Paper by S. Lee et all: “Large language models (LLMs) have demonstrated their potential in social science research by emulating human perceptions and behaviors, a concept referred to as algorithmic fidelity. This study assesses the algorithmic fidelity and bias of LLMs by utilizing two nationally representative climate change surveys. The LLMs were conditioned on demographics and/or psychological covariates to simulate survey responses. The findings indicate that LLMs can effectively capture presidential voting behaviors but encounter challenges in accurately representing global warming perspectives when relevant covariates are not included. GPT-4 exhibits improved performance when conditioned on both demographics and covariates. However, disparities emerge in LLM estimations of the views of certain groups, with LLMs tending to underestimate worry about global warming among Black Americans. While highlighting the potential of LLMs to aid social science research, these results underscore the importance of meticulous conditioning, model selection, survey question format, and bias assessment when employing LLMs for survey simulation. Further investigation into prompt engineering and algorithm auditing is essential to harness the power of LLMs while addressing their inherent limitations…(More)”.
Unintended Consequences of Data-driven public participation: How Low-Traffic Neighborhood planning became polarized
Paper by Alison Powell: “This paper examines how data-driven consultation contributes to dynamics of political polarization, using the case of ‘Low-Traffic Neighborhoods’ in London, UK. It explores how data-driven consultation can facilitate participation, including ‘agonistic data practices” (Crooks and Currie, 2022) that challenge the dominant interpretations of digital data. The paper adds empirical detail to previous studies of agonistic data practices, concluding that agonistic data practices require certain normative conditions to be met, otherwise dissenting data practices can contribute to dynamics of polarization. The results of this paper draw on empirical insights from the political context of the UK to explain how ostensibly democratic processes including data-driven consultation establish some kinds of knowledge as more legitimate than others. Apparently ‘objective’ knowledge, or calculable data, is attributed greater legitimacy than strong feelings or affective narratives. This can displace affective responses to policy decisions into insular social media spaces where polarizing dynamics are at play. Affective polarization, where political difference is solidified through appeals to feeling, creates political distance and the dehumanization of ‘others’. This can help to amplify conspiracy theories that pose risks to democracy and to the overall legitimacy of media environments. These tendencies are exacerbated when processes of consultation prescribe narrow or specific contributions, valorize quantifiable or objective data and create limited room for dissent…(More)”
The Tragedy of AI Governance
Paper by Simon Chesterman: “Despite hundreds of guides, frameworks, and principles intended to make AI “ethical” or “responsible”, ever more powerful applications continue to be released ever more quickly. Safety and security teams are being downsized or sidelined to bring AI products to market. And a significant portion of AI developers apparently believe there is a real risk that their work poses an existential threat to humanity.
This contradiction between statements and action can be attributed to three factors that undermine the prospects for meaningful governance of AI. The first is the shift of power from public to private hands, not only in deployment of AI products but in fundamental research. The second is the wariness of most states about regulating the sector too aggressively, for fear that it might drive innovation elsewhere. The third is the dysfunction of global processes to manage collective action problems, epitomized by the climate crisis and now frustrating efforts to govern a technology that does not respect borders. The tragedy of AI governance is that those with the greatest leverage to regulate AI have the least interest in doing so, while those with the greatest interest have the least leverage.
Resolving these challenges either requires rethinking the incentive structures — or waiting for a crisis that brings the need for regulation and coordination into sharper focus…(More)”
Enhancing the European Administrative Space (ComPAct)
European Commission: “Efficient national public administrations are critical to transform EU and national policies into reality, to implement reforms to the benefit of people and business alike, and to channel investments towards the achievement of the green and digital transition, and greater competitiveness. At the same time, national public administrations are also under an increasing pressure to deal with polycrisis and with many competing priorities.
For the first time, with the ComPAct, the Commission is proposing a strategic set of actions not only to support the public administrations in the Member States to become more resilient, innovative and skilled, but also to strengthen the administrative cooperation between them, thereby allowing to close existing gaps in policies and services at European level.
With the ComPAct, the Commission aims to enhance the European Administrative Space by promoting a common set of overarching principles underpinning the quality of public administration and reinforcing its support for the administrative modernisation of the Member States. The ComPAct will help Member States address the EU Skills Agenda and the actions under the European Year of Skills, deliver on the targets of the Digital Decade to have 100% of key public services accessible online by 2030, and shape the conditions for the economies and societies to deliver on the ambitious 2030 climate and energy targets. The ComPAct will also help EU enlargement countries on their path to building better public administrations…(More)”.
Learning Like a State: Statecraft in the Digital Age
Paper by Marion Fourcade and Jeff Gordon: “What does it mean to sense, see, and act like a state in the digital age? We examine the changing phenomenology, governance, and capacity of the state in the era of big data and machine learning. Our argument is threefold. First, what we call the dataist state may be less accountable than its predecessor, despite its promise of enhanced transparency and accessibility. Second, a rapid expansion of the data collection mandate is fueling a transformation in political rationality, in which data affordances increasingly drive policy strategies. Third, the turn to dataist statecraft facilitates a corporate reconstruction of the state. On the one hand, digital firms attempt to access and capitalize on data “minted” by the state. On the other hand, firms compete with the state in an effort to reinvent traditional public functions. Finally, we explore what it would mean for this dataist state to “see like a citizen” instead…(More)”.
