Paper by C. Huang & L. Soete: “In history, open science has been effective in facilitating knowledge sharing and promoting and diffusing innovations. However, as a result of geopolitical tensions, technological sovereignty has recently been increasingly emphasized in various countries’ science and technology policy making, posing a challenge to open science policy. In this paper, we argue that the European Union significantly benefits from and contributes to open science and should continue to support it. Similarly, China embraced foreign technologies and engaged in open science as its economy developed rapidly in the last 40 years. Today both economies could learn from each other in finding the right balance between open science and technological sovereignty particularly given the very different policy experience and the urgency of implementing new technologies addressing the grand challenges such as climate change faced by mankind…(More)”.
Nurturing innovation through intelligent failure: The art of failing on purpose
Paper by Alessandro Narduzzo and Valentina Forrer: “Failure, even in the context of innovation, is primarily conceived and experienced as an inevitable (e.g., innovation funnel) or unintended (e.g., unexpected drawbacks) outcome. This paper aims to provide a more systematic understanding of innovation failure by considering and problematizing the case of “intelligent failures”, namely experiments that are intentionally designed and implemented to explore technological and market uncertainty. We conceptualize intelligent failure through an epistemic perspective that recognizes its contribution to challenging and revising the organizational knowledge system. We also outline an original process model of intelligent failure that fully reveals its potential and distinctiveness in the context of learning from failure (i.e., failure as an outcome vs failure of expectations and initial beliefs), analyzing and comparing intended and unintended innovation failures. By positioning intelligent failure in the context of innovation and explaining its critical role in enhancing the ability of innovative firms to achieve breakthroughs, we identify important landmarks for practitioners in designing an intelligent failure approach to innovation…(More)”.
Artificial intelligence for modelling infectious disease epidemics
Paper by Moritz U. G. Kraemer et al: “Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI…(More)”.
Moving Toward the FAIR-R principles: Advancing AI-Ready Data
Paper by Stefaan Verhulst, Andrew Zahuranec and Hannah Chafetz: “In today’s rapidly evolving AI ecosystem, making data ready for AI-optimized for training, fine-tuning, and augmentation-is more critical than ever. While the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) have guided data management and open science, they do not inherently address AI-specific needs. Expanding FAIR to FAIR-R, incorporating Readiness for AI, could accelerate the responsible use of open data in AI applications that serve the public interest. This paper introduces the FAIR-R framework and identifies current efforts for enhancing AI-ready data through improved data labeling, provenance tracking, and new data standards. However, key challenges remain: How can data be structured for AI without compromising ethics? What governance models ensure equitable access? How can AI itself be leveraged to improve data quality? Answering these questions is essential for unlocking the full potential of AI-driven innovation while ensuring responsible and transparent data use…(More)”.
Presenting the StanDat database on international standards: improving data accessibility on marginal topics
Article by Solveig Bjørkholt: “This article presents an original database on international standards, constructed using modern data gathering methods. StanDat facilitates studies into the role of standards in the global political economy by (1) being a source for descriptive statistics, (2) enabling researchers to assess scope conditions of previous findings, and (3) providing data for new analyses, for example the exploration of the relationship between standardization and trade, as demonstrated in this article. The creation of StanDat aims to stimulate further research into the domain of standards. Moreover, by exemplifying data collection and dissemination techniques applicable to investigating less-explored subjects in the social sciences, it serves as a model for gathering, systematizing, and sharing data in areas where information is plentiful yet not readily accessible for research…(More)”.
Citizen participation and technology: lessons from the fields of deliberative democracy and science and technology studies
Paper by Julian “Iñaki” Goñi: “Calls for democratising technology are pervasive in current technological discourse. Indeed, participating publics have been mobilised as a core normative aspiration in Science and Technology Studies (STS), driven by a critical examination of “expertise”. In a sense, democratic deliberation became the answer to the question of responsible technological governance, and science and technology communication. On the other hand, calls for technifying democracy are ever more pervasive in deliberative democracy’s discourse. Many new digital tools (“civic technologies”) are shaping democratic practice while navigating a complex political economy. Moreover, Natural Language Processing and AI are providing novel alternatives for systematising large-scale participation, automated moderation and setting up participation. In a sense, emerging digital technologies became the answer to the question of how to augment collective intelligence and reconnect deliberation to mass politics. In this paper, I explore the mutual shaping of (deliberative) democracy and technology (studies), highlighting that without careful consideration, both disciplines risk being reduced to superficial symbols in discourses inclined towards quick solutionism. This analysis highlights the current disconnect between Deliberative Democracy and STS, exploring the potential benefits of fostering closer links between the two fields. Drawing on STS insights, the paper argues that deliberative democracy could be enriched by a deeper engagement with the material aspects of democratic processes, the evolving nature of civic technologies through use, and a more critical approach to expertise. It also suggests that STS scholars would benefit from engaging more closely with democratic theory, which could enhance their analysis of public participation, bridge the gap between descriptive richness and normative relevance, and offer a more nuanced understanding of the inner functioning of political systems and politics in contemporary democracies…(More)”.
