Article by Kate Woodford: “Today, the Cambridge Dictionary announces its Word of the Year for 2023: hallucinate. You might already be familiar with this word, which we use to talk about seeing, hearing, or feeling things that don’t really exist. But did you know that it has a new meaning when it’s used in the context of artificial intelligence?
To celebrate the Word of the Year, this post is dedicated to AI terms that have recently come into the English language. AI, as you probably know, is short for artificial intelligence – the use of computer systems with qualities similar to the human brain that allow them to ‘learn’ and ‘think’. It’s a subject that arouses a great deal of interest and excitement and, it must be said, a degree of anxiety. Let’s have a look at some of these new words and phrases and see what they mean and how we’re using them to talk about AI…
Blog by Beth Noveck: “A recent issue of the Daily Beast featured an article about vTaiwan, Taiwan’s flagship crowdlaw project to engage the public in the legislative process, reporting what I long suspected and feared: early success has not translated into lasting impact or institutionalization of public participation in policymaking.
“The platform hasn’t been used for any major decisions since 2018” said vTaiwan co-creator and former Taiwanese legislator Jason Hsu. He went on to add that: “since the government is not mandated to adopt recommendations coming from vTaiwan, ‘legislators don’t take it seriously.’”
After vTaiwan enabled over two hundred thousand people to participate in crafting 26 pieces of national legislation, advocates for tech and democracy hailed this four-stage online and offline deliberative process as the poster child of tech-enabled public engagement. We celebrated vTaiwan as evidence of the powerful potential for meaningful public participation in governance.
vTaiwan began with a proposal stage, with offline and online discussion of problems using a series of different tools for deliberation and frequent polling.This collaborative problem-definition process, which lasted from a few weeks to a year, helped a large number of people to agree on and define which problems should be tackled.
While disappointing, vTaiwan is not unique in failing to deliver on the promise of tech-enabled participation. As my GovLab colleagues and I reported last year, Madrid’s online engagement platform Decide Madrid attracted almost half a million sign-ups. But of the 28,000 legislative proposals submitted by residents since 2015, only one became policy. Sign-ups have declined dramatically.
Online public engagements fizzle for a variety of reasons…(More)”.
Article by Katie Reytar, Jessica Webb and Peter Veit: “Indigenous Peoples and local communities hold some of the most pristine and resource-rich lands in the world — areas highly coveted by mining and logging companies and other profiteers. Land grabs and other threats are especially severe in places where the government does not recognize communities’ land rights, or where anti-deforestation and other laws are weak or poorly enforced. It’s the reason many Indigenous Peoples and local communities often take land monitoring into their own hands — and some are now using digital tools to do it.
Freely available satellite imagery and data from sites like Global Forest Watch and LandMark provide near-real-time information that tracks deforestation and land degradation. Indigenous and local communities are increasingly using tools like this to gather evidence that deforestation and degradation are happening on their lands, build their case against illegal activities and take legal action to prevent it from continuing.
Three examples from Suriname, Indonesia and Peru illustrate a growing trend in fighting land rights violations with data…(More)”.
Blog by Ken Roy:” I have been looking at some of the narratives used by bodies producing Official Statistics – specifically those in a sample of recent strategies and business plans from different National Statistical Offices. Inevitably these documents focus on planned programmes of work – the key statistical outputs, the technical and methodological investments etc – and occasionally on interesting things like budgets.
When these documents touch on the rationale for (or purpose of) Official Statistics, one approach is to present Official Statistics as a ‘right’ of citizens or as essential national infrastructure. For example Statistics Finland frame Official Statistics as “our shared national capital”. A further common approach is to reference the broad purpose of improved decision making – Statistics Canada has the aim that “Canadians have the key information they need to make evidence-based decisions.”
Looking beyond these high-level statements, I was keen to find any further, more specific, expressions of real-world impacts. The following sets out some initial groups of ideas and some representative quotes.
In terms of direct impacts for citizens, some strategies have a headline aim that citizens are knowledgeable about their world – Statistics Iceland aims to enable an “informed society”. A slightly different ambition is that different groups of citizens are represented or ‘seen’ by Official Statistics. The UK Statistics Authority aims to “reflect the experiences of everyone in our society so that everyone counts, and is counted, and no one is forgotten”. There are also references to the role of Official Statistics (and data more broadly) in empowering citizens – most commonly through giving them the means to hold government to account. One of the headline aims of New Zealand’s Data Investment Plan is that “government is held to account through a robust and transparent data system”.
