Editorial Paper of Special Issue edited by Ludovica Paseri: “This paper analyses the relationship between open science policies and data protection. In order to tackle the research data paradox of the contemporary science, i.e., the tension between the pursuit of data-driven scientific research and the crisis of repeatability or reproducibility of science, a theoretical perspective suggests a potential convergence between open science and data protection. Both fields regard governance mechanisms that shall take into account the plurality of interests at stake. The aim is to shed light on the processing of personal data for scientific research purposes in the context of open science. The investigation supports a threefold need: that of broadening the legal debate; of expanding the territorial scope of the analysis, in addition to the extra-territoriality effects of the European Union’s law; and an interdisciplinary discussion. Based on these needs, four perspectives are then identified, that encompass the challenges related to data processing in the context of open science: (i) the contextual and epistemological perspectives; (ii) the legal coordination perspectives; (iii) the governance perspectives; and (iv) the technical perspectives…(More)”.
Initial policy considerations for generative artificial intelligence
OECD Report: “Generative artificial intelligence (AI) creates new content in response to prompts, offering transformative potential across multiple sectors such as education, entertainment, healthcare and scientific research. However, these technologies also pose critical societal and policy challenges that policy makers must confront: potential shifts in labour markets, copyright uncertainties, and risk associated with the perpetuation of societal biases and the potential for misuse in the creation of disinformation and manipulated content. Consequences could extend to the spreading of mis- and disinformation, perpetuation of discrimination, distortion of public discourse and markets, and the incitement of violence. Governments recognise the transformative impact of generative AI and are actively working to address these challenges. This paper aims to inform these policy considerations and support decision makers in addressing them…(More)”.
More Companies Are Disclosing Their ESG Data, but Confusion on How Persists
Article by David Breg: “Public companies in the U.S. are increasingly disclosing sustainability information, but many say they find it a challenge to report fundamental climate data that many regulators around the globe likely will require under incoming mandatory reporting standards.
Nearly two-thirds of respondents said their company was disclosing environmental, social and governance information, up from 56% in the prior year, according to the annual survey of sustainability officials that WSJ Pro conducted this spring.
However, there was little consensus on which framework to use and respondents highlighted three fundamental types of information as their three biggest environmental reporting challenges: Greenhouse-gas emissions, climate-change risk and energy management.
The proportion of companies disclosing sustainability and ESG information was 63%, up from 56% last year. Those that don’t yet report this data but plan to was 16%, down from 25% last year. About one-fifth of respondents said their organization had no plans to report their progress, virtually unchanged from last year. Breaking that down, a quarter of private companies don’t plan any ESG reporting, while only 7% of public companies felt the same.
Regulators around the globe are finalizing rules that would require companies to publish standardized information after years of patchy voluntary ESG reporting based on a host of frameworks. California’s governor has said he would soon sign that state’s requirements into law. The U.S. Securities and Exchange Commission’s rules are expected later this year. European regulations are already in place and many other countries are also working on standards. The International Sustainability Standards Board hopes its climate framework, completed this past summer, becomes the global baseline.
While it is mostly public companies that face mandatory requirements, even private businesses face increased scrutiny of their sustainability and ESG policies from stakeholders including shareholders, eco-conscious consumers, suppliers, insurers and lenders…(More)”.
Surveys Provide Insight Into Three Factors That Encourage Open Data and Science
Article by Joshua Borycz, Alison Specht and Kevin Crowston: “Open Science is a game changer for researchers and the research community. The UNESCO Open Science recommendations in 2021 suggest that the practice of Open Science is a win-win for researchers as they gain from others’ work while making contributions, which in turn benefits the community, as transparency of conclusions and hence confidence in new knowledge improves.
Over a 10-year period Carol Tenopir of DataONE and her team conducted a global survey of scientists, managers and government workers involved in broad environmental science activities about their willingness to share data and their opinion of the resources available to do so (Tenopir et al., 2011, 2015, 2018, 2020). Comparing the responses over that time shows a general increase in the willingness to share data (and thus engage in open science).
A higher willingness to share data corresponded with a decrease in satisfaction with data sharing resources across nations.
The most surprising result was that a higher willingness to share data corresponded with a decrease in satisfaction with data sharing resources across nations (e.g., skills, tools, training) (Fig.1). That is, researchers who did not want to share data were satisfied with the available resources, and those that did want to share data were dissatisfied. Researchers appear to only discover that the tools are insufficient when they begin the hard work of engaging in open science practices. This indicates that a cultural shift in the attitudes of researchers needs to precede the development of support and tools for data management…(More)”.