Emerging Practices in Participatory AI Design in Public Sector Innovation
Paper by Devansh Saxena, et al: “Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design – or the use of public participation and community engagement – in the scoping, design, adoption, and implementation of public sector algorithms…(More)”.
Data equity and official statistics in the age of private sector data proliferation
Paper by Pietro Gennari: “Over the last few years, the private sector has become a primary generator of data due to widespread digitisation of the economy and society, the use of social media platforms, and advancements of technologies like the Internet of Things and AI. Unlike traditional sources, these new data streams often offer real-time information and unique insights into people’s behaviour, social dynamics, and economic trends. However, the proprietary nature of most private sector data presents challenges for public access, transparency, and governance that have led to fragmented, often conflicting, data governance arrangements worldwide. This lack of coherence can exacerbate inequalities, limit data access, and restrict data’s utility as a global asset.
Within this context, data equity has emerged as one of the key principles at the basis of any proposal of new data governance framework. The term “data equity” refers to the fair and inclusive access, use, and distribution of data so that it benefits all sections of society, regardless of socioeconomic status, race, or geographic location. It involves making sure that the collection, processing, and use of data does not disproportionately benefit or harm any particular group and seeks to address disparities in data access and quality that can perpetuate social and economic inequalities. This is important because data systems significantly influence access to resources and opportunities in society. In this sense, data equity aims to correct imbalances that have historically affected various groups and to ensure that decision-making based on data does not perpetuate these inequities…(More)”.
When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy
Paper by Sara Marcucci, Stefaan Verhulst and María Esther Cervantes: “The various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, ranging from predictive models to foresight techniques, emerge as valuable tools for policymakers. These methods, now bolstered by advancements in technology and leveraging nontraditional data sources, can offer a pathway to develop more precise, responsive, and forward-thinking policies.
This paper seeks to map out the rapidly evolving domain of anticipatory methods in the realm of migration policy, capturing the trend toward integrating quantitative and qualitative methodologies and harnessing novel tools and data. It introduces a new taxonomy designed to organize these methods into three core categories: Experience-based, Exploration-based, and Expertise-based. This classification aims to guide policymakers in selecting the most suitable methods for specific contexts or questions, thereby enhancing migration policies…(More)”
On the Shoulders of Others: The Importance of Regulatory Learning in the Age of AI
Paper by Urs Gasser and Viktor Mayer-Schonberger: “…International harmonization of regulation is the right strategy when the appropriate regulatory ends and means are sufficiently clear to reap efficiencies of scale and scope. When this is not the case, a push for efficiency through uniformity is premature and may lead to a suboptimal regulatory lock-in: the establishment of a rule framework that is either inefficient in the use of its means to reach the intended goal, or furthers the wrong goal, or both.
A century ago, economist Joseph Schumpeter suggested that companies have two distinct strategies to achieve success. The first is to employ economies of scale and scope to lower their cost. It’s essentially a push for improved efficiency. The other strategy is to invent a new product (or production process) that may not, at least initially, be hugely efficient, but is nevertheless advantageous because demand for the new product is price inelastic. For Schumpeter this was the essence of innovation. But, as Schumpeter also argued, innovation is not a simple, linear, and predictable process. Often, it happens in fits and starts, and can’t be easily commandeered or engineered.
As innovation is hard to foresee and plan, the best way to facilitate it is to enable a wide variety of different approaches and solutions. Public policies in many countries to foster startups and entrepreneurship stems from this view. Take, for instance, the policy of regulatory sandboxing, i.e. the idea that for a limited time certain sectors should not or only lightly be regulated…(More)”.