State of Digital Local Government

Curated on Posted on by Stefaan Verhulst

Report by the Local Government Association (UK): “This report is themed around four inter-related areas on the state of local government digital: market concentration, service delivery, technology, and delivery capabilities.  It is particularly challenging to assess the current state of digital transformation in local government, given the diversity of experience, resources and lack of consistent data collection on digital transformation and technology estates. 

This report is informed through our regular and extensive engagement with local government, primary research carried out by the LGA, and the research of stakeholders. It is worth noting that research on market concentration is challenging as it is a highly sensitive area.

Key messages:

  1. Local Government is a vital part of the public sector innovation ecosystem. Local government needs their priorities and context to be understood within cross public sector digital transformation ambitions through representation on public sector strategic boards and subsequently integrated into the design of public sector guidance and cross-government products at the earliest point. This will reduce the likelihood of duplication at public expense. Local government must also have equivalent access to training as civil servants…(More)”.

Governing artificial intelligence means governing data: (re)setting the agenda for data justice

Curated on Posted on by Stefaan Verhulst

Paper by Linnet Taylor, Siddharth Peter de Souza, Aaron Martin, and Joan López Solano: “The field of data justice has been evolving to take into account the role of data in powering the field of artificial intelligence (AI). In this paper we review the main conceptual bases for governing data and AI: the market-based approach, the personal–non-personal data distinction and strategic sovereignty. We then analyse how these are being operationalised into practical models for governance, including public data trusts, data cooperatives, personal data sovereignty, data collaboratives, data commons approaches and indigenous data sovereignty. We interrogate these models’ potential for just governance based on four benchmarks which we propose as a reformulation of the Data Justice governance agenda identified by Taylor in her 2017 framework. Re-situating data justice at the intersection of data and AI, these benchmarks focus on preserving and strengthening public infrastructures and public goods; inclusiveness; contestability and accountability; and global responsibility. We demonstrate how they can be used to test whether a governance approach will succeed in redistributing power, engaging with public concerns and creating a plural politics of AI…(More)”.

Data sharing restrictions are hampering precision health in the European Union

Curated on Posted on by Stefaan Verhulst

Paper by Cristina Legido-Quigley et al: “Contemporary healthcare is undergoing a transition, shifting from a population-based approach to personalized medicine on an individual level. In October 2023, the European Partnership for Personalized Medicine was officially launched to communicate the benefits of this approach to citizens and healthcare systems in member countries. The main debate revolves around the inconsistency in regulatory changes within personal data access and its potential commercialization. Moreover, the lack of unified consensus within European Union (EU) countries is leading to problems with data sharing to progress personalized medicine. Here we discuss the integration of biological data with personal information on a European scale for the advancement of personalized medicine, raising legal considerations of data protection under the EU General Data Protection Regulation (GDPR)…(More)”.

Survey of attitudes in a Danish public towards reuse of health data

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Paper by Lea Skovgaard et al: “Everyday clinical care generates vast amounts of digital data. A broad range of actors are interested in reusing these data for various purposes. Such reuse of health data could support medical research, healthcare planning, technological innovation, and lead to increased financial revenue. Yet, reuse also raises questions about what data subjects think about the use of health data for various different purposes. Based on a survey with 1071 respondents conducted in 2021 in Denmark, this article explores attitudes to health data reuse. Denmark is renowned for its advanced integration of data infrastructures, facilitating data reuse. This is therefore a relevant setting from which to explore public attitudes to reuse, both as authorities around the globe are currently working to facilitate data reuse opportunities, and in the light of the recent agreement on the establishment in 2024 of the European Health Data Space (EHDS) within the European Union (EU). Our study suggests that there are certain forms of health data reuse—namely transnational data sharing, commercial involvement, and use of data as national economic assets—which risk undermining public support for health data reuse. However, some of the purposes that the EHDS is supposed to facilitate are these three controversial purposes. Failure to address these public concerns could well challenge the long-term legitimacy and sustainability of the data infrastructures currently under construction…(More)”

The Limitations of Consent as a Legal Basis for Data Processing in the Digital Society

