Stefaan Verhulst

A Report of the Center for Open Data Enterprise (CODE): “The U.S. has had a strong bipartisan consensus that open federal data is an essential public good. Since 2009, initiatives by Presidents Obama, Trump, and Biden and two acts of Congress have made federal data more accessible, transparent, and useful. The current presidential administration has not challenged these established principles. However, the administration has altered many government data programs on an individual basis, often with the rationale that they do not align with the President’s priorities.
Civil society has responded to these actions with a data rescue movement to archive critical datasets and keep them publicly available. There is a good chance that the movement will be able to save most of the federal data that was available in January 2025.
The greater risk, however, is to the future. The data we have today will not be very useful in a year or two, and future data collections are now under threat. Since the start of the Trump Administration, the federal government has:
● Dismantled and defunded agencies that collect data mandated by Congress
● Discontinued specific data programs
● Defunded research that can be a source of open scientific data
● Disbanded advisory committees for the U.S. Census Bureau and other data-collecting
agencies and offices
● Removed data disaggregated by sexual orientation and gender identity
● Proposed changing established methods of data collection and publishing in some key
areas
These changes can have a major impact on the many institutions – including state and local governments, businesses, civil society organizations, and more – that depend on federal data for policymaking, decision making, and growth…(More)”

Report by ProPublica: “The Internal Revenue Service is building a computer program that would give deportation officers unprecedented access to confidential tax data.

ProPublica has obtained a blueprint of the system, which would create an “on demand” process allowing Immigration and Customs Enforcement to obtain the home addresses of people it’s seeking to deport.

Last month, in a previously undisclosed dispute, the acting general counsel at the IRS, Andrew De Mello, refused to turn over the addresses of 7.3 million taxpayers sought by ICE. In an email obtained by ProPublica, De Mello said he had identified multiple legal “deficiencies” in the agency’s request.

Two days later, on June 27, De Mello was forced out of his job, people familiar with the dispute said. The addresses have not yet been released to ICE. De Mello did not respond to requests for comment, and the administration did not address questions sent by ProPublica about his departure.

The Department of Government Efficiency began pushing the IRS to provide taxpayer data to immigration agents soon after President Donald Trump took office. The tax agency’s acting general counsel refused and was replaced by De Mello, who Trump administration officials viewed as more willing to carry out the president’s agenda. Soon after, the Department of Homeland Security, ICE’s parent agency, and the IRS negotiated a “memorandum of understanding” that included specific legal guardrails to safeguard taxpayers’ private information.

In his email, De Mello said ICE’s request for millions of records did not meet those requirements, which include having a written assurance that each taxpayer whose address is being sought was under active criminal investigation.

“There’s just no way ICE has 7 million real criminal investigations, that’s a fantasy,” said a former senior IRS official who had been advising the agency on this issue. The demands from the DHS were “unprecedented,” the official added, saying the agency was pressing the IRS to do what amounted to “a big data dump.”

In the past, when law enforcement sought IRS data to support its investigations, agencies would give the IRS the full legal name of the target, an address on file and an explanation of why the information was relevant to a criminal inquiry. Such requests rarely involved more than a dozen people at a time, former IRS officials said.

Danny Werfel, IRS commissioner during the Biden administration, said the privacy laws allowing federal investigators to obtain taxpayer data have never “been read to open the door to the sharing of thousands, tens of thousands, or hundreds of thousands of tax records for a broad-based enforcement initiative.”

A spokesperson for the White House said the planned use of IRS data was legal and a means of fulfilling Trump’s campaign pledge to carry out mass deportations of “illegal criminal aliens.”

Taxpayer data is among the most confidential in the federal government and is protected by strict privacy laws, which have historically limited its transfer to law enforcement and other government agencies. Unauthorized disclosure of taxpayer return information is a felony that can carry a penalty of up to five years in prison…(More)”.

Paper by Christophe Gouache: “.. we’ll propose to draw a new guiding approach to policymaking inspired by design. To do so, we will build upon the initial critics of the policy cycle model such as the one of Lasswell drawn in 1957 (still in use today as a reference model) which, despite its clarity and simplicity, is purely theoretical (too linear, too static and too rational) and never takes place in the « real world » and question the actionability of policy threads or streams model as proposed by Howlett in 2015. Then, we’ll look into how, in practice, designers have, through practice, « tinted » policy making with their own methods to finally, extract and draw a new model for policy making, one which would build upon the design thinking & doing methodology (questioning the double diamond of the UK Design Council) but also the design « spirit » (capacity to improvise, to detour, to navigate at ease through uncertainty, etc.)…(More)”.

