Stefaan Verhulst

Book by Andrew Guthrie Ferguson: “For consumers living in a digitally-connected world, smart technologies have built an inescapable trap of digital self-surveillance. Smart cars, smart homes, smart watches, and smart medical devices track our most private activities and intimate patterns. While these devices allow users to receive personal insights by monitoring their every move, that data can be accessed by police and prosecutors looking to find incriminating clues. Digital technology exposes everyone, everywhere, all at once, and we have few laws to regulate it.

In Your Data Will Be Used Against You, Andrew Guthrie Ferguson warns us of how the rise of sensor-driven technology, social media monitoring, and artificial intelligence can be weaponized against democratic values and personal freedoms. At the same time, that data will solve crimes, radically transforming how criminal cases are prosecuted. Ferguson explores how this proliferation of private data in combination with public surveillance networks promises new ways to solve previously unsolvable crimes, but also leaves us vulnerable to governmental overreach and abuse. He argues for legal interventions that address the threat of digital self-surveillance and provides concrete suggestions about how legislators, judges, and communities should respond…(More)”.

Article by Ioannis Chrysakis et al: “The 2020 European Strategy for Data aims at developing Common European Data Spaces as a means to build a pan-European single market for data, thereby supporting economic growth and maximizing citizens’ use of data. It demands data spaces in strategic sectors, with capabilities for effective data management and sharing. Although several initiatives support their adoption, data spaces are still in the early stages of development and face several data management and sharing challenges. To identify the requirements needed to address these challenges, we review the literature in developing a conceptual framework for applying data management and sharing in the context of data spaces. We then evaluate the practical implementation of the proposed solutions by analysing six representative European-funded projects. Focusing on requirements from trust and business models to interoperability, workflow orchestration, energy efficiency, and data quality, the work highlights prominent issues and explains how each project addresses them through technical means. Our evaluation outlines each project’s aim and contribution, along with a representative use case from different domains, e.g., water, agriculture, and energy. We recognize widely accepted strategies such as the use of semantic standards, data catalogues, distributed ledger technologies for trust enhancement, and federated identity management. This work highlights recurring patterns, common practices, and key differences in implementation and identifies open research gaps. Thus, it aims to inform future initiatives and provide concrete recommendations to researchers and practitioners on achieving data spaces with best practices. As the field matures, the work hopes to help achieve scalable, stable, and cross-domain data spaces that support sustainable innovation and long-term collaboration throughout Europe…(More)”.

Article by Jack Hardinges, and Irina Bejan: “Attribution has always been a cornerstone of working with other people’s creativity and knowledge.

As Creative Commons reminds us, practicing attribution serves many important functions, from providing verifiable evidence of a claim, to paying respect to the work of others, and creating pathways for traffic and financial value to flow.

Despite the importance of attribution, many of today’s AI systems fail to acknowledge the sources of knowledge and creativity that make them possible.

A recent study led by the AI Disclosures Project found that more than 30% of responses by leading search-enabled Large Language Models (LLMs) provided no attribution whatsoever. Even where attribution is provided, it can be limited in ways that are hidden to users, and deep technical challenges persist in connecting outputs from AI to the sources they are derived from.

Despite this, we believe there are good reasons to be optimistic that AI systems can attribute the sources they use. Not perfectly, and not always, but enough and improving, such that we should reject the argument that a lack of attribution is an inevitable side effect of the way the technology works. The commons needn’t become “hidden substrate.”..(More)”.

Article by Alexandra Bruell: “One of the richest sources of online information is re-evaluating its relationship with Google.

Reddit, the online message board that powers a swath of Google search results, has discussed shutting off the technology giant’s access to its content for AI use, according to people familiar with the matter.

It is part of a growing chorus of online media companies expressing frustration with the tech giant as AI changes the way people ask questions, siphons off search traffic and upends publishers’ revenue models. They say the search engine is no longer a reliable source of visitors, especially after Alphabet’s GOOGL  Google expanded its AI search features in recent months. USA Today, Politico, the Economist, People Inc. and Reuters are all evaluating how, or even if, they will continue to work with Google.

Reddit struck a $60 million-a-year deal in 2024 that allowed Google to use its material to train AI models. But with AI-generated answers to queries reducing clicks to outside websites, Reddit executives are assessing what the upside is of continuing to feed its content to Google, said the people familiar with the matter. The companies are in talks about potentially renewing their deal, which is ending soon.

“This is existential for some categories of publishers,” said David Buttle, CEO of media consulting firm DJB Strategies. “They are looking at more radical things.”..(More)”.