Curated on Posted on by Stefaan Verhulst

Paper by the Centre for Information Policy Leadership: “Contemporary everyday life is increasingly permeated by digital information, whether by creating, consuming or depending on it. Most of our professional and private lives now rely to a large degree on digital interactions. As a result, access to and the use of data, and in particular personal data, are key elements and drivers of the digital economy and society. This has brought us to a significant inflection point on the issue of legitimising the processing of personal data in the wide range of contexts that are essential to our data-driven, AI-enabled digital products and services. The time has come to seriously re-consider the status of consent as a privileged legal basis and to consider alternatives that are better suited for a wide range of essential data processing contexts. The most prominent among these alternatives are the “legitimate interest” and “contractual necessity” legal bases, which have found an equivalent in a number of jurisdictions. One example is Singapore, where revisions to their data protection framework include a legitimate interest exemption…(More)”.

Towards Civic Digital Twins: Co-Design the Citizen-Centric Future of Bologna

Curated on Posted on by Stefaan Verhulst

Paper by Massimiliano Luca et al: “We introduce Civic Digital Twin (CDT), an evolution of Urban Digital Twins designed to support a citizen-centric transformative approach to urban planning and governance. CDT is being developed in the scope of the Bologna Digital Twin initiative, launched one year ago by the city of Bologna, to fulfill the city’s political and strategic goal of adopting innovative digital tools to support decision-making and civic engagement. The CDT, in addition to its capability of sensing the city through spatial, temporal, and social data, must be able to model and simulate social dynamics in a city: the behavior, attitude, and preference of citizens and collectives and how they impact city life and transform transformation processes. Another distinctive feature of CDT is that it must be able to engage citizens (individuals, collectives, and organized civil society) and other civic stakeholders (utilities, economic actors, third sector) interested in co-designing the future of the city. In this paper, we discuss the motivations that led to the definition of the CDT, define its modeling aspects and key research challenges, and illustrate its intended use with two use cases in urban mobility and urban development…(More)”.

Revealed: bias found in AI system used to detect UK benefits fraud

Curated on Posted on by Stefaan Verhulst

Article by Robert Booth: “An artificial intelligence system used by the UK government to detect welfare fraud is showing bias according to people’s age, disability, marital status and nationality, the Guardian can reveal.

An internal assessment of a machine-learning programme used to vet thousands of claims for universal credit payments across England found it incorrectly selected people from some groups more than others when recommending whom to investigate for possible fraud.

The admission was made in documents released under the Freedom of Information Act by the Department for Work and Pensions (DWP). The “statistically significant outcome disparity” emerged in a “fairness analysis” of the automated system for universal credit advances carried out in February this year.

The emergence of the bias comes after the DWP this summer claimed the AI system “does not present any immediate concerns of discrimination, unfair treatment or detrimental impact on customers”.

This assurance came in part because the final decision on whether a person gets a welfare payment is still made by a human, and officials believe the continued use of the system – which is attempting to help cut an estimated £8bn a year lost in fraud and error – is “reasonable and proportionate”.

But no fairness analysis has yet been undertaken in respect of potential bias centring on race, sex, sexual orientation and religion, or pregnancy, maternity and gender reassignment status, the disclosures reveal.

Campaigners responded by accusing the government of a “hurt first, fix later” policy and called on ministers to be more open about which groups were likely to be wrongly suspected by the algorithm of trying to cheat the system…(More)”.

The British state is blind

Curated on Posted on by Stefaan Verhulst

The Economist: “Britiain is a bit bigger than it thought. In 2023 net migration stood at 906,000 people, rather more than the 740,000 previously estimated, according to the Office for National Statistics. It is equivalent to discovering an extra Slough. New numbers for 2022 also arrived. At first the ONS thought net migration stood at 606,000. Now it reckons the figure was 872,000, a difference roughly the size of Stoke-on-Trent, a small English city.

If statistics enable the state to see, then the British government is increasingly short-sighted. Fundamental questions, such as how many people arrive each year, are now tricky to answer. How many people are in work? The answer is fuzzy. Just how big is the backlog of court cases? The Ministry of Justice will not say, because it does not know. Britain is a blind state.

