Paper by Christopher Small et al: “Polis is a platform that leverages machine intelligence to scale up deliberative processes. In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements. In particular, we demonstrate with pilot experiments using Anthropic’s Claude that LLMs can indeed augment human intelligence to help more efficiently run Polis conversations. In particular, we find that summarization capabilities enable categorically new methods with immense promise to empower the public in collective meaning-making exercises. And notably, LLM context limitations have a significant impact on insight and quality of these results.
However, these opportunities come with risks. We discuss some of these risks, as well as principles and techniques for characterizing and mitigating them, and the implications for other deliberative or political systems that may employ LLMs. Finally, we conclude with several open future research directions for augmenting tools like Polis with LLMs….(More)”.
Can AI help governments clean out bureaucratic “Sludge”?
Blog by Abhi Nemani: “Government services often entail a plethora of paperwork and processes that can be exasperating and time-consuming for citizens. Whether it’s applying for a passport, filing taxes, or registering a business, chances are one has encountered some form of sludge.
Sludge is a term coined by Cass Sunstein, in his straightforward book, Sludge, a legal scholar and former administrator of the White House Office of Information and Regulatory Affairs, to describe unnecessarily effortful processes, bureaucratic procedures, and other barriers to desirable outcomes in government services…
So how can sludge be reduced or eliminated in government services? Sunstein suggests that one way to achieve this is to conduct Sludge Audits, which are systematic evaluations of the costs and benefits of existing or proposed sludge. He also recommends that governments adopt ethical principles and guidelines for the design and use of public services. He argues that by reducing sludge, governments can enhance the quality of life and well-being of their citizens.
One example of sludge reduction in government is the simplification and automation of tax filing in some countries. According to a study by the World Bank, countries that have implemented electronic tax filing systems have reduced the time and cost of tax compliance for businesses and individuals. The study also found that electronic tax filing systems have improved tax administration efficiency, transparency, and revenue collection. Some countries, such as Estonia and Chile, have gone further by pre-filling tax returns with information from various sources, such as employers, banks, and other government agencies. This reduces the burden on taxpayers to provide or verify data, and increases the accuracy and completeness of tax returns.
Future Opportunities for AI in Cutting Sludge
AI technology is rapidly evolving, and its potential applications are manifold. Here are a few opportunities for further AI deployment:
- AI-assisted policy design: AI can analyze vast amounts of data to inform policy design, identifying areas of administrative burden and suggesting improvements.
- Smart contracts and blockchain: These technologies could automate complex procedures, such as contract execution or asset transfer, reducing the need for paperwork.
- Enhanced citizen engagement: AI could personalize government services, making them more accessible and less burdensome.
Key Takeaways:
- AI could play a significant role in policy design, contract execution, and citizen engagement.
- These technologies hold the potential to significantly reduce sludge…(More)”.
Three approaches to re-design digital public spaces
Article by Gianluca Sgueo: “The underlying tenet of so-called “human centred-design” is a public administration capable of delivering a satisfactory (even gratifying) digital experience to every user. Public services, however, are still marked by severe qualitative asymmetries, both nationally and supranationally. In this article we discuss the key shortcomings of digital public spaces, and we explore three approaches to re-design such spaces with the aim to widen the existing gaps separating the ideal from the actual rendering of human-centred digital government…(More)”.
Better Government Tech Is Possible
Article by Beth Noveck: “In the first four months of the Covid-19 pandemic, government leaders paid $100 million for management consultants at McKinsey to model the spread of the coronavirus and build online dashboards to project hospital capacity.
It’s unsurprising that leaders turned to McKinsey for help, given the notorious backwardness of government technology. Our everyday experience with online shopping and search only highlights the stark contrast between user-friendly interfaces and the frustrating inefficiencies of government websites—or worse yet, the ongoing need to visit a government office to submit forms in person. The 2016 animated movie Zootopia depicts literal sloths running the DMV, a scene that was guaranteed to get laughs given our low expectations of government responsiveness.
More seriously, these doubts are reflected in the plummeting levels of public trust in government. From early Healthcare.gov failures to the more recent implosions of state unemployment websites, policymaking without attention to the technology that puts the policy into practice has led to disastrous consequences.
The root of the problem is that the government, the largest employer in the US, does not keep its employees up-to-date on the latest tools and technologies. When I served in the Obama White House as the nation’s first deputy chief technology officer, I had to learn constitutional basics and watch annual training videos on sexual harassment and cybersecurity. But I was never required to take a course on how to use technology to serve citizens and solve problems. In fact, the last significant legislation about what public professionals need to know was the Government Employee Training Act, from 1958, well before the internet was invented.