Paper by Zeynep Engin et al: “The digital substrate of states — data, algorithms, infrastructure, platforms, applications — is being governed without adequate conceptual foundations. The ability and legitimacy required to govern this substrate, and to govern with it, are simultaneously misaligned, contested, and structurally absent. We introduce digital statecraft as the organising concept for this emerging field, arguing that ‘digital’ reconstitutes the statecraft question rather than merely extending its domain. The concept operates on two dimensions – statecraft over digital systems, concerning the authority and capacity of the state in relation to the digital substrate itself, and statecraft with digital systems, concerning the deployment of algorithmic tools as instruments of governing authority. And it rests on two foundational requirements, technical coherence and legitimate authority, that are genuinely in tension. We derive ten principles of digital statecraft from these foundations, each naming a condition whose absence produces an identifiable and structural governance failure: public interest first, human-machine complementarity, governability by design, systemic coherence, hybrid institutions, adaptive governance, human centricity and civic agency, accountable and traceable authority, judgment across time, and the non-delegable core. This article takes the state as the starting point, the institutional form that developed historically in response to the problem of effective and legitimate public governance, and the only current candidate for which the full set of legitimacy conditions is institutionally available. But the digital statecraft programme holds open a deeper question than just whether states can reform themselves: governing well in the algorithmic age may require rethinking the boundaries, scale, and affiliative basis of statehood itself…(More)”.

Article by Emanuel Maiberg: “As AI companies search for more training data to improve their models, one company is offering old, printed books as an ideal source because they are guaranteed to be free of the very AI slop AI companies are producing. “The world’s best AI training data is sitting on a shelf,” ISBNdb, a company that produces what it claims is “the world’s largest book database,” and that offers high-volume book acquisition services for AI companies, says on its site. “Books represent curated, peer-reviewed, domain-specific human knowledge, structured in a way no web crawl can replicate. Dense, edited, authoritative.” In one article on its site, ISBNdb explains that printed books published before 2022 are ideal for AI training data because they don’t include AI generated text. As the article correctly notes, much of the data that AI companies can scrape from the internet today is likely to include AI generated text, which could result in “model collapse,” a process by which AI models that are trained on AI generated data results in worse models that are more prone to errors. The article also notes that book authors who object to their writing being scraped for training purposes can now easily poison AI models by producing writing designed to manipulate and sabotage the resulting AI models. As AI companies search for more training data to improve their models, one company is offering old, printed books as an ideal source because they are guaranteed to be free of the very AI slop AI companies are producing…(More)”

Article by Leya Mohsin: “…Successful moonshots involve both a clear what and a clear how. If you think about prior moonshot efforts, there are probably a select few that jump to mind: the Apollo Program, the Manhattan Project, and the Human Genome Project are the most notable efforts. What’s something these all have in common?: A “moon”, a goal that anyone can clearly define. This is a critical element of successful moonshots.

But other similar efforts have been tried before to less avail. As an example, consider the Human Brain Project. This project had a clear goal: to simulate the entire human brain on a computer. It concluded in 2023 after 10 years and €1 billion spent. The effort did lead to significant advances in neuroscience, but it failed to accomplish its stated goal. From the jump, scientists were highly critical of this initiative because they did not believe the requisite technology existed, or was even close to existing. In other words, the how was unclear. 

This is common for many such initiatives. For example, when the Human Genome Project was launched, we did not have the technology capable of meeting the mission. The project was highly criticized by many in the research community because the traditional method of DNA sequencing could not possibly be scaled to meet the goal. They needed a new way to sequence DNA faster, with longer reads, and for less money. Therefore, the first goal was to drive investment in the development of technology that could answer the how question. 

A successful moonshot is one where it’s clear that we are on the precipice of achieving a once-unimaginable goal, if only we make a significant investment toward that end. Think of the original moonshot: when JFK announced this initiative, we had seen a man orbit the Earth. And we knew that putting a man on the moon was challenging, but far from impossible – we just needed to devote the appropriate time, resources, and organization around that goal…(More)”.

Article by Anne-Laure Le Cunff: “More than 60 percent of Google searches in the United States now end without the user clicking on a link. We type a question, read an artificial-intelligence-generated summary of the results and leave with our answer.

Google is hardly alone. Claude, ChatGPT and upstart competitors like Perplexity do roughly the same thing: They take a question and swiftly return an answer, compressing what used to be a meandering journey through the internet into an immediate arrival at your destination. The explorative phase of searches — clicking through links, stumbling onto unexpected pages, following a reference that leads to somewhere unplanned — is disappearing.