This causes all sorts of problems. The Labour Force Survey, once a gold standard of data collection, now struggles to provide basic figures. At one point the Resolution Foundation, an economic think-tank, reckoned the ONS had underestimated the number of workers by almost 1m since 2019. Even after the ONS rejigged its tally on December 3rd, the discrepancy is still perhaps 500,000, Resolution reckons. Things are so bad that Andrew Bailey, the governor of the Bank of England, makes jokes about the inaccuracy of Britain’s job-market stats in after-dinner speeches—akin to a pilot bursting out of the cockpit mid-flight and asking to borrow a compass, with a chuckle.

Sometimes the sums in question are vast. When the Department for Work and Pensions put out a new survey on household income in the spring, it was missing about £40bn ($51bn) of benefit income, roughly 1.5% of gdp or 13% of all welfare spending. This makes things like calculating the rate of child poverty much harder. Labour mps want this line to go down. Yet it has little idea where the line is to begin with.

Even small numbers are hard to count. Britain has a backlog of court cases. How big no one quite knows: the Ministry of Justice has not published any data on it since March. In the summer, concerned about reliability, it held back the numbers (which means the numbers it did publish are probably wrong, says the Institute for Government, another think-tank). And there is no way of tracking someone from charge to court to prison to probation. Justice is meant to be blind, but not to her own conduct…(More)”.

Informality in Policymaking

Curated on Posted on by Stefaan Verhulst

Book edited by Lindsey Garner-Knapp, Joanna Mason, Tamara Mulherin and E. Lianne Visser: “Public policy actors spend considerable time writing policy, advising politicians, eliciting stakeholder views on policy concerns, and implementing initiatives. Yet, they also ‘hang out’ chatting at coffee machines, discuss developments in the hallway walking from one meeting to another, or wander outside to carparks for a quick word and to avoid prying eyes. Rather than interrogating the rules and procedures which govern how policies are made, this volume asks readers to begin with the informal as a concept and extend this to what people do, how they relate to each other, and how this matters.

Emerging from a desire to enquire into the lived experience of policy professionals, and to conceptualise afresh the informal in the making of public policy, Informality in Policymaking explores how informality manifests in different contexts, spaces, places, and policy arenas, and the implications of this. Including nine empirical chapters, this volume presents studies from around the world and across policy domains spanning the rural and urban, and the local to the supranational. The chapters employ interdisciplinary approaches and integrate creative elements, such as drawings of hand gestures and fieldwork photographs, in conjunction with ethnographic ‘thick descriptions’.

In unveiling the realities of how policy is made, this deeply meaningful and thoughtfully constructed collection argues that the formal is only part of the story of policymaking, and thus only part of the solutions it seeks to create. Informality in Policymaking will be of interest to researchers and policymakers alike…(More)”.

Can AI review the scientific literature — and figure out what it all means?

Curated on Posted on by Stefaan Verhulst

Article by Helen Pearson: “When Sam Rodriques was a neurobiology graduate student, he was struck by a fundamental limitation of science. Even if researchers had already produced all the information needed to understand a human cell or a brain, “I’m not sure we would know it”, he says, “because no human has the ability to understand or read all the literature and get a comprehensive view.”

Five years later, Rodriques says he is closer to solving that problem using artificial intelligence (AI). In September, he and his team at the US start-up FutureHouse announced that an AI-based system they had built could, within minutes, produce syntheses of scientific knowledge that were more accurate than Wikipedia pages1. The team promptly generated Wikipedia-style entries on around 17,000 human genes, most of which previously lacked a detailed page.How AI-powered science search engines can speed up your research

Rodriques is not the only one turning to AI to help synthesize science. For decades, scholars have been trying to accelerate the onerous task of compiling bodies of research into reviews. “They’re too long, they’re incredibly intensive and they’re often out of date by the time they’re written,” says Iain Marshall, who studies research synthesis at King’s College London. The explosion of interest in large language models (LLMs), the generative-AI programs that underlie tools such as ChatGPT, is prompting fresh excitement about automating the task…(More)”.