Book by Naci Karkin and Volkan Göçoğlu: “The book explores and positions citizen centricity within conventional public administration and public policy analysis theories and approaches. It seeks to define an appropriate perspective while utilizing popular, independent, and standalone concepts from the literature that support citizen centricity. Additionally, it illustrates the implementation part with practical cases. It ultimately presents a novel and descriptive approach to provide insights into how citizen centricity can be applied in practice. This descriptive novel approach has three essential components: a base and two pillars. The foundation includes new-age public policy making approaches and complexity theory. The first column reflects the conceptual dimension, which comprises supporting concepts from the literature on citizen centricity. The second column represents the practical dimension, a structure supported by academic research that provides practical cases and inspiration for future applications. The descriptive novel approach accepts citizen centricity as a fundamental approach in public policy making and aims to create a new awareness in the academic community on the subject. Additionally, the book provides refreshed conceptual and theoretical backgrounds, along with tangible participatory models and frameworks, benefiting academics, professionals, and graduate students…(More)”.
Why Generative AI Isn’t Transforming Government (Yet) — and What We Can Do About It
Article by Tiago C. Peixoto: “A few weeks ago, I reached out to a handful of seasoned digital services practitioners, NGOs, and philanthropies with a simple question: Where are the compelling generative AI (GenAI) use cases in public-sector workflows? I wasn’t looking for better search or smarter chatbots. I wanted examples of automation of real public workflows – something genuinely interesting and working. The responses, though numerous, were underwhelming.
That question has gained importance amid a growing number of reports forecasting AI’s transformative impact on government. The Alan Turing Institute, for instance, published a rigorous study estimating the potential of AI to help automate over 140 million government transactions in the UK. The Tony Blair Institute also weighed in, suggesting that a substantive portion of public-sector work could be automated. While the report helped bring welcome attention to the issue, its use of GPT-4 to assess task automatability has sparked a healthy discussion about how best to evaluate feasibility. Like other studies in this area, both reports highlight potential – but stop short of demonstrating real service automation.
Without testing technologies in real service environments – where workflows, incentives, and institutional constraints shape outcomes – and grounding each pilot in clear efficiency or well-being metrics, estimates risk becoming abstractions that underestimate feasibility.
This pattern aligns with what Arvind Narayanan and Sayash Kapoor argue in “AI as Normal Technology:” the impact of AI is realized only when methods translate into applications and diffuse through real-world systems. My own review, admittedly non-representative, confirms their call for more empirical work on the innovation-diffusion lag.
In the public sector, the gap between capability and impact is not only wide but also structural…(More)”
When data disappear: public health pays as US policy strays
Paper by Thomas McAndrew, Andrew A Lover, Garrik Hoyt, and Maimuna S Majumder: “Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources; and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good…(More)”.
Public AI White Paper – A Public Alternative to Private AI Dominance
White paper by the Bertelsmann Stiftung and Open Future: “Today, the most advanced AI systems are developed and controlled by a small number of private companies. These companies hold power not only over the models themselves but also over key resources such as computing infrastructure. This concentration of power poses not only economic risks but also significant democratic challenges.
The Public AI White Paper presents an alternative vision, outlining how open and public-interest approaches to AI can be developed and institutionalized. It advocates for a rebalancing of power within the AI ecosystem – with the goal of enabling societies to shape AI actively, rather than merely consume it…(More)”.
What Happens When AI-Generated Lies Are More Compelling than the Truth?
Essay by Nicholas Carr: “…In George Orwell’s 1984, the functionaries in Big Brother’s Ministry of Truth spend their days rewriting historical records, discarding inconvenient old facts and making up new ones. When the truth gets hazy, tyrants get to define what’s true. The irony here is sharp. Artificial intelligence, perhaps humanity’s greatest monument to logical thinking, may trigger a revolution in perception that overthrows the shared values of reason and rationality we inherited from the Enlightenment.
In 1957, a Russian scientist-turned-folklorist named Yuri Mirolyubov published a translation of an ancient manuscript—a thousand years old, he estimated—in a Russian-language newspaper in San Francisco. Mirolyubov’s Book of Veles told stirring stories of the god Veles, a prominent deity in pre-Christian Slavic mythology. A shapeshifter, magician, and trickster, Veles would visit the mortal world in the form of a bear, sowing mischief wherever he went.
Mirolyubov claimed that the manuscript, written on thin wooden boards bound with leather straps, had been discovered by a Russian soldier in a bombed-out Ukrainian castle in 1919. The soldier had photographed the boards and given the pictures to Mirolyubov, who translated the work into modern Russian. Mirolyubov illustrated his published translation with one of the photographs, though the original boards, he said, had disappeared mysteriously during the Second World War. Though historians and linguists soon dismissed the folklorist’s Book of Veles as a hoax, its renown spread. Today, it’s revered as a holy text by certain neo-pagan and Slavic nationalist cults.