For anyone who publishes on the internet, this is a troubling development, since it lowers website traffic and makes protecting and profiting from your intellectual property more difficult. But you might think it is good news for internet users. Could there be anything wrong with getting a reliable answer more quickly?

There is. By shortening the time between asking a question and getting an answer, these tools are actually undermining curiosity — and paradoxically threatening our ability to understand the world.

About a decade ago, I worked at Google. When I was there, we often measured the value of internet content based on factors that indicated user engagement, like clicks and scroll depth. The metric Google seemed to reward — people exploring — is precisely what its A.I. products are now designed to eliminate.

I left Google to study neuroscience, and what I found in the research literature helps explain why the A.I. summary poses a danger to learning. Curiosity, it turns out, is not just an individual’s desire to find out discrete facts; it’s also a feature of our biology designed to help us learn more broadly. And it requires a specific condition: a gap between what you want to know and what you find out.

Researchers have found that people in a state of curiosity, while waiting for an answer to an intriguing question, remember unrelated information they encounter during that time far better than they otherwise would. In that study, the researchers also placed those people in brain scanners. They found that waiting for an answer activates reward circuits in the brain and readies the hippocampus to help create memories. Similar findings have been reported by other researchers in studies involving infants, older children and adults.

In short, curiosity puts the entire brain into a mode of heightened receptivity — not just for the specific thing you want to know but also for everything around it. Curiosity opens a window, and while the window is open, learning deepens across the board…(More)”.

Essay by Audrey Tang: “When people ask whether AI threatens humanism, I often feel the question arrives a little late. The deeper threat came earlier, when digital systems learned to sort attention at industrial scale. Long before generative models could write essays, compose songs or simulate conversation, many of us had already been trained to behave like components inside ranking systems: always visible, always reactive, always measurable. By the time synthetic fluency arrived, a quieter transformation was underway. We were becoming easier to score.

This is why I do not think the seminal question of this century is whether machines will become more human. The question is whether humans will still be allowed to remain more than something that machines can easily evaluate.

I have seen both possibilities. On one hand, a language model can help me enter a conversation I otherwise could not have had. Before meeting a Japanese thinker, whose newest work I could not read in the original, I used AI to build a working vocabulary across our different philosophical traditions. The system did not replace the encounter. It made the encounter possible. Once the conversation began, the model’s importance began to fade. It had done its work well precisely because it no longer needed to be at the center.

On the other hand, we have all experienced systems that do the opposite. They do not deepen understanding. They train people to live at the tempo of the feed, to compress themselves into whatever is most legible to the platform, and to mistake constant reaction for participation. The issue is not that AI will talk like us. The issue is that institutions will reward us for talking like machines.

Our scarcity is not content. It is mutual comprehension. A humane technological future will not be secured by sentimentality, nor by a nostalgic defense of every task that software can now accelerate. It will depend on whether we can design tools, institutions and norms that protect what is distinctively human in public life: the capacity to interpret one another across differences, to make commitments that bind us over time, to revise ourselves without humiliation, to hold power answerable and to care for consequences that no benchmark can fully capture…(More)”.

Essay by E. Glen Weyl, James Evans and Chris White: “…Technology capable of enlarging human freedom, abundance and collective intelligence is being deployed into labor markets, public institutions and information systems that were already creaking before AI arrived. Left unaddressed, that mismatch produces three foreseeable failure modes.

1. Productivity without prosperity. As the economists Erik Brynjolfsson, Daniel Rock and Chad Syverson illustrate in a 2021 article, general-purpose technologies create broad value only after heavy complementary investment in new processes, skills and organizational redesign. When those complements lag, work is reorganized faster than people can retrain, bargain or move, and the gains fail to translate into security, mobility or broader participation.
2. Execution without verification. As the economists Christian Catalini, Xiang Hui and Jane Wu argue in a recent working paper, once the cost of producing text, images and code collapses, the scarce input becomes the human bandwidth to validate outputs, establish provenance and ensure accountability. Decisions grow easier to automate than to justify, and algorithmic systems without accountability erode sacred values and the social fabric.
3. Capacity without constraint. AI can make states and large organizations far more capable, but as influential intellectuals and policy leaders in AI, Justin B. Bullock, Samuel Hammond and Sèb Krier note in a chapter published last year in “The Art of Digital Governance,” the same tools lower monitoring costs and concentrate discretion, pushing bureaucracies toward centralized control unless contestability, appeal and civil-liberties safeguards advance in tandem.

None of these is inevitable. Each is a consequence of institutional neglect, which raises the question of why some societies, in earlier periods of upheaval, adapted far more successfully than others…(More)”.