What Is Public Trust in the Health System? Insights into Health Data Use

Curated on Posted on by Stefaan Verhulst

Open Access Book by Felix Gille: “This book explores the concept of public trust in health systems.

In the context of recent events, including public response to interventions to tackle the COVID-19 pandemic, vaccination uptake and the use of health data and digital health, this important book uses empirical evidence to address why public trust is vital to a well-functioning health system.

In doing so, it provides a comprehensive contemporary explanation of public trust, how it affects health systems and how it can be nurtured and maintained as an integral component of health system governance…(More)”.

Chatbots May ‘Hallucinate’ More Often Than Many Realize

Curated on Posted on by Stefaan Verhulst

Cade Metz at The New York Times: “When the San Francisco start-up OpenAI unveiled its ChatGPT online chatbot late last year, millions were wowed by the humanlike way it answered questions, wrote poetry and discussed almost any topic. But most people were slow to realize that this new kind of chatbot often makes things up.

When Google introduced a similar chatbot several weeks later, it spewed nonsense about the James Webb telescope. The next day, Microsoft’s new Bing chatbot offered up all sorts of bogus information about the Gap, Mexican nightlife and the singer Billie Eilish. Then, in March, ChatGPT cited a half dozen fake court cases while writing a 10-page legal brief that a lawyer submitted to a federal judge in Manhattan.

Now a new start-up called Vectara, founded by former Google employees, is trying to figure out how often chatbots veer from the truth. The company’s research estimates that even in situations designed to prevent it from happening, chatbots invent information at least 3 percent of the time — and as high as 27 percent.

Experts call this chatbot behavior “hallucination.” It may not be a problem for people tinkering with chatbots on their personal computers, but it is a serious issue for anyone using this technology with court documents, medical information or sensitive business data.

Because these chatbots can respond to almost any request in an unlimited number of ways, there is no way of definitively determining how often they hallucinate. “You would have to look at all of the world’s information,” said Simon Hughes, the Vectara researcher who led the project…(More)”.

Climate data can save lives. Most countries can’t access it.

Curated on Posted on by Stefaan Verhulst

Article by Zoya Teirstein: “Earth just experienced one of its hottest, and most damaging, periods on record. Heat waves in the United States, Europe, and China; catastrophic flooding in India, Brazil, Hong Kong, and Libya; and outbreaks of malaria, dengue, and other mosquito-borne illnesses across southern Asia claimed tens of thousands of lives. The vast majority of these deaths could have been averted with the right safeguards in place.

The World Meteorological Organization, or WMO, published a report last week that shows just 11 percent of countries have the full arsenal of tools required to save lives as the impacts of climate change — including deadly weather events, infectious diseases, and respiratory illnesses like asthma — become more extreme. The United Nations climate agency predicts that significant natural disasters will hit the planet 560 times per year by the end of this decade. What’s more, countries that lack early warning systems, such as extreme heat alerts, will see eight times more climate-related deaths than countries that are better prepared. By midcentury, some 50 percent of these deaths will take place in Africa, a continent that is responsible for around 4 percent of the world’s greenhouse gas emissions each year…(More)”.

Smart City Data Governance

Curated on Posted on by Stefaan Verhulst

OECD Report: “Smart cities leverage technologies, in particular digital, to generate a vast amount of real-time data to inform policy- and decision-making for an efficient and effective public service delivery. Their success largely depends on the availability and effective use of data. However, the amount of data generated is growing more rapidly than governments’ capacity to store and process them, and the growing number of stakeholders involved in data production, analysis and storage pushes cities data management capacity to the limit. Despite the wide range of local and national initiatives to enhance smart city data governance, urban data is still a challenge for national and city governments due to: insufficient financial resources; lack of business models for financing and refinancing of data collection; limited access to skilled experts; the lack of full compliance with the national legislation on data sharing and protection; and data and security risks. Facing these challenges is essential to managing and sharing data sensibly if cities are to boost citizens’ well-being and promote sustainable environments…(More)”

Assessing and Suing an Algorithm

Curated on Posted on by Stefaan Verhulst

Report by Elina Treyger, Jirka Taylor, Daniel Kim, and Maynard A. Holliday: “Artificial intelligence algorithms are permeating nearly every domain of human activity, including processes that make decisions about interests central to individual welfare and well-being. How do public perceptions of algorithmic decisionmaking in these domains compare with perceptions of traditional human decisionmaking? What kinds of judgments about the shortcomings of algorithmic decisionmaking processes underlie these perceptions? Will individuals be willing to hold algorithms accountable through legal channels for unfair, incorrect, or otherwise problematic decisions?

Answers to these questions matter at several levels. In a democratic society, a degree of public acceptance is needed for algorithms to become successfully integrated into decisionmaking processes. And public perceptions will shape how the harms and wrongs caused by algorithmic decisionmaking are handled. This report shares the results of a survey experiment designed to contribute to researchers’ understanding of how U.S. public perceptions are evolving in these respects in one high-stakes setting: decisions related to employment and unemployment…(More)”.

Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias

Curated on Posted on by Stefaan Verhulst

Paper by S. Lee et all: “Large language models (LLMs) have demonstrated their potential in social science research by emulating human perceptions and behaviors, a concept referred to as algorithmic fidelity. This study assesses the algorithmic fidelity and bias of LLMs by utilizing two nationally representative climate change surveys. The LLMs were conditioned on demographics and/or psychological covariates to simulate survey responses. The findings indicate that LLMs can effectively capture presidential voting behaviors but encounter challenges in accurately representing global warming perspectives when relevant covariates are not included. GPT-4 exhibits improved performance when conditioned on both demographics and covariates. However, disparities emerge in LLM estimations of the views of certain groups, with LLMs tending to underestimate worry about global warming among Black Americans. While highlighting the potential of LLMs to aid social science research, these results underscore the importance of meticulous conditioning, model selection, survey question format, and bias assessment when employing LLMs for survey simulation. Further investigation into prompt engineering and algorithm auditing is essential to harness the power of LLMs while addressing their inherent limitations…(More)”.

AI and Democracy’s Digital Identity Crisis

Curated on Posted on by Stefaan Verhulst

Essay by Shrey Jain, Connor Spelliscy, Samuel Vance-Law and Scott Moore: “AI-enabled tools have become sophisticated enough to allow a small number of individuals to run disinformation campaigns of an unprecedented scale. Privacy-preserving identity attestations can drastically reduce instances of impersonation and make disinformation easy to identify and potentially hinder. By understanding how identity attestations are positioned across the spectrum of decentralization, we can gain a better understanding of the costs and benefits of various attestations. In this paper, we discuss attestation types, including governmental, biometric, federated, and web of trust-based, and include examples such as e-Estonia, China’s social credit system, Worldcoin, OAuth, X (formerly Twitter), Gitcoin Passport, and EAS. We believe that the most resilient systems create an identity that evolves and is connected to a network of similarly evolving identities that verify one another. In this type of system, each entity contributes its respective credibility to the attestation process, creating a larger, more comprehensive set of attestations. We believe these systems could be the best approach to authenticating identity and protecting against some of the threats to democracy that AI can pose in the hands of malicious actors. However, governments will likely attempt to mitigate these risks by implementing centralized identity authentication systems; these centralized systems could themselves pose risks to the democratic processes they are built to defend. We therefore recommend that policymakers support the development of standards-setting organizations for identity, provide legal clarity for builders of decentralized tooling, and fund research critical to effective identity authentication systems…(More)”

The Bletchley Declaration

Curated on Posted on by Stefaan Verhulst

Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023: “In the context of our cooperation, and to inform action at the national and international levels, our agenda for addressing frontier AI risk will focus on:

  • identifying AI safety risks of shared concern, building a shared scientific and evidence-based understanding of these risks, and sustaining that understanding as capabilities continue to increase, in the context of a wider global approach to understanding the impact of AI in our societies.
  • building respective risk-based policies across our countries to ensure safety in light of such risks, collaborating as appropriate while recognising our approaches may differ based on national circumstances and applicable legal frameworks. This includes, alongside increased transparency by private actors developing frontier AI capabilities, appropriate evaluation metrics, tools for safety testing, and developing relevant public sector capability and scientific research.

In furtherance of this agenda, we resolve to support an internationally inclusive network of scientific research on frontier AI safety that encompasses and complements existing and new multilateral, plurilateral and bilateral collaboration, including through existing international fora and other relevant initiatives, to facilitate the provision of the best science available for policy making and the public good.

In recognition of the transformative positive potential of AI, and as part of ensuring wider international cooperation on AI, we resolve to sustain an inclusive global dialogue that engages existing international fora and other relevant initiatives and contributes in an open manner to broader international discussions, and to continue research on frontier AI safety to ensure that the benefits of the technology can be harnessed responsibly for good and for all. We look forward to meeting again in 2024…(More)”.

Enterprise Value and the Value of Data

Curated on Posted on by Stefaan Verhulst

Paper by Dan Ciuriak: “Data is often said to be the most valuable commodity of our age. It is a curiosity, therefore, that it remains largely invisible on the balance sheets of companies and largely unmeasured in our national economic accounts. This paper comments on the problems of using cost-based or transactions-based methods to establish value for a nation’s data in the system of national accounts and suggests that this should be complemented with value of economic rents attributable to data. This rent is part of enterprise value; accordingly, an indicator is required as an instrumental variable for the use of data for value creation within firms. The paper argues that traditional accounting looks through the firm to its tangible (and certain intangible) assets; that may no longer be feasible in measuring and understanding the data-driven economy…(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)”.