Mythmaking, more than truth seeking, is what seems likely to define the future of media and of the public square.
Myths are works of art. They provide a way of understanding the world that appeals not to reason but to emotion, not to the conscious mind but to the subconscious one. What is most pleasing to our sensibilities—what is most beautiful to us—is what feels most genuine, most worthy of belief. History and psychology both suggest that, in politics as in art, generative AI will succeed in fulfilling the highest aspiration of its creators: to make the virtual feel more authentic than the real…(More)”
Government ‘With’ The People
Article by Nathan Gardels: “The rigid polarization that has gripped our societies and eroded trust in each other and in governing institutions feeds the appeal of authoritarian strongmen. Poised as tribunes of the people, they promise to lay down the law (rather than be constrained by it) and put the house in order not by bridging divides, but by targeting scapegoats and persecuting political adversaries who don’t conform to their ideological and cultural worldview.
The alternative to this course of illiberal democracy is the exact opposite: engaging citizens directly in governance through non-partisan platforms that encourage and enable deliberation, negotiation and compromise, to reach consensus across divides. Even as politics is tilting the other way at the national level, this approach of participation without populism is gaining traction from the bottom up.
The embryonic forms of this next step in democratic innovation, such as citizens’ assemblies or virtual platforms for bringing the public together and listening at scale, have so far been mostly advisory to the powers-that-be, with no guarantee that citizen input will have a binding impact on legislation or policy formation. That is beginning to change….
Claudia Chwalisz, who heads DemocracyNext, has spelled out the key elements of this innovative process that make it a model for others elsewhere:
- “Implementation should be considered from the start, not as an afterthought. The format of the final recommendations, the process for final approval, and the time needed to ensure this part of the process does not get neglected need to be considered in the early design stages of the assembly.
- Dedicated time and resources for transforming recommendations into legislation are also crucial for successful implementation. Bringing citizens, politicians, and civil servants together in the final stages can help bridge the gap between recommendations and action. While it has been more typical for citizens’ assemblies to draft recommendations that they then hand onward to elected officials and civil servants, who review them and then respond to the citizens’ assembly, the Parisian model demonstrates another way.
- Collaborative workshops where consensus amongst the triad of actors is needed adds more time to the process, but ensures that there is a high level of consensus for the final output, and reduces the time that would have been needed for officials to review and respond to the citizens’ assembly’s recommendations.
- Formal institutional integration of citizens’ assemblies through legal measures can help ensure their recommendations are taken seriously and ensures the assembly’s continuity regardless of shifts in government. The citizens’ assembly has become a part of Paris’s democratic architecture, as have other permanent citizens’ assemblies elsewhere. While one-off assemblies typically depend on political will at a moment in time and risk becoming politicized — i.e. in being associated with the party that initially launched the first one — an institutionalized citizens’ assembly anchored in policy and political decision-making helps to set the foundation for a new institution that can endure.
- It is also important that there is regular engagement with all political parties and stakeholders throughout the process. This helps build cross-partisan support for final recommendations, as well as more sustainable support for the enduring nature of the permanent citizens assembly.”…(More)”.
Accounting for State Capacity
Essay by Kevin Hawickhorst: “The debates over the Department of Government Efficiency have revealed, if nothing else, that the federal budget is obscure even to the political combatants ostensibly responsible for developing and overseeing it. In the executive branch, Elon Musk highlights that billions of dollars of payments are processed by the Treasury without even a memo line. Meanwhile, in Congress, Republican politicians highlight the incompleteness of the bureaucracy’s spending records, while Democrats bemoan the Trump administration’s dissimulation in ceasing to share budgetary guidance documents. The camp followers of these obscure programs are thousands of federal contractors, pursuing vague goals with indefinite timelines. As soon as the ink on a bill is dry, it seems, Congress loses sight of its initiatives until their eventual success or their all-too-frequent failure.
Contrast this with the 1930s, when the Roosevelt administration provided Congress with hundreds of pages of spending reports every ten days, outlining how tax dollars were being put to use in minute detail. The speed and thoroughness with which these reports were produced is hard to fathom, and yet the administration was actually holding its best information back. FDR’s Treasury had itemized information on hundreds of thousands of projects, down to the individual checks that were written. Incredibly, politicians had better dashboards in the era of punch cards than we have in the era of AI. The decline in government competence runs deeper than our inability to match the speed and economy of New Deal construction: even their accounting was better. What happened?