Report by the National Academies of Sciences, Engineering, and Medicine: “Intergenerational mobility is an important measure of well-being that underlies a fundamental value: that anyone should be able to succeed economically based on their own merits, regardless of their circumstances. This has been a value held by many Americans throughout U.S. history, even as many observers may rightly argue that it has been, at times and for many groups, severely constrained. For all the emphasis placed on mobility in the United States, the chances Americans have of doing better than their parents and their chances of succeeding economically regardless of the advantages of birth are not higher than in other wealthy countries.

This report provides a forward-looking framework for data, research, and policy initiatives to boost upward mobility and better fulfill promises of opportunity and advancement for all members of U.S. society. The report focuses on key domains that shape mobility, including early life and family; the spaces and places where people live and work; postsecondary education; and credit, wealth, and debt. It also discusses the data infrastructure needed to support an extensive research agenda on economic and social mobility…(More)”.

Paper by Wolfram Barfuss et al: “Cooperation at scale is critical for achieving a sustainable future for humanity. However, achieving collective, cooperative behavior—in which intelligent actors in complex environments jointly improve their well-being—remains poorly understood. Complex systems science (CSS) provides a rich understanding of collective phenomena, the evolution of cooperation, and the institutions that can sustain both. Yet, much of the theory in this area fails to fully consider individual-level complexity and environmental context—largely for the sake of tractability and because it has not been clear how to do so rigorously. These elements are well captured in multiagent reinforcement learning (MARL), which has recently put focus on cooperative (artificial) intelligence. However, typical MARL simulations can be computationally expensive and challenging to interpret. In this perspective, we propose that bridging CSS and MARL affords new directions forward. Both fields can complement each other in their goals, methods, and scope. MARL offers CSS concrete ways to formalize cognitive processes in dynamic environments. CSS offers MARL improved qualitative insight into emergent collective phenomena. We see this approach as providing the necessary foundations for a proper science of collective, cooperative intelligence. We highlight work that is already heading in this direction and discuss concrete steps for future research…(More)”.

The Economist: “… artificial intelligence is transforming the way that people navigate the web. As users pose their queries to chatbots rather than conventional search engines, they are given answers, rather than links to follow. The result is that “content” publishers, from news providers and online forums to reference sites such as Wikipedia, are seeing alarming drops in their traffic.

As AI changes how people browse, it is altering the economic bargain at the heart of the internet. Human traffic has long been monetised using online advertising; now that traffic is drying up. Content producers are urgently trying to find new ways to make AI companies pay them for information. If they cannot, the open web may evolve into something very different.

Since the launch of ChatGPT in late 2022, people have embraced a new way to seek information online. OpenAI, maker of ChatGPT, says that around 800m people use the chatbot. It is the most popular download on the iPhone app store. Apple said that conventional searches in its Safari web browser had fallen for the first time in April, as people posed their questions to AI instead. OpenAI is soon expected to launch a browser of its own. Its rise is so dramatic that a Hollywood adaptation is in the works.

As OpenAI and other upstarts have soared, Google, which has about 90% of the conventional search market in America, has added AI features to its own search engine in a bid to keep up. Last year it began preceding some search results with AI-generated “overviews”, which have since become ubiquitous. In May it launched “AI mode”, a chatbot-like version of its search engine. The company promises that, with AI, users can “let Google do the Googling for you”.

Yet as Google does the Googling, humans no longer visit the websites from which the information is gleaned. Similarweb, which measures traffic to more than 100m web domains, estimates that worldwide search traffic (by humans) fell by about 15% in the year to June. Although some categories, such as hobbyists’ sites, are doing fine, others have been hit hard (see chart). Many of the most affected are just the kind that might have commonly answered search queries. Science and education sites have lost 10% of their visitors. Reference sites have lost 15%. Health sites have lost 31%…(More)”.

