It Was the Best of Times, It Was the Worst of Times: The Dual Realities of Data Access in the Age of Generative AI

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst: “It was the best of times, it was the worst of times… It was the spring of hope, it was the winter of despair.” –Charles Dickens, A Tale of Two Cities

Charles Dickens’s famous line captures the contradictions of the present moment in the world of data. On the one hand, data has become central to addressing humanity’s most pressing challenges — climate change, healthcare, economic development, public policy, and scientific discovery. On the other hand, despite the unprecedented quantity of data being generated, significant obstacles remain to accessing and reusing it. As our digital ecosystems evolve, including the rapid advances in artificial intelligence, we find ourselves both on the verge of a golden era of open data and at risk of slipping deeper into a restrictive “data winter.”

A Tale of Two Cities by Charles Dickens (1902)

These two realities are concurrent: the challenges posed by growing restrictions on data reuse, and the countervailing potential brought by advancements in privacy-enhancing technologies (PETs), synthetic data, and data commons approaches. It argues that while current trends toward closed data ecosystems threaten innovation, new technologies and frameworks could lead to a “Fourth Wave of Open Data,” potentially ushering in a new era of data accessibility and collaboration…(More)” (First Published in Industry Data for Society Partnership’s (IDSP) 2024 Year in Review).

Space, Satellites, and Democracy: Implications of the New Space Age for Democratic Processes and Recommendations for Action

Curated on Posted on by Stefaan Verhulst

NDI Report: “The dawn of a new space age is upon us, marked by unprecedented engagement from both state and private actors. Driven by technological innovations such as reusable rockets and miniaturized satellites, this era presents a double-edged sword for global democracy. On one side, democratized access to space offers powerful tools for enhancing civic processes. Satellite technology now enables real-time election monitoring, improved communication in remote areas, and more effective public infrastructure planning. It also equips democratic actors with means to document human rights abuses and circumvent authoritarian internet restrictions.

However, the accessibility of these technologies also raises significant concerns. The potential for privacy infringements and misuse by authoritarian regimes or malicious actors casts a shadow over these advancements.

This report discusses the opportunities and risks that space and satellite technologies pose to democracy, human rights, and civic processes globally. It examines the current regulatory and normative frameworks governing space activities and highlights key considerations for stakeholders navigating this increasingly competitive domain.

It is essential that the global democracy community be familiar with emerging trends in space and satellite technology and their implications for the future. Failure to do so will leave the community unprepared to harness the opportunities or address the challenges that space capabilities present. It would also cede influence over the development of global norms and standards in this arena to states and private sector interests alone and, in turn, ensure those standards are not rooted in democratic norms and human rights, but rather in principles such as state sovereignty and profit maximization…(More)”.

Synthetic content and its implications for AI policy: a primer

Curated on Posted on by Stefaan Verhulst

UNESCO Paper: “The deployment of advanced Artificial Intelligence (AI) models, particularly generative AI, has sparked discussions regarding the creation and use of synthetic content – i.e. AI-generated or modified outputs, including text, images, sounds, and combinations thereof – and its impact on individuals, societies, and economies. This note explores the different ways in which synthetic content can be generated and used and proposes a taxonomy that encompasses synthetic media and deepfakes, among others. The taxonomy aims to systematize key characteristics, enhancing understanding and informing policy discussions. Key findings highlight both the potential benefits and concerns associated with synthetic content in fields like data analytics, environmental sustainability, education, creativity, and mis/disinformation and point to the need to frame them ethically, in line with the principles and values of UNESCO Recommendation on the Ethics of Artificial Intelligence. Finally, the note brings to the fore critical questions that policymakers and experts alike need to address to ensure that the development of AI technologies aligns with human rights, human dignity, and fundamental freedoms…(More)”.

Setting the Standard: Statistical Agencies’ Unique Role in Building Trustworthy AI

Curated on Posted on by Stefaan Verhulst

Article by Corinna Turbes: “As our national statistical agencies grapple with new challenges posed by artificial intelligence (AI), many agencies face intense pressure to embrace generative AI as a way to reach new audiences and demonstrate technological relevance. However, the rush to implement generative AI applications risks undermining these agencies’ fundamental role as authoritative data sources. Statistical agencies’ foundational mission—producing and disseminating high-quality, authoritative statistical information—requires a more measured approach to AI adoption.

Statistical agencies occupy a unique and vital position in our data ecosystem, entrusted with creating the reliable statistics that form the backbone of policy decisions, economic planning, and social research. The work of these agencies demands exceptional precision, transparency, and methodological rigor. Implementation of generative AI interfaces, while technologically impressive, could inadvertently compromise the very trust and accuracy that make these agencies indispensable.