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
Paper by Fabrizio Dell’Acqua et al: “The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine the performance implications of AI on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company. After establishing a performance baseline on a similar task, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. We suggest that the capabilities of AI create a “jagged technological frontier” where some tasks are easily done by AI, while others, though seemingly similar in difficulty level, are outside the current capability of AI. For each one of a set of 18 realistic consulting tasks within the frontier of AI capabilities, consultants using AI were significantly more productive (they completed 12.2% more tasks on average, and completed task 25.1% more quickly), and produced significantly higher quality results (more than 40% higher quality compared to a control group). Consultants across the skills distribution benefited significantly from having AI augmentation, with those below the average performance threshold increasing by 43% and those above increasing by 17% compared to their own scores. For a task selected to be outside the frontier, however, consultants using AI were 19 percentage points less likely to produce correct solutions compared to those without AI. Further, our analysis shows the emergence of two distinctive patterns of successful AI use by humans along a spectrum of human-AI integration. One set of consultants acted as “Centaurs,” like the mythical halfhorse/half-human creature, dividing and delegating their solution-creation activities to the AI or to themselves. Another set of consultants acted more like “Cyborgs,” completely integrating their task flow with the AI and continually interacting with the technology…(More)”.
Artificial intelligence in local governments: perceptions of city managers on prospects, constraints and choices
Paper by Tan Yigitcanlar, Duzgun Agdas & Kenan Degirmenci: “Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of AI is on the rise in urban policy circles, there is limited understanding and lack of empirical studies on the city manager perceptions concerning urban AI systems. Bridging this gap is the rationale of this study. The methodological approach adopted in this study is twofold. First, the study collects data through semi-structured interviews with city managers from Australia and the US. Then, the study analyses the data using the summative content analysis technique with two data analysis software. The analysis identifies the following themes and generates insights into local government services: AI adoption areas, cautionary areas, challenges, effects, impacts, knowledge basis, plans, preparedness, roadblocks, technologies, deployment timeframes, and usefulness. The study findings inform city managers in their efforts to deploy AI in their local government operations, and offer directions for prospective research…(More)”.
AI and the next great tech shift
Book review by John Thornhill: “When the South Korean political activist Kim Dae-jung was jailed for two years in the early 1980s, he powered his way through some 600 books in his prison cell, such was his thirst for knowledge. One book that left a lasting impression was The Third Wave by the renowned futurist Alvin Toffler, who argued that an imminent information revolution was about to transform the world as profoundly as the preceding agricultural and industrial revolutions.
“Yes, this is it!” Kim reportedly exclaimed. When later elected president, Kim referred to the book many times in his drive to turn South Korea into a technological powerhouse.
Forty-three years after the publication of Toffler’s book, another work of sweeping futurism has appeared with a similar theme and a similar name. Although the stock in trade of futurologists is to highlight the transformational and the unprecedented, it is remarkable how much of their output appears the same.
The chief difference is that The Coming Wave by Mustafa Suleyman focuses more narrowly on the twin revolutions of artificial intelligence and synthetic biology. But the author would surely be delighted if his book were to prove as influential as Toffler’s in prompting politicians to action.
As one of the three co-founders of DeepMind, the London-based AI research company founded in 2010, and now chief executive of the AI start-up Inflection, Suleyman has been at the forefront of the industry for more than a decade. The Coming Wave bristles with breathtaking excitement about the extraordinary possibilities that the revolutions in AI and synthetic biology could bring about.
AI, we are told, could unlock the secrets of the universe, cure diseases and stretch the bounds of imagination. Biotechnology can enable us to engineer life and transform agriculture. “Together they will usher in a new dawn for humanity, creating wealth and surplus unlike anything ever seen,” he writes.
But what is striking about Suleyman’s heavily promoted book is how the optimism of his will is overwhelmed by the pessimism of his intellect, to borrow a phrase from the Marxist philosopher Antonio Gramsci. For most of history, the challenge of technology has been to unleash its power, Suleyman writes. Now the challenge has flipped.
In the 21st century, the dilemma will be how to contain technology’s power given the capabilities of these new technologies have exploded and the costs of developing them have collapsed. “Containment is not, on the face of it, possible. And yet for all our sakes, containment must be possible,” he writes…(More)”.
