Paper by Jenny Lewis: “Around the world in recent times, numerous policy design labs have been established, related to a rising focus on the need for public sector innovation. These labs are a response to the challenging nature of many societal problems and often have a purpose of navigating uncertainty. They do this by “labbing” ill-structured problems through moving them into an experimental environment, outside of traditional government structures, and using a design-for-policy approach. Labs can, therefore, be considered as a particular type of procedural policy tool, used in attempts to change how policy is formulated and implemented to address uncertainty. This paper considers the role of policy design labs in learning and explores the broader governance context they are embedded within. It examines whether labs have the capacity to innovate and also retain and circulate learning to other policy actors. It argues that labs have considerable potential to change the spaces of policymaking at the micro level and innovate, but for learning to be kept rather than lost, innovation needs to be institutionalized in governing structures at higher levels…(More)”.
It’s just distributed computing: Rethinking AI governance
Paper by Milton L. Mueller: “What we now lump under the unitary label “artificial intelligence” is not a single technology, but a highly varied set of machine learning applications enabled and supported by a globally ubiquitous system of distributed computing. The paper introduces a 4 part conceptual framework for analyzing the structure of that system, which it labels the digital ecosystem. What we now call “AI” is then shown to be a general functionality of distributed computing. “AI” has been present in primitive forms from the origins of digital computing in the 1950s. Three short case studies show that large-scale machine learning applications have been present in the digital ecosystem ever since the rise of the Internet. and provoked the same public policy concerns that we now associate with “AI.” The governance problems of “AI” are really caused by the development of this digital ecosystem, not by LLMs or other recent applications of machine learning. The paper then examines five recent proposals to “govern AI” and maps them to the constituent elements of the digital ecosystem model. This mapping shows that real-world attempts to assert governance authority over AI capabilities requires systemic control of all four elements of the digital ecosystem: data, computing power, networks and software. “Governing AI,” in other words, means total control of distributed computing. A better alternative is to focus governance and regulation upon specific applications of machine learning. An application-specific approach to governance allows for a more decentralized, freer and more effective method of solving policy conflicts…(More)”
Empowering open data sharing for social good: a privacy-aware approach
Paper by Tânia Carvalho et al: “The Covid-19 pandemic has affected the world at multiple levels. Data sharing was pivotal for advancing research to understand the underlying causes and implement effective containment strategies. In response, many countries have facilitated access to daily cases to support research initiatives, fostering collaboration between organisations and making such data available to the public through open data platforms. Despite the several advantages of data sharing, one of the major concerns before releasing health data is its impact on individuals’ privacy. Such a sharing process should adhere to state-of-the-art methods in Data Protection by Design and by Default. In this paper, we use a Covid-19 data set from Portugal’s second-largest hospital to show how it is feasible to ensure data privacy while improving the quality and maintaining the utility of the data. Our goal is to demonstrate how knowledge exchange in multidisciplinary teams of healthcare practitioners, data privacy, and data science experts is crucial to co-developing strategies that ensure high utility in de-identified data…(More).”
Citizens’ assemblies in fragile and conflict-affected settings
Article by Nicole Curato, Lucy J Parry, and Melisa Ross: “Citizens’ assemblies have become a popular form of citizen engagement to address complex issues like climate change, electoral reform, and assisted dying. These assemblies bring together randomly selected citizens to learn about an issue, consider diverse perspectives, and develop collective recommendations. Growing evidence highlights their ability to depolarise views, enhance political efficacy, and rebuild trust in institutions. However, the story of citizens’ assemblies is more complicated on closer look. This demanding form of political participation is increasingly critiqued for its limited impact, susceptibility to elite influence, and rigid design features unsuitable to local contexts. These challenges are especially pronounced in fragile and conflict-affected settings, where trust is low, expectations for action are high, and local ownership is critical. Well-designed assemblies can foster civic trust and dialogue across difference, but poorly implemented ones risk exacerbating tensions.
This article offers a framework to examine citizens’ assemblies in fragile and conflict-affected settings, focusing on three dimensions: deliberative design, deliberative integrity, and deliberative sustainability. We apply this framework to cases in Bosnia and France to illustrate both the transformative potential and the challenges of citizens’ assemblies when held amidst or in the aftermath of political conflict. This article argues that citizens’ assemblies can be vital mechanisms to manage intractable conflict, provided they are designed with intentionality, administered deliberatively, and oriented towards sustainability…(More)”.
Data Stewardship Decoded: Mapping Its Diverse Manifestations and Emerging Relevance at a time of AI
Paper by Stefaan Verhulst: “Data stewardship has become a critical component of modern data governance, especially with the growing use of artificial intelligence (AI). Despite its increasing importance, the concept of data stewardship remains ambiguous and varies in its application. This paper explores four distinct manifestations of data stewardship to clarify its emerging position in the data governance landscape. These manifestations include a) data stewardship as a set of competencies and skills, b) a function or role within organizations, c) an intermediary organization facilitating collaborations, and d) a set of guiding principles.
The paper subsequently outlines the core competencies required for effective data stewardship, explains the distinction between data stewards and Chief Data Officers (CDOs), and details the intermediary role of stewards in bridging gaps between data holders and external stakeholders. It also explores key principles aligned with the FAIR framework (Findable, Accessible, Interoperable, Reusable) and introduces the emerging principle of AI readiness to ensure data meets the ethical and technical requirements of AI systems.
