Paper by Kai P. Purnhagen and Alexandra Molitorisova: “Between 26-28 January 2024, a citizen jury was convened at the Schloss Thurnau in Upper Franconia, Germany to deliberate about new genomic techniques (NGTs) used in agriculture and food/feed production, ahead of the vote of the European Parliament and the Council of the European Union on the European Commission’s proposal for a regulation on plants obtained by certain NGTs and their food and feed. This report serves as a policy brief with all observations, assessments, and recommendations agreed by the jury with a minimum of 75 percent of the jurors’ votes. This report aims to provide policymakers, stakeholders, and the public with perspectives and considerations surrounding the use of NGTs in agriculture and food/feed production, as articulated by the members of the jury. There are 18 final recommendations produced by the jury. Through thoughtful analysis and dialogue, the jury sought to contribute to informed decision-making processes…(More)”.
Predicting IMF-Supported Programs: A Machine Learning Approach
Paper by Tsendsuren Batsuuri, Shan He, Ruofei Hu, Jonathan Leslie and Flora Lutz: “This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical…(More)”.
Facial Recognition Technology: Current Capabilities, Future Prospects, and Governance
Report by the National Academies of Sciences, Engineering, and Medicine: “Facial recognition technology is increasingly used for identity verification and identification, from aiding law enforcement investigations to identifying potential security threats at large venues. However, advances in this technology have outpaced laws and regulations, raising significant concerns related to equity, privacy, and civil liberties.
This report explores the current capabilities, future possibilities, and necessary governance for facial recognition technology. Facial Recognition Technology discusses legal, societal, and ethical implications of the technology, and recommends ways that federal agencies and others developing and deploying the technology can mitigate potential harms and enact more comprehensive safeguards…(More)”.
Commons-based Data Set: Governance for AI
Report by Open Future: “In this white paper, we propose an approach to sharing data sets for AI training as a public good governed as a commons. By adhering to the six principles of commons-based governance, data sets can be managed in a way that generates public value while making shared resources resilient to extraction or capture by commercial interests.
The purpose of defining these principles is two-fold:
We propose these principles as input into policy debates on data and AI governance. A commons-based approach can be introduced through regulatory means, funding and procurement rules, statements of principles, or data sharing frameworks. Secondly, these principles can also serve as a blueprint for the design of data sets that are governed and shared as a commons. To this end, we also provide practical examples of how these principles are being brought to life. Projects like Big Science or Common Voice have demonstrated that commons-based data sets can be successfully built.
These principles, tailored for the governance of AI data sets, are built on our previous work on Data Commons Primer. They are also the outcome of our research into the governance of AI datasets, including the AI_Commons case study. Finally, they are based on ongoing efforts to define how AI systems can be shared and made open, in which we have been participating – including the OSI-led process to define open-source AI systems, and the DPGA Community of Practice exploring AI systems as Digital Public Goods…(More)”.
The six principles for commons-based data set governance are as follows:
Digital public infrastructure and public value: What is ‘public’ about DPI?
Paper by David Eaves, Mariana Mazzucato and Beatriz Vasconcellos: “Digital Public Infrastructures (DPI) are becoming increasingly relevant in the policy and academic domains, with DPI not just being regulated, but funded and created by governments, international organisations, philanthropies and the private sector. However, these transformations are not neutral; they have a direction. This paper addresses how to ensure that DPI is not only regulated but created and governed for the common good by maximising public value creation. Our analysis makes explicit which normative values may be associated with DPI development. We also argue that normative values are necessary but not sufficient for maximising public value creation with DPI, and that a more proactive role of the state and governance are key. In this work, policymakers and researchers will find valuable frameworks for understanding where the value-creation elements of DPI come from and how to design a DPI governance that maximises public value…(More)”.
Responsible Data Re-use in Developing Countries: Social Licence through Public Engagement
Report by Stefaan Verhulst, Laura Sandor, Natalia Mejia Pardo, Elena Murray and Peter Addo: “The datafication era has transformed the technological landscape, digitizing multiple areas of human life and offering opportunities for societal progress through the re-use of digital data. Developing countries stand to benefit from datafication but are faced with challenges like insufficient data quality and limited infrastructure. One of the primary obstacles to unlocking data re-use lies in agency asymmetries—disparities in decision-making authority among stakeholders—which fuel public distrust. Existing consent frameworks amplify the challenge, as they are individual-focused, lack information, and fail to address the nuances of data re-use. To address these limitations, a Social License for re-use becomes imperative—a community-focused approach that fosters responsible data practices and benefits all stakeholders. This shift is crucial for establishing trust and collaboration, and bridging the gap between institutions, governments, and citizens…(More)”.
Untapped
About: “Twenty-first century collective intelligence- combining people’s knowledge and skills, new forms of data and increasingly, technology – has the untapped potential to transform the way we understand and act on climate change.
Collective intelligence for climate action in the Global South takes many forms: from crowdsourcing of indigenous knowledge to preserve biodiversity to participatory monitoring of extreme heat and farmer experiments adapting crops to weather variability.
This research analyzes 100+ climate case studies across 45 countries that tap into people’s participation and use new forms of data. This research illustrates the potential that exists in communities everywhere to contribute to climate adaptation and mitigation efforts. It also aims to shine a light on practical ways in which these initiatives could be designed and further developed so this potential can be fully unleashed…(More)”.
Data Disquiet: Concerns about the Governance of Data for Generative AI
Paper by Susan Aaronson: “The growing popularity of large language models (LLMs) has raised concerns about their accuracy. These chatbots can be used to provide information, but it may be tainted by errors or made-up or false information (hallucinations) caused by problematic data sets or incorrect assumptions made by the model. The questionable results produced by chatbots has led to growing disquiet among users, developers and policy makers. The author argues that policy makers need to develop a systemic approach to address these concerns. The current piecemeal approach does not reflect the complexity of LLMs or the magnitude of the data upon which they are based, therefore, the author recommends incentivizing greater transparency and accountability around data-set development…(More)”.
Navigating the Future of Work: Perspectives on Automation, AI, and Economic Prosperity
Report by Erik Brynjolfsson, Adam Thierer and Daron Acemoglu: “Experts and the media tend to overestimate technology’s negative impact on employment. Case studies suggest that technology-induced unemployment fears are often exaggerated, evidenced by the McKinsey Global Institute reversing its AI forecasts and the growth in jobs predicted to be at high risk of automation.
Flexible work arrangements, technical recertification, and creative apprenticeship models offer real-time learning and adaptable skills development to prepare workers for future labor market and technological changes.
AI can potentially generate new employment opportunities, but the complex transition for workers displaced by automation—marked by the need for retraining and credentialing—indicates that the productivity benefits may not adequately compensate for job losses, particularly among low-skilled workers.
Instead of resorting to conflictual relationships, labor unions in the US must work with employers to support firm automation while simultaneously advocating for worker skill development, creating a competitive business enterprise built on strong worker representation similar to that found in Germany…(More)”.
Youth Media Literacy Program Fact Checking Manual
Internews: “As part of the USAID-funded Advancing Rights in Southern Africa Program (ARISA), Internews developed the Youth Media Literacy Program to enhance the digital literacy skills of young people. Drawing from university journalism students, and young leaders from civil society organizations in Botswana, Eswatini, Lesotho, and South Africa, the program equipped 124 young people to apply critical thinking to online communication and practice improved digital hygiene and digital security practices. The Youth Media Literacy Program Fact Checking Manual was developed to provide additional support and tools to combat misinformation and disinformation and improve online behavior and security…(More)”.