Also relevant to citizens is the ambition for Official Statistics to enable healthy, informed public debate – one aim of the Australian Bureau of Statistics is that their work will “provide reliable information on a range of matters critical to public debate”.
Some narratives hint at the contribution of Official Statistics systems to national economic success. Stats NZ notes that “the integrity of official data can have wide-ranging implications … such as the interest charged on government borrowing.” The Papua New Guinea statistics office references a focus on “private sector investors who want to use data and statistics to aid investment decisions”.
Finally, we come to governments. Official Statistics are regularly presented as essential to a better, more effective, government process – through establishing understanding of the circumstances and needs of citizens, businesses and places and hence supporting the development and implementation of better policies, programmes and services in response. The National Bureau of Statistics (Tanzania) sees Official Statistics as enabling “evidence-based formulation, planning, monitoring and evaluation which are key in the realization of development aspirations.” A related theme is the contribution to good governance – the United Nations presents Official Statistics as “an essential element of the accountability of governments and public bodies to the public in a democratic society…(More)”.
Blog by Sara Marcucci and Stefaan Verhulst: “In the ever-evolving landscape of anticipatory methods for migration policy, innovation is a dynamic force propelling the field forward. This seems to be happening in two main ways: first, as we mentioned in our previous blog, one of the significant shifts lies in the blurring of boundaries between quantitative forecasting and qualitative foresight, as emerging mixed-method approaches challenge traditional paradigms. This transformation opens up new pathways for understanding complex phenomena, particularly in the context of human migration flows.
Second, the innovation happening today is not necessarily rooted in the development of entirely new methodologies, but rather in how existing methods are adapted and enhanced. Indeed, innovation seems to extend to the utilization of diverse tools and data sources that bolster the effectiveness of existing methods, offering a more comprehensive and timely perspective on migration trends.
In the context of this blog series, methods refer to the various approaches and techniques used to anticipate and analyze migration trends, challenges, and opportunities. These methods are employed to make informed decisions and develop policies related to human migration. They can include a wide range of strategies to gather and interpret data and insights in the field of migration policy.
Tools, on the other hand, refer to the specific instruments or technologies used to support and enhance the effectiveness of these methods. They encompass a diverse set of resources and technologies that facilitate data collection, analysis, and decision-making in the context of migration policy. These tools can include both quantitative and qualitative data collection and analysis tools, as well as innovative data sources, software, and techniques that help enhance anticipatory methods.
This blog aims to deep dive into the main anticipatory methods adopted in the field of migration, as well as some of the tools and data sources employed to enhance and experiment with them. First, the blog will provide a list of methods considered; second, it will illustrate the main innovative tools employed, and finally it will provide a set of new, non-traditional data sources that are increasingly being used to feed anticipatory methods…(More)”.
Article by Deloitte: “…But our research shows that this focus may be overlooking some of the most important tools already on the books. Of the 1,600+ policies we analyzed, only 11% were focused on regulating AI-adjacent issues like data privacy, cybersecurity, intellectual property, and so on (Figure 5). Even when limiting the search to only regulations, 60% were focused directly on AI and only 40% on AI-adjacent issues (Figure 5). For example, several countries have data protection agencies with regulatory powers to help protect citizens’ data privacy. But while these agencies may not have AI or machine learning named specifically in their charters, the importance of data in training and using AI models makes them an important AI-adjacent tool.
This can be problematic because directly regulating a fast-moving technology like AI can be difficult. Take the hypothetical example of removing bias from home loan decisions. Regulators could accomplish this goal by mandating that AI should have certain types of training data to ensure that the models are representative and will not produce biased results, but such an approach can become outdated when new methods of training AI models emerge. Given the diversity of different types of AI models already in use, from recurrent neural networks to generative pretrained transformers to generative adversarial networks and more, finding a single set of rules that can deliver what the public desires both now, and in the future, may be a challenge…(More)”.
Blog by Citylab: “Generative artificial intelligence promises to transform the way we work, and city leaders are taking note. According to a recent survey by Bloomberg Philanthropies in partnership with the Centre for Public Impact, the vast majority of mayors (96 percent) are interested in how they can use generative AI tools like ChatGPT—which rely on machine learning to identify patterns in data and create, or generate, new content after being fed prompts—to improve local government. Of those cities surveyed, 69 percent report that they are already exploring or testing the technology. Specifically, they’re interested in how it can help them more quickly and successfully address emerging challenges with traffic and transportation, infrastructure, public safety, climate, education, and more.