Shifting policy systems – a framework for what to do and how to do it
Blog by UK Policy Lab: “Systems change is hard work, and it takes time. The reality is that no single system map or tool is enough to get you from point A to point B, from system now to system next. Over the last year, we have explored the latest in systems change theory and applied it to policymaking. In this four part blog series, we share our reflections on the wealth of knowledge we’ve gained working on intractable issues surrounding how support is delivered for people experiencing multiple disadvantage. Along the way, we realised that we need to make new tools to support policy teams to do this deep work in the future, and to see afresh the limitations of existing mental models for change and transformation.
Policy Lab has previously written about systems mapping as a useful process for understanding the interconnected nature of factors and actors that make up policy ecosystems. Here, we share our latest experimentation on how we can generate practical ideas for long-lasting and systemic change.
This blog includes:
- An overview of what we did on our latest project – including the policy context, systems change frameworks we experimented with, and the bespoke project framework we created;
- Our reflections on how we carried out the project;
- A matrix which provides a practical guide for you to use this approach in your own work…(More)”.
Artificial intelligence in government: Concepts, standards, and a unified framework
Paper by Vincent J. Straub, Deborah Morgan, Jonathan Bright, Helen Margetts: “Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI…(More)”.
Why Deliberation and Voting Belong Together
Paper by Simone Chambers & Mark E. Warren: “The field of deliberative democracy now generally recognizes the co-dependence of deliberation and voting. The field tends to emphasize what deliberation accomplishes for vote-based decisions. In this paper, we reverse this now common view to ask: In what ways does voting benefit deliberation? We discuss seven ways voting can complement and sometimes enhance deliberation. First, voting furnishes deliberation with a feasible and fair closure mechanism. Second, the power to vote implies equal recognition and status, both morally and strategically, which is a condition of democratic deliberation. Third, voting politicizes deliberation by injecting the strategic features of politics into deliberation—effectively internalizing conflict into deliberative processes, without which they can become detached from their political environments. Fourth, anticipation of voting may induce authenticity by revealing preferences, as what one says will count. Fifth, voting preserves expressions of dissent, helping to push back against socially induced pressures for consensus. Sixth, voting defines the issues, such that deliberation is focused, and thus more likely to be effective. And, seventh, within contexts where votes are public—as in representative contexts, voting can induce accountability, particularly for one’s claims. We then use these points to discuss four general types of institutions—general elections, legislatures, minipublics, and minipublics embedded in referendum processes—that combine talking and voting, with the aim of identifying designs that do a better or worse job of capitalizing upon the strengths of each…(More)”.
Setting Democratic Ground Rules for AI: Civil Society Strategies
Report by Beth Kerley: “…analyzes priorities, challenges, and promising civil society strategies for advancing democratic approaches to governing artificial intelligence (AI). The report is based on conversations from a private Forum workshop in Buenos Aires, Argentina that brought together Latin American and global researchers and civil society practitioners.
With recent leaps in the development of AI, we are experiencing a seismic shift in the balance of power between people and governments, posing new challenges to democratic principles such as privacy, transparency, and non-discrimination. We know that AI will shape the political world we inhabit–but how can we ensure that democratic norms and institutions shape the trajectory of AI?
Drawing on global civil society perspectives, this report surveys what stakeholders need to know about AI systems and the human relationships behind them. It delves into the obstacles– from misleading narratives to government opacity to gaps in technical expertise–that hinder democratic engagement on AI governance, and explores how new thinking, new institutions, and new collaborations can better equip societies to set democratic ground rules for AI technologies…(More)”.
Europe wants to get better at planning for the worst
Article by Sarah Anne Aarup: “The European Union is beset by doom and gloom — from wars on its doorstep to inflation and the climate crisis — not to mention political instability in the U.S. and rivalry with China.
All too often, the EU has been overtaken by events, which makes the task of getting better at planning for the worst all the more pressing.
As European leaders fought political fires at their informal summit last week in Granada, unaware that Palestinian militants would launch their devastating raid on Israel a day later, they quietly started a debate on strategic foresight.
At this stage still very much a thought experiment, the concept of “open strategic autonomy” is being championed by host Spain, the current president of the Council of the EU. The idea reflects a shift in priorities to navigate an increasingly uncertain world, and a departure from the green and digital transitions that have dominated the agenda in recent years.
To the uninitiated, the concept of open strategic autonomy sounds like an oxymoron — that’s because it is.
After the hyper globalized early 2000s, trust in liberalism started to erode. Then the Trump-era trade wars, COVID-19 pandemic and Russia’s invasion of Ukraine exposed Europe’s economic reliance on powerful nations that are either latent — or overt — strategic rivals.
“The United States and China are becoming more self-reliant, and some voices were saying that this is what we have to do,” an official with the Spanish presidency told POLITICO. “But that’s not a good idea for Europe.”
Instead, open strategic autonomy is about shielding the EU just enough to protect its economic security while remaining an international player. In other words, it means “cooperating multilaterally wherever we can, acting autonomously wherever we must.”
It’s a grudging acceptance that great power politics now dominate economics…
The open strategic autonomy push is about countering an inward turn that was all about cutting dependencies, such as the EU’s reliance on Russian energy, after President Vladimir Putin ordered the invasion of Ukraine.
“[We’re] missing a more balanced and forward-looking strategy” following the Versailles Declaration, the Spanish official said, referring to a first response by EU leaders to the Russian attack of February 24, 2022.
Spain delivered its contribution to the debate in the form of a thick paper drafted by its foresight office, in coordination with over 80 ministries across the EU…(More)”.