The paper emphasizes the importance of data stewardship in enhancing data collaboration, fostering public value, and managing data reuse responsibly, particularly in the era of AI. It concludes by identifying challenges and opportunities for advancing data stewardship, including the need for standardized definitions, capacity building efforts, and the creation of a professional association for data stewardship…(More)”
Flipping data on its head: Differing conceptualisations of data and the implications for actioning Indigenous data sovereignty principles
Paper by Stephanie Cunningham-Reimann et al: “Indigenous data sovereignty is of global concern. The power of data through its multitude of uses can cause harm to Indigenous Peoples, communities, organisations and Nations in Canada and globally. Indigenous research principles play a vital role in guiding researchers, scholars and policy makers in their careers and roles. We define data, data sovereignty principles, ways of practicing Indigenous research principles, and recommendations for applying and actioning Indigenous data sovereignty through culturally safe self-reflection, interpersonal and reciprocal relationships built upon respect, reciprocity, relevance, responsibility and accountability. Research should be co-developed, co-led, and co-disseminated in partnership with Indigenous Peoples, communities, organisations and/or nations to build capacity, support self-determination, and reduce harms produced through the analysis and dissemination of research findings. OCAP® (Ownership, Control, Access & Possession), OCAS (Ownership, Control, Access & Stewardship), Inuit Qaujimajatuqangit principles in conjunction the 4Rs (respect, relevance, reciprocity & responsibility) and cultural competency including self-examination of the 3Ps (power, privilege, and positionality) of researchers, scholars and policy makers can be challenging, but will amplify the voices and understandings of Indigenous research by implementing Indigenous data sovereignty in Canada…(More)”
Developing a theory of robust democracy
Paper by Eva Sørensen and Mark E. Warren: “While many democratic theorists recognise the necessity of reforming liberal democracies to keep pace with social change, they rarely consider what enables such reform. In this conceptual article, we suggest that liberal democracies are politically robust when they are able to continuously adapt and innovate how they operate when doing so is necessary to continue to serve key democratic functions. These functions include securing the empowered inclusion of those affected, collective agenda setting and will formation, and the making of joint decisions. Three current challenges highlight the urgency of adapting and innovating liberal democracies to become more politically robust: an increasingly assertive political culture, the digitalisation of political communication and increasing global interdependencies. A democratic theory of political robustness emphasises the need to strengthen the capacity of liberal democracies to adapt and innovate in response to changes, just as it helps to frame the necessary adaptations and innovations in times such as the present…(More)”
Leveraging Crowd Intelligence to Enhance Fairness and Accuracy in AI-powered Recruitment Decisions
Paper by Zhen-Song Chen and Zheng Ma: “Ensuring fair and accurate hiring outcomes is critical for both job seekers’ economic opportunities and organizational development. This study addresses the challenge of mitigating biases in AI-powered resume screening systems by leveraging crowd intelligence, thereby enhancing problem-solving efficiency and decision-making quality. We propose a novel counterfactual resume-annotation method based on a causal model to capture and correct biases from human resource (HR) representatives, providing robust ground truth data for supervised machine learning. The proposed model integrates multiple language embedding models and diverse HR-labeled data to train a cohort of resume-screening agents. By training 60 such agents with different models and data, we harness their crowd intelligence to optimize for three objectives: accuracy, fairness, and a balance of both. Furthermore, we develop a binary bias-detection model to visualize and analyze gender bias in both human and machine outputs. The results suggest that harnessing crowd intelligence using both accuracy and fairness objectives helps AI systems robustly output accurate and fair results. By contrast, a sole focus on accuracy may lead to severe fairness degradation, while, conversely, a sole focus on fairness leads to a relatively minor loss of accuracy. Our findings underscore the importance of balancing accuracy and fairness in AI-powered resume-screening systems to ensure equitable hiring outcomes and foster inclusive organizational development…(More)”
Problems of participatory processes in policymaking: a service design approach
Paper by Susana Díez-Calvo, Iván Lidón, Rubén Rebollar, Ignacio Gil-Pérez: “This study aims to identify and map the problems of participatory processes in policymaking through a Service Design approach….Fifteen problems of participatory processes in policymaking were identified, and some differences were observed in the perception of these problems between the stakeholders responsible for designing and implementing the participatory processes (backstage stakeholders) and those who are called upon to participate (frontstage stakeholders). The problems were found to occur at different stages of the service and to affect different stakeholders. A number of design actions were proposed to help mitigate these problems from a human-centred approach. These included process improvements, digital opportunities, new technologies and staff training, among others…(More)”.
The disparities and development trajectories of nations in achieving the sustainable development goals
Paper by Fengmei Ma, et al: “The Sustainable Development Goals (SDGs) provide a comprehensive framework for societal progress and planetary health. However, it remains unclear whether universal patterns exist in how nations pursue these goals and whether key development areas are being overlooked. Here, we apply the product space methodology, widely used in development economics, to construct an ‘SDG space of nations’. The SDG space models the relative performance and specialization patterns of 166 countries across 96 SDG indicators from 2000 to 2022. Our SDG space reveals a polarized global landscape, characterized by distinct groups of nations, each specializing in specific development indicators. Furthermore, we find that as countries improve their overall SDG scores, they tend to modify their sustainable development trajectories, pursuing different development objectives. Additionally, we identify orphaned SDG indicators — areas where certain country groups remain under-specialized. These patterns, and the SDG space more broadly, provide a high-resolution tool to understand and evaluate the progress and disparities of countries towards achieving the SDGs…(More)”