While public-facing interfaces play a valuable role in democratizing access to statistical information, statistical agencies need not—and often should not—rely on generative AI to be effective in that effort. For statistical agencies, an extractive AI approach – which retrieves and presents existing information from verified databases rather than generating new content – offers a more appropriate path forward. By pulling from verified, structured datasets and providing precise, accurate responses, extractive AI systems can maintain the high standards of accuracy required while making statistical information more accessible to users who may find traditional databases overwhelming. An extractive, rather than generative,  approach allows agencies to modernize data delivery while preserving their core mission of providing reliable, verifiable statistical information…(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 Collaboration Playbook: A leader’s guide to cross-sector collaboration

Curated on Posted on by Stefaan Verhulst

Playbook by Ian Taylor and Nigel Ball: “The challenges facing our societies and economies today are so large and complex that, in many cases, cross-sector collaboration is not a choice, but an imperative. Yet collaboration remains elusive for many, often being put into the ‘too hard’ category. This playbook offers guidance on how we can seize collaboration opportunities successfully and rise to the challenges.

The recommendations in the playbook were informed by academic literature and practitioner experience. Rather than offer a procedural, step-by-step guide, this playbook offers provoking questions and frameworks that applies to different situations and objectives. While formal aspects such as contracts and procedures are well understood, it was found that what was needed was guidance on the intangible elements, sometimes referred to as ‘positive chemistry’. The significance of aspects like leadership, trust, culture, learning and power in cross-sector collaborations can be the game-changers for productive endeavours but are hard to get right.

Structured around these five key themes, the playbook presents 18 discreet ‘plays’ for effective collaboration. The plays allow the reader to delve into specific areas of interest to gain a deeper understanding of what it means for their collaborative work.

The intention of the playbook is to provide a resource that informs and guides cross-sector leaders. It will be especially relevant for those working in, and partnering with, central and local government in an effort to improve social outcomes…(More)”.

Predictability, AI, And Judicial Futurism: Why Robots Will Run The Law And Textualists Will Like It

Curated on Posted on by Stefaan Verhulst

Paper by Jack Kieffaber: “The question isn’t whether machines are going to replace judges and lawyers—they are. The question is whether that’s a good thing. If you’re a textualist, you have to answer yes. But you won’t—which means you’re not a textualist. Sorry.

Hypothetical: The year is 2030.  AI has far eclipsed the median federal jurist as a textual interpreter. A new country is founded; it’s a democratic republic that uses human legislators to write laws and programs a state-sponsored Large Language Model called “Judge.AI” to apply those laws to facts. The model makes judicial decisions as to conduct on the back end, but can also provide advisory opinions on the front end; if a citizen types in his desired action and hits “enter,” Judge.AI will tell him, ex ante, exactly what it would decide ex post if the citizen were to perform the action and be prosecuted. The primary result is perfect predictability; secondary results include the abolition of case law, the death of common law, and the replacement of all judges—indeed, all lawyers—by a single machine. Don’t fight the hypothetical, assume it works. This article poses the question:  Is that a utopia or a dystopia?

If you answer dystopia, you cannot be a textualist. Part I of this article establishes why:  Because predictability is textualism’s only lodestar, and Judge.AI is substantially more predictable than any regime operating today. Part II-A dispatches rebuttals premised on positive nuances of the American system; such rebuttals forget that my hypothetical presumes a new nation and take for granted how much of our nation’s founding was premised on mitigating exactly the kinds of human error that Judge.AI would eliminate. And Part II-B dispatches normative rebuttals, which ultimately amount to moral arguments about objective good—which are none of the textualist’s business. 

When the dust clears, you have only two choices: You’re a moralist, or you’re a formalist. If you’re the former, you’ll need a complete account of the objective good—which has evaded man for his entire existence. If you’re the latter, you should relish the fast-approaching day when all laws and all lawyers are usurped by a tin box.  But you’re going to say you’re something in between. And you’re not…(More)”

Bad data costs Americans trillions. Let’s fix it with a renewed data strategy

Curated on Posted on by Stefaan Verhulst

Article by Nick Hart & Suzette Kent: “Over the past five years, the federal government lost $200-to-$500 billion per year in fraud to improper payments — that’s up to $3,000 taken from every working American’s pocket annually. Since 2003, these preventable losses have totaled an astounding $2.7 trillion. But here’s the good news: We already have the data and technology to greatly eliminate this waste in the years ahead. The operational structure and legal authority to put these tools to work protecting taxpayer dollars needs to be refreshed and prioritized.

The challenge is straightforward: Government agencies often can’t effectively share and verify basic information before sending payments. For example, federal agencies may not be able to easily check if someone is deceased, verify income or detect duplicate payments across programs…(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)”.

Data for Better Governance: Building Government Analytics Ecosystems in Latin America and the Caribbean

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Report by the Worldbank: “Governments in Latin America and the Caribbean face significant development challenges, including insufficient economic growth, inflation, and institutional weaknesses. Overcoming these issues requires identifying systemic obstacles through data-driven diagnostics and equipping public officials with the skills to implement effective solutions.

Although public administrations in the region often have access to valuable data, they frequently fall short in analyzing it to inform decisions. However, the impact is big. Inefficiencies in procurement, misdirected transfers, and poorly managed human resources result in an estimated waste of 4% of GDP, equivalent to 17% of all public spending. 

The report “Data for Better Governance: Building Government Analytical Ecosystems in Latin America and the Caribbean” outlines a roadmap for developing government analytics, focusing on key enablers such as data infrastructure and analytical capacity, and offers actionable strategies for improvement…(More)”.