In the United States, public sector awareness of how to use data or human-centered design is very low. Out of 400-plus public servants surveyed in 2020, less than 25 percent received training in these more tech-enabled ways of working, though 70 percent said they wanted such training…(More)”.
Why picking citizens at random could be the best way to govern the A.I. revolution
Article by Hélène Landemore, Andrew Sorota, and Audrey Tang: “Testifying before Congress last month about the risks of artificial intelligence, Sam Altman, the OpenAI CEO behind the massively popular large language model (LLM) ChatGPT, and Gary Marcus, a psychology professor at NYU famous for his positions against A.I. utopianism, both agreed on one point: They called for the creation of a government agency comparable to the FDA to regulate A.I. Marcus also suggested scientific experts should be given early access to new A.I. prototypes to be able to test them before they are released to the public.
Strikingly, however, neither of them mentioned the public, namely the billions of ordinary citizens around the world that the A.I. revolution, in all its uncertainty, is sure to affect. Don’t they also deserve to be included in decisions about the future of this technology?
We believe a global, democratic approach–not an exclusively technocratic one–is the only adequate answer to what is a global political and ethical challenge. Sam Altman himself stated in an earlier interview that in his “dream scenario,” a global deliberation involving all humans would be used to figure out how to govern A.I.
There are already proofs of concept for the various elements that a global, large-scale deliberative process would require in practice. By drawing on these diverse and complementary examples, we can turn this dream into a reality.
Deliberations based on random selection have grown in popularity on the local and national levels, with close to 600 cases documented by the OECD in the last 20 years. Their appeal lies in capturing a unique array of voices and lived experiences, thereby generating policy recommendations that better track the preferences of the larger population and are more likely to be accepted. Famous examples include the 2012 and 2016 Irish citizens’ assemblies on marriage equality and abortion, which led to successful referendums and constitutional change, as well as the 2019 and 2022 French citizens’ conventions on climate justice and end-of-life issues.
Taiwan has successfully experimented with mass consultations through digital platforms like Pol.is, which employs machine learning to identify consensus among vast numbers of participants. Digitally engaged participation has helped aggregate public opinion on hundreds of polarizing issues in Taiwan–such as regulating Uber–involving half of its 23.5 million people. Digital participation can also augment other smaller-scale forms of citizen deliberations, such as those taking place in person or based on random selection…(More)”.
Privacy-enhancing technologies (PETs)
Report by the Information Commissioner’s Office (UK): “This guidance discusses privacy-enhancing technologies (PETs) in detail. Read it if you have questions not answered in the Guide, or if you need a deeper understanding to help you apply PETs in practice.
The first part of the guidance is aimed at DPOs (data protection officers) and those with specific data protection responsibilities in larger organisations. It focuses on how PETs can help you achieve compliance with data protection law.
The second part is intended for a more technical audience, and for DPOs who want to understand more detail about the types of PETs that are currently available. It gives a brief introduction to eight types of PETs and explains their risks and benefits…(More)”.
Collective Intelligence to Co-Create the Cities of the Future: Proposal of an Evaluation Tool for Citizen Initiatives
Paper by Fanny E. Berigüete, Inma Rodriguez Cantalapiedra, Mariana Palumbo and Torsten Masseck: “Citizen initiatives (CIs), through their activities, have become a mechanism to promote empowerment, social inclusion, change of habits, and the transformation of neighbourhoods, influencing their sustainability, but how can this impact be measured? Currently, there are no tools that directly assess this impact, so our research seeks to describe and evaluate the contributions of CIs in a holistic and comprehensive way, respecting the versatility of their activities. This research proposes an evaluation system of 33 indicators distributed in 3 blocks: social cohesion, urban metabolism, and transformation potential, which can be applied through a questionnaire. This research applied different methods such as desk study, literature review, and case study analysis. The evaluation of case studies showed that the developed evaluation system well reflects the individual contribution of CIs to sensitive and important aspects of neighbourhoods, with a lesser or greater impact according to the activities they carry out and the holistic conception they have of sustainability. Further implementation and validation of the system in different contexts is needed, but it is a novel and interesting proposal that will favour decision making for the promotion of one or another type of initiative according to its benefits and the reality and needs of the neighbourhood…(More)”.
