AI and the Future of Government: Unexpected Effects and Critical Challenges

Curated on Posted on by Stefaan Verhulst

Policy Brief by Tiago C. Peixoto, Otaviano Canuto, and Luke Jordan: “Based on observable facts, this policy paper explores some of the less- acknowledged yet critically important ways in which artificial intelligence (AI) may affect the public sector and its role. Our focus is on those areas where AI’s influence might be understated currently, but where it has substantial implications for future government policies and actions.

We identify four main areas of impact that could redefine the public sector role, require new answers from it, or both. These areas are the emergence of a new language-based digital divide, jobs displacement in the public administration, disruptions in revenue mobilization, and declining government responsiveness.

This discussion not only identifies critical areas but also underscores the importance of transcending conventional approaches in tackling them. As we examine these challenges, we shed light on their significance, seeking to inform policymakers and stakeholders about the nuanced ways in which AI may quietly, yet profoundly, alter the public sector landscape…(More)”.

AI Accountability Policy Report

Curated on Posted on by Stefaan Verhulst

Report by NTIA: “Artificial intelligence (AI) systems are rapidly becoming part of the fabric of everyday American life. From customer service to image generation to manufacturing, AI systems are everywhere.

Alongside their transformative potential for good, AI systems also pose risks of harm. These risks include inaccurate or false outputs; unlawful discriminatory algorithmic decision making; destruction of jobs and the dignity of work; and compromised privacy, safety, and security. Given their influence and ubiquity, these systems must be subject to security and operational mechanisms that mitigate risk and warrant stakeholder trust that they will not cause harm….


The AI Accountability Policy Report conceives of accountability as a chain of inputs linked to consequences. It focuses on how information flow (documentation, disclosures, and access) supports independent evaluations (including red-teaming and audits), which in turn feed into consequences (including liability and regulation) to create accountability. It concludes with recommendations for federal government action, some of which elaborate on themes in the AI EO, to encourage and possibly require accountability inputs…(More)”.

Graphic showing the AI Accountability Chain model

How Public Polling Has Changed in the 21st Century

Curated on Posted on by Stefaan Verhulst

Report by Pew Research: “The 2016 and 2020 presidential elections left many Americans wondering whether polling was broken and what, if anything, pollsters might do about it. A new Pew Research Center study finds that most national pollsters have changed their approach since 2016, and in some cases dramatically. Most (61%) of the pollsters who conducted and publicly released national surveys in both 2016 and 2022 used methods in 2022 that differed from what they used in 2016. The study also finds the use of multiple methods increasing. Last year 17% of national pollsters used at least three different methods to sample or interview people (sometimes in the same survey), up from 2% in 2016….(More)”.

Open Government Products (OGP)

Curated on Posted on by Stefaan Verhulst

About: “We are an experimental development team that builds technology for the public good. This includes everything from building better apps for citizens to automating the internal operations of public agencies. Our role is to accelerate the digital transformation of the Singapore Government by being a space where it can experiment with new tech practices, including new technologies, management techniques, corporate systems, and even cultural norms. Our end goal is that through our work, Singapore becomes a model of how governments can use technology to improve the public good…(More)”.

Citizen Jury on New Genomic Techniques

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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?

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”.