, social safety net benefits, climate change, education, and criminal justice. The report concludes that the principles of behavioral economics are indispensable for the design of policy and recommends integrating behavioral specialists into policy development within government units. In addition, the report calls for strengthening research methodology and identifies research priorities for building on the accomplishments of the field to date…(More)”.

A Genealogy of Open

Curated on Posted on by Stefaan Verhulst

Paper by Betsy Yoon: “The term open has become a familiar part of library and education practice and discourse, with open source software being a common referent. However, the conditions surrounding the emergence of the open source movement are not well understood within librarianship. After identifying capitalism and neoliberalism as structures that shape library and open practice, this article contextualizes the term open by delineating the discursive struggle within the free software movement that led to the emergence of the open source movement. An understanding of the genealogy of open can lend clarity to many of the contradictions that have been grappled with in the literature, such as what open means, whether it supports social justice aims, and its relation to neoliberal and capitalist structures. The article concludes by inquiring into how librarianship and open can reframe practices that are typically oriented toward mitigation and survival to encompass an orientation toward life and flourishing…(More)”.

Soft power, hard choices: Science diplomacy and the race for solutions

Curated on Posted on by Stefaan Verhulst

Article by Stephan Kuster and Marga Gual Soler: “…Global challenges demand that we build consensus for action. But reaching agreement on how – and even if – science and technology should be applied, for the aggregate benefit of all, is complex, and increasingly so.

Science and technology are tightly intertwined with fast-changing economic, geopolitical, and ideological agendas. That pace of change complicates, and sometimes deviates, the discussions and decisions that could unlock the positive global impact of scientific advances.

Therefore, anticipation is key. Understanding the societal, economic, and geopolitical consequences of emerging and possible new technologies before they are deployed is critical. Just recently, for example, artificial intelligence (AI) labs have been urged by a large number of researchers and leading industry figures to pause the training of powerful AI systems, given the inherent risks to society and humanity’s existence.

Indeed, the rapid pace of scientific development calls for more effective global governance when it comes to emerging technology. That in turn requires better anticipatory tools and new mechanisms to embed the science community as key stakeholder and influencer in this work.

The Geneva Science and Diplomacy Anticipator (GESDA) was created with those goals in mind. GESDA identifies the most significant science breakthroughs in the next five, 10, and 25 years. It assesses those advances with the potential to most profoundly to impact people, society, and the planet. It then brings together scientific and policy leaders from around the world to devise the diplomatic envelopes and approaches needed to embrace these advances, while minimizing downsides risks of unintended consequences…(More)”.

Institutional review boards need new skills to review data sharing and management plans

Curated on Posted on by Stefaan Verhulst

Article by Vasiliki Rahimzadeh, Kimberley Serpico & Luke Gelinas: “New federal rules require researchers to submit plans for how to manage and share their scientific data, but institutional ethics boards may be underprepared to review them.

Data sharing is widely considered a conduit to scientific progress, the benefits of which should return to individuals and communities who invested in that science. This is the central premise underpinning changes recently announcement by the US Office of Science Technology and Policy (OSTP)1 on sharing and managing data generated from federally funded research. Researchers will now be required to make publicly accessible any scholarly publications stemming from their federally funded research, as well as supporting data, according to the OSTP announcement. However, the attendant risks to individuals’ privacy-related interests and the increasing threat of community-based harms remain barriers to fostering a trustworthy ecosystem of biomedical data science.

Institutional review boards (IRBs) are responsible for ensuring protections for all human participants engaged in research, but they rarely include members with specialized expertise needed to effectively minimize data privacy and security risks. IRBs must be prepared to meet these review demands given the new data sharing policy changes. They will need additional resources to conduct quality and effective reviews of data management and sharing (DMS) plans. Practical ways forward include expanding IRB membership, proactively consulting with researchers, and creating new research compliance resources. This Comment will focus on data management and sharing oversight by IRBs in the US, but the globalization of data science research underscores the need for enhancing similar review capacities in data privacy, management and security worldwide…(More)”.

No Ground Truth? No Problem: Improving Administrative Data Linking Using Active Learning and a Little Bit of Guile

Curated on Posted on by Stefaan Verhulst

Paper by Sarah Tahamont et al: “While linking records across large administrative datasets [“big data”] has the potential to revolutionize empirical social science research, many administrative data files do not have common identifiers and are thus not designed to be linked to others. To address this problem, researchers have developed probabilistic record linkage algorithms which use statistical patterns in identifying characteristics to perform linking tasks. Naturally, the accuracy of a candidate linking algorithm can be substantially improved when an algorithm has access to “ground-truth” examples — matches which can be validated using institutional knowledge or auxiliary data. Unfortunately, the cost of obtaining these examples is typically high, often requiring a researcher to manually review pairs of records in order to make an informed judgement about whether they are a match. When a pool of ground-truth information is unavailable, researchers can use “active learning” algorithms for linking, which ask the user to provide ground-truth information for select candidate pairs. In this paper, we investigate the value of providing ground-truth examples via active learning for linking performance. We confirm popular intuition that data linking can be dramatically improved with the availability of ground truth examples. But critically, in many real-world applications, only a relatively small number of tactically-selected ground-truth examples are needed to obtain most of the achievable gains. With a modest investment in ground truth, researchers can approximate the performance of a supervised learning algorithm that has access to a large database of ground truth examples using a readily available off-the-shelf tool…(More)”.

Am I Normal? The 200-Year Search for Normal People (and Why They Don’t Exist)

Curated on Posted on by Stefaan Verhulst

Book by Sarah Chaney: “Before the 19th century, the term ’normal’ was rarely ever associated with human behaviour. Normal was a term used in maths, for right angles. People weren’t normal; triangles were.

But from the 1830s, this branch of science really took off across Europe and North America, with a proliferation of IQ tests, sex studies, a census of hallucinations – even a UK beauty map (which concluded the women in Aberdeen were “the most repellent”). This book tells the surprising history of how the very notion of the normal came about, how it shaped us all, often while entrenching oppressive values.

Sarah Chaney looks at why we’re still asking the internet: Do I have a normal body? Is my sex life normal? Are my kids normal? And along the way, she challenges why we ever thought it might be a desirable thing to be…(More)”.

Protecting the integrity of survey research

Curated on Posted on by Stefaan Verhulst

Paper by Jamieson, Kathleen Hall, et al: “Although polling is not irredeemably broken, changes in technology and society create challenges that, if not addressed well, can threaten the quality of election polls and other important surveys on topics such as the economy. This essay describes some of these challenges and recommends remediations to protect the integrity of all kinds of survey research, including election polls. These 12 recommendations specify ways that survey researchers, and those who use polls and other public-oriented surveys, can increase the accuracy and trustworthiness of their data and analyses. Many of these recommendations align practice with the scientific norms of transparency, clarity, and self-correction. The transparency recommendations focus on improving disclosure of factors that affect the nature and quality of survey data. The clarity recommendations call for more precise use of terms such as “representative sample” and clear description of survey attributes that can affect accuracy. The recommendation about correcting the record urges the creation of a publicly available, professionally curated archive of identified technical problems and their remedies. The paper also calls for development of better benchmarks and for additional research on the effects of panel conditioning. Finally, the authors suggest ways to help people who want to use or learn from survey research understand the strengths and limitations of surveys and distinguish legitimate and problematic uses of these methods…(More)”.