Yet even as a majority of city leaders surveyed are exploring generative AI’s potential, only a small fraction of them (2 percent) are actively deploying the technology. They indicated there are a number of issues getting in the way of broader implementation, including a lack of technical expertise, budgetary constraints, and ethical considerations like security, privacy, and transparency…(More)”.
Article by Andrew J. Zahuranec, Stefaan Verhulst, and Hannah Chafetz: “The process of forming a data-sharing agreement is not easy. The process involves figuring out incentives, evaluating the degree to which others are willing and able to collaborate, and defining the specific conduct that is and is not allowed. Even under the best of circumstances, these steps can be costly and time-consuming.
A Guide to Principled Data Sharing Agreement Negotiation by Design: A document outlining the different principles that a data practitioner might seek to uphold while negotiating an agreement;
The Contractual Wheel of Data Collaboration 2.0: A listing of the different kinds of data sharing agreement provisions that a data practitioner might include in an agreement;
A Readiness Matrix for Data Sharing Agreements: A form to evaluate the degree to which a partner can participate in a data-sharing agreement.
The resources are a result of a series of Open Data Action Labs, an initiative from the Open Data Policy Lab to define new strategies and tools that can help organizations resolve policy challenges they face. The Action Labs are built around a series of workshops (called “studios”) which given experts and stakeholders an opportunity to define the problems facing them and then ideate possible solutions in a collaborative setting. In February and March 2023, the Open Data Policy Lab and Trust Relay co-hosted conversations with experts in law, data, and smart cities on the challenge of forming a data sharing agreement. Find all the resources here.”
Article by Zoya Teirstein: “Earth just experienced one of its hottest, and most damaging, periods on record. Heat waves in the United States, Europe, and China; catastrophic flooding in India, Brazil, Hong Kong, and Libya; and outbreaks of malaria, dengue, and other mosquito-borne illnesses across southern Asia claimed tens of thousands of lives. The vast majority of these deaths could have been averted with the right safeguards in place.
The World Meteorological Organization, or WMO, published a report last week that shows just 11 percent of countries have the full arsenal of tools required to save lives as the impacts of climate change — including deadly weather events, infectious diseases, and respiratory illnesses like asthma — become more extreme. The United Nations climate agency predicts that significant natural disasters will hit the planet 560 times per year by the end of this decade. What’s more, countries that lack early warning systems, such as extreme heat alerts, will see eight times more climate-related deaths than countries that are better prepared. By midcentury, some 50 percent of these deaths will take place in Africa, a continent that is responsible for around 4 percent of the world’s greenhouse gas emissions each year…(More)”.
Article by Stefaan G. Verhulst: “With the UK Summit in full swing, 2023 will likely be seen as a pivotal year for AI governance, with governments promoting a global governance model: AI Globalism. For it to be relevant, flexible, and effective, any global approach will need to be informed by and complemented with local experimentation and leadership, ensuring local responsiveness: AI Localism.
Even as consumers and businesses extend their use of AI (generative AI in particular), governments are also taking notice. Determined not to be caught on the back foot, as they were with social media, regulators and policymakers around the world are exploring frameworks and institutional structures that could help maximize the benefits while minimizing the potential harms of AI. This week,the UK is hosting a high-profile AI Safety Summit, attended by political and business leaders from around the world, including Kamala Harris and Elon Musk. Similarly, US President Biden recently signed an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, which he hailed as a “landmark executive order” to ensure “safety, security, trust, openness, and American leadership.”
Generated with DALL-E
Amid the various policy and regulatory proposals swirling around, there has been a notable emphasis on what we might call AI globalism. The UK summit has explicitly endorsed a global approach to AI safety, with coordination between the US, EU, and China core to its vision of more responsible and safe AI. This global perspective follows similar recent calls for “an AI equivalent of the IPCC” or the International Atomic Energy Agency (IAEA). Notably, such calls are emerging both from the private sector and from civil society leaders.
In many ways, a global approach makes sense. Like most technology, AI is transnational in scope, and its governance will require cross-jurisdictional coordination and harmonization. At the same time, we believe that AI globalism should be accompanied by a recognition that some of the most innovative AI initiatives are taking place in cities and municipalities and being regulated at those levels too.
We call it AI localism. In what follows, I outline a vision of a more decentralized approach to AI governance, one that would allow cities and local jurisdictions — including states — to develop and iterate governance frameworks tailored to their specific needs and challenges. This decentralized, local approach would need to take place alongside global efforts. The two would not be mutually exclusive but instead necessarily complementary…(More)”.