Paper by Liz Richardson, Catherine Durose, Lucy Kimbell and Ramia Mazé: “Design for policy’ is a prominent framing of the intersection between policy and design. Here, we ask, if design is ‘for’ policy, then what exactly is it doing? We make a critique of literature that explains the interaction of design and policy by listing practices (prototyping or visualisation, for example) but that misses the reasons why those practices are being used. We build on and advance scholarship that anchors design in relation to the demands, constraints and politics of policy making, taking account of the quite different forms a relationship between design (as a thing) and policy design (as a process) can have. Within this debate we propose that design’s relationship to policy is not always in service to (‘for’), but also sometimes ‘with’, and even sometimes ‘against’. We set out an original typology which differentiates roles of design in policy along the lines of their ultimate purpose, scope and terms on which design and policy interact. We identify an instrumental relationship, in which design is a tool to support achieving specified goals of policy making; an improvisational relationship, seeing design as a practice enabling policy making to be more open in the face of unfolding events and experiences; and a generative relationship where design facilitates the re-envisioning of policy making. Through our analysis and proposed typology, we aim to address overly specific and overly homogenising understandings of design in the policy space, enabling a more critical understanding of the different intents and implications at play within the ‘design turn’ in policy…(More)”.

A classification system by the Dubai Future Foundation (DFF): “… that supports a visual representation of the evolving human-machine collaboration in research, design and publications.

Described in detail in our white paper, our aim is to support transparency in research and provide – at a glance – a standard mechanism that allows readers, researchers and decision-makers to see the extent to which research outputs have been shaped by machines, i.e. a process based approach. While we recognise that research, design and publications in the future may increasingly rely on autonomous processes, this shift may not be uniformly applied across all contexts, fields, functions and industries during the transitional period, a time frame that may last a couple of years or up to (and perhaps even longer than) 10 years.

Effective from the date of the white paper, every DFF research report will display respective icons for human-machine collaboration, demonstrating our commitment to transparency and establishing a new standard for ethical research practices…(More)”

Article by Eman Alashwali: “…aims to shed light on the often-overlooked difference between two main types of privacy from a control perspective: Privacy between a user and other users, and privacy between a user and institutions. I discuss why this difference is important and what we need to do from here…

Raynes-Goldie coined the term social privacy as opposed to institutional privacy. The former is about controlling access to personal information while the latter is about controlling how institutions such as Facebook and their partners might use this information. Heyman et al. defined the term privacy as subject to refer to controlling a user’s personal information disclosure to other users, and privacy as object to refer to controlling information disclosure to third parties, which represent the user as an object in a data mining process. Brandimarte et al. classified privacy controls according to purpose, where release controls refer to controlling information disclosure between users, while usage controls refer to controlling the use of users’ information, for example, by the service providers or third parties. Bazarova and Masur introduced multiple approaches to privacy, which include the networked approach where information flows in a horizontal direction between users, and the institutional approach where information flows in a vertical direction between a user and institution.

I will use the terms user-to-user privacy and user-to-institution privacy. In user-to-user, the other users could be family, friends, coworkers, and others. In user-to-institution, the institution could be a service provider, organization, government, and so forth.

In recent years, many service providers, for example, social media platforms, have improved the privacy controls provided to users. However, they may have improved one type of privacy controls: the user-to-user.³ Ignoring the difference between the two types of privacy controls may lead users to have an illusory sense of control over their privacy. For example, users’ perceived control over user-to-user privacy may result in fewer privacy concerns as a result of an incomplete assessment of the associated risks of data sharing, ignoring what Stutzman called “silent listeners.”..(More)”.

Report by Dan Ciuriak: “Data is widely acknowledged as the essential capital asset of the modern economy, yet its value remains largely invisible in corporate balance sheets and understated in national economic accounts. This paper argues that conventional valuation approaches — particularly those based on the costs of datafication — capture only part of the story. While expenditures on datafication
enter GDP as investment in intangible assets, they do not reflect the substantial economic rents
generated by the effective use of data within firms. These rents arise from data’s distinctive economic characteristics, including non-rivalry and combinatorial scalability, and its role in creating information asymmetries that give data-rich firms a competitive advantage. As a result, data contributes to enterprise value not through direct transactions, but by enhancing profitability, accelerating innovation through machine learning, and enabling the creation of machine knowledge capital.
Drawing on trends in the US economy, the paper estimates that data rents alone account for more
than two percent of GDP — representing a layer of value in addition to the investment flows currently captured in GDP. This has profound implications for national accounting methodologies, which
underestimate the value contribution of data. It also flags risks for economic policy in small open
economies that lack the scale to effectively capture data rents, since investing in datafication at less than critical scale may not recover costs and may result in negative productivity outcomes…(More)”.