Political scientists discuss the decline in government competence in terms of “state capacity,” which describes a government’s ability to achieve the goals it pursues. Most political scientists agree that the United States not only suffers from degraded state capacity in absolute terms, but has less state capacity today than in the early twentieth century. A popular theory for this decline blames the excessive proceduralism of the U.S. government: the “cascade of rigidity” or the “procedure fetish.”
But reformers need more than complaints. To rebuild state capacity, reformers need an affirmative vision of what good procedure should look like and, in order to enact it, knowledge of how government procedure is changed. The history of government budgeting and accounting reform illustrates both. There were three major eras of reform to federal accounting in the twentieth century: New Deal reforms of the 1930s, conservative reforms of the 1940s and 1950s, and liberal reforms of the 1960s. This history tells the story of how accounting reforms first built up American state capacity and how later reforms contributed to its gradual decline. These reforms thus offer lessons on rebuilding state capacity today…(More)”.
How to Break Down Silos and Collaborate Across Government
Blog by Jessica MacLeod: “…To help public sector leaders navigate these cultural barriers, I use a simple but powerful framework: Clarity, Care, and Challenge. It’s built from research, experience, and what I’ve seen actually shift how teams work. You can read more about the framework in my previous article on high-performing teams. Here’s how this framework relates to breaking down silos:
- Clarity → How We Work:
Clear priorities, aligned expectations, and a shared understanding of how individual work connects to the bigger picture. - Care → How We Relate:
Trust, psychological safety, and strong collaboration. - Challenge → How We Achieve:
Stretch goals, high standards, and a culture that encourages innovation and growth.
Silos thrive in ambiguity. If no one can see the work, understand the language, or map who owns what, collaboration dies on arrival.
When I work with public sector teams, one of the first things I look for is how visible the work is. Can people across departments explain where things stand on a project today? Or what the context is behind a project? Do they know who’s accountable? Can they locate the latest draft of the work without digging through three email chains?
Often, the answer is no, and it’s not because people aren’t trying. It’s because our systems are optimized for siloed visibility, not shared clarity.
Here’s what that looks like in practice:
- A particular acronym means one thing to IT, another to leadership, and something entirely different to community stakeholders.
- “Launch” for one team means public announcement. For another, it means testing a feature with a pilot group.
- Documents live in private folders, on individual desktops, or in tools that don’t talk to each other…(More)”.
Reimagining Data Governance for AI: Operationalizing Social Licensing for Data Reuse
Report by Stefaan Verhulst, Adam Zable, Andrew J. Zahuranec, and Peter Addo: “…introduces a practical, community-centered framework for governing data reuse in the development and deployment of artificial intelligence systems in low- and middle-income countries (LMICs). As AI increasingly relies on data from LMICs, affected communities are often excluded from decision-making and see little benefit from how their data is used. This report,…reframes data governance through social licensing—a participatory model that empowers communities to collectively define, document, and enforce conditions for how their data is reused. It offers a step-by-step methodology and actionable tools, including a Social Licensing Questionnaire and adaptable contract clauses, alongisde real-world scenarios and recommendations for enforcement, policy integration, and future research. This report recasts data governance as a collective, continuous process – shifting the focus from individual consent to community decision-making…(More)”.
“R&D” Means Something Different on Capitol Hill
Article by Sheril Kirshenbaum: “My first morning as a scientist-turned-Senate-staffer began with a misunderstanding that would become a metaphor for my impending immersion into the complex world of policymaking. When my new colleagues mentioned “R&D,” I naively assumed they were discussing critical topics related to research and development. After 10 or so confused minutes, I realized they were referring to Republicans and Democrats—my first lesson in the distinctive language and unique dynamics of congressional work. The “R&D” at the center of their world was vastly different than that of mine.In the 20 years since, I’ve moved between academic science positions and working on science policy in the Senate, under both Republican and Democratic majorities. My goal during these two decades has remained the same—to promote evidence-based policymaking that advances science and serves the public, regardless of the political landscape. But the transition from scientist to staffer has transformed my understanding of why so many efforts by scientists to influence policy falter. Despite generations of scholarly research to understand how information informs political decisions, scientists and other academics consistently overlook a crucial part of the process: the role of congressional staffers.
The staff hierarchy shapes how scientific information flows to elected officials. Chiefs of staff manage office operations and serve as the member’s closest advisors. Legislative directors oversee all policy matters, while legislative assistants (LAs) handle specific issue portfolios. One or two LAs may be designated as the office “science people,” although they often lack formal scientific training. Committee staffers provide deeper expertise and institutional knowledge on topics within their jurisdiction. In this ecosystem, few dedicated science positions exist, and science-related topics are distributed among staff already juggling multiple responsibilities…(More)”