Data Commons
Paper by R. V. Guha et al: “Publicly available data from open sources (e.g., United States Census Bureau (Census), World Health Organization (WHO), Intergovernmental Panel on Climate Change (IPCC) are vital resources for policy makers, students and researchers across different disciplines. Combining data from different sources requires the user to reconcile the differences in schemas, formats, assumptions, and more. This data wrangling is time consuming, tedious and needs to be repeated by every user of the data. Our goal with Data Commons (DC) is to help make public data accessible and useful to those who want to understand this data and use it to solve societal challenges and opportunities. We do the data processing and make the processed data widely available via standard schemas and Cloud APIs. Data Commons is a distributed network of sites that publish data in a common schema and interoperate using the Data Commons APIs. Data from different Data Commons can be ‘joined’ easily. The aggregate of these Data Commons can be viewed as a single Knowledge Graph. This Knowledge Graph can then be searched over using Natural Language questions utilizing advances in Large Language Models. This paper describes the architecture of Data Commons, some of the major deployments and highlights directions for future work…(More)”.
Data Collaboratives
Policy Brief by Center for the Governance of Change: “Despite the abundance of data generated, it is becoming increasingly clear that its accessibility and advantages are not equitably or effectively distributed throughout society. Data asymmetries, driven in large part by deeply entrenched inequalities and lack of incentives by many public- and private-sector organizations to collaborate, are holding back the public good potential of data and hindering progress and innovation in key areas such as financial inclusion, health, and the future of work.
More (and better) collaboration is needed to address the data asymmetries that exist across society, but early efforts at opening data have fallen short of achieving their intended aims. In the EU, the proposed Data Act is seeking to address these shortcomings and make more data available for public use by setting up new rules on data sharing. However, critics say its current reading risks limiting the potential for delivering innovative solutions by failing to establish cross-sectoral data-sharing frameworks, leaving the issue of public data stewardship off the table, and avoiding the thorny question of business incentives.
This policy brief, based on Stefaan Verhulst’s recent policy paper for the Center for the Governance of Change, argues that data collaboratives, an emerging model of collaboration in which participants from different sectors exchange data to solve public problems, offer a promising solution to address these data asymmetries and contribute to a healthy data economy that can benefit society as a whole. However, data collaboratives require a systematic, sustainable, and responsible approach to be successful, with a particular focus on..(More):
| Establishing a new science of questions, to help identify the most pressing public and private challenges that can be addressed with data sharing. | Fostering a new profession of data stewards, to promote a culture of responsible sharing within organizations and recognize opportunities for productive collaboration. | Clarifying incentives, to bring the private sector to the table and help operationalize data collaboration, ideally with some sort of market-led compensation model. |
| Establishing a social license for data reuse, to promote trust among stakeholders through public engagement, data stewardship, and an enabling regulatory framework. | Becoming more data-driven about data, to improve our understanding of collaboration, build sustainable initiatives, and achieve project accountability. |
Sharing Health Data: The Why, the Will, and the Way Forward.
Book edited by Grossmann C, Chua PS, Ahmed M, et al. : “Sharing health data and information1 across stakeholder groups is the bedrock of a learning health system. As data and information are increasingly combined across various sources, their generative value to transform health, health care, and health equity increases significantly. Facilitating this potential is an escalating surge of digital technologies (i.e., cloud computing, broadband and wireless solutions, digital health technologies, and application programming interfaces [APIs]) that, with each successive generation, not only enhance data sharing, but also improve in their ability to preserve privacy and identify and mitigate cybersecurity risks. These technological advances, coupled with notable policy developments, new interoperability standards (particularly the Fast Healthcare Interoperability Resources [FHIR] standard), and the launch of innovative payment models within the last decade, have resulted in a greater recognition of the value of health data sharing among patients, providers, and researchers. Consequently, a number of data sharing collaborations are emerging across the health care ecosystem.
Unquestionably, the COVID-19 pandemic has had a catalytic effect on this trend. The criticality of swift data exchange became evident at the outset of the pandemic, when the scientific community sought answers about the novel SARS-CoV-2 virus and emerging disease. Then, as the crisis intensified, data sharing graduated from a research imperative to a societal one, with a clear need to urgently share and link data across multiple sectors and industries to curb the effects of the pandemic and prevent the next one.
In spite of these evolving attitudes toward data sharing and the ubiquity of data-sharing partnerships, barriers persist. The practice of health data sharing occurs unevenly, prominent in certain stakeholder communities while absent in others. A stark contrast is observed between the volume, speed, and frequency with which health data is aggregated and linked—oftentimes with non-traditional forms of health data—for marketing purposes, and the continuing challenges patients experience in contributing data to their own health records. In addition, there are varying levels of data sharing. Not all types of data are shared in the same manner and at the same level of granularity, creating a patchwork of information. As highlighted by the gaps observed in the haphazard and often inadequate sharing of race and ethnicity data during the pandemic, the consequences can be severe—impacting the allocation of much-needed resources and attention to marginalized communities. Therefore, it is important to recognize the value of data sharing in which stakeholder participation is equitable and comprehensive— not only for achieving a future ideal state in health care, but also for redressing long-standing inequities…(More)”