An algorithm intended to reduce poverty in Jordan disqualifies people in need
Article by Tate Ryan-Mosley: “An algorithm funded by the World Bank to determine which families should get financial assistance in Jordan likely excludes people who should qualify, according to an investigation published this morning by Human Rights Watch.
The algorithmic system, called Takaful, ranks families applying for aid from least poor to poorest using a secret calculus that assigns weights to 57 socioeconomic indicators. Applicants say that the calculus is not reflective of reality, however, and oversimplifies people’s economic situation, sometimes inaccurately or unfairly. Takaful has cost over $1 billion, and the World Bank is funding similar projects in eight other countries in the Middle East and Africa.
Human Rights Watch identified several fundamental problems with the algorithmic system that resulted in bias and inaccuracies. Applicants are asked how much water and electricity they consume, for example, as two of the indicators that feed into the ranking system. The report’s authors conclude that these are not necessarily reliable indicators of poverty. Some families interviewed believed the fact that they owned a car affected their ranking, even if the car was old and necessary for transportation to work.
The report reads, “This veneer of statistical objectivity masks a more complicated reality: the economic pressures that people endure and the ways they struggle to get by are frequently invisible to the algorithm.”..(More)”.
Brazil launches participatory national planning process
Article by Tarson Núñez and Luiza Jardim: “At a time when signs of a crisis in democracy are prevalent around the world, the Brazilian government is seeking to expand and deepen the active participation of citizens in its decisions. The new administration of Luiz Inácio Lula da Silva believes that more democracy is needed to rebuild citizens’ trust in political processes. And it just launched one of its main initiatives, the Participatory Pluriannual Plan (PPA Participativo). The PPA sets the goals and objectives for Brazil over the following four years, and Lula is determined to not only allow but facilitate public participation in its development.
On May 11, the federal government held the first state plenary for the Participatory PPA, an assembly open to all citizens, social movements and civil society organizations. Participants at the state plenaries are able to discuss proposals and deliberate on the government’s public policies. Over the next two months, government officials will travel to the capitals of the country’s 26 states as well as the federal district (the capital of Brazil) to listen to people present their priorities. If they prefer, people can also submit their suggestions through a digital platform (Decidim, accessible only to people in Brazil) or the Interconselhos Forum, which brings together various councils and civil society groups…(More)”.
Will Democracies Stand Up to Big Brother?
Article by Simon Johnson, Daron Acemoglu and Sylvia Barmack: “Rapid advances in AI and AI-enhanced surveillance tools have created an urgent need for international norms and coordination to set sensible standards. But with oppressive authoritarian regimes unlikely to cooperate, the world’s democracies should start preparing to play economic hardball…Fiction writers have long imagined scenarios in which every human action is monitored by some malign centralized authority. But now, despite their warnings, we find ourselves careening toward a dystopian future worthy of George Orwell’s 1984. The task of assessing how to protect our rights – as consumers, workers, and citizens – has never been more urgent.
One sensible proposal is to limit patents on surveillance technologies to discourage their development and overuse. All else being equal, this could tilt the development of AI-related technologies away from surveillance applications – at least in the United States and other advanced economies, where patent protections matter, and where venture capitalists will be reluctant to back companies lacking strong intellectual-property rights. But even if such sensible measures are adopted, the world will remain divided between countries with effective safeguards on surveillance and those without them. We therefore also need to consider the legitimate basis for trade between these emergent blocs.
AI capabilities have leapt forward over the past 18 months, and the pace of further development is unlikely to slow. The public release of ChatGPT in November 2022 was the generative-AI shot heard round the world. But just as important has been the equally rapid increase in governments and corporations’ surveillance capabilities. Since generative AI excels at pattern matching, it has made facial recognition remarkably accurate (though not without some major flaws). And the same general approach can be used to distinguish between “good” and problematic behavior, based simply on how people move or comport themselves.
Such surveillance technically leads to “higher productivity,” in the sense that it augments an authority’s ability to compel people to do what they are supposed to be doing. For a company, this means performing jobs at what management considers to be the highest productivity level. For a government, it means enforcing the law or otherwise ensuring compliance with those in power.
Unfortunately, a millennium of experience has established that increased productivity does not necessarily lead to improvements in shared prosperity. Today’s AI-powered surveillance allows overbearing managers and authoritarian political leaders to enforce their rules more effectively. But while productivity may increase, most people will not benefit…(More)”