Paper by Fanni Kertesz: “The European healthcare sector is transforming toward patient-centred and value-based healthcare delivery. The European Health Data Space (EHDS) Regulation aims to unlock the potential of health data by establishing a single market for its primary and secondary use. This paper examines the legal challenges associated with the secondary use of health data within the EHDS and offers recommendations for improvement. Key issues include the compatibility between the EHDS and the General Data Protection Regulation (GDPR), barriers to cross-border data sharing, and intellectual property concerns. Resolving these challenges is essential for realising the full potential of health data and advancing healthcare research and innovation within the EU…(More)”.
Geographies of missing data: Spatializing counterdata production against feminicide
Paper by Catherine D’Ignazio et al: “Feminicide is the gender-related killing of cisgender and transgender women and girls. It reflects patriarchal and racialized systems of oppression and reveals how territories and socio-economic landscapes configure everyday gender-related violence. In recent decades, many grassroots data production initiatives have emerged with the aim of monitoring this extreme but invisibilized phenomenon. We bridge scholarship in feminist and information geographies with data feminism to examine the ways in which space, broadly defined, shapes the counterdata production strategies of feminicide data activists. Drawing on a qualitative study of 33 monitoring efforts led by civil society organizations across 15 countries, primarily in Latin America, we provide a conceptual framework for examining the spatial dimensions of data activism. We show how there are striking transnational patterns related to where feminicide goes unrecorded, resulting in geographies of missing data. In response to these omissions, activists deploy multiple spatialized strategies to make these geographies visible, to situate and contextualize each case of feminicide, to reclaim databases as spaces for memory and witnessing, and to build transnational networks of solidarity. In this sense, we argue that data activism about feminicide constitutes a space of resistance and resignification of everyday forms of gender-related violence…(More)”.
DAOs of Collective Intelligence? Unraveling the Complexity of Blockchain Governance in Decentralized Autonomous Organizations
Paper by Mark C. Ballandies, Dino Carpentras, and Evangelos Pournaras: “Decentralized autonomous organizations (DAOs) have transformed organizational structures by shifting from traditional hierarchical control to decentralized approaches, leveraging blockchain and cryptoeconomics. Despite managing significant funds and building global networks, DAOs face challenges like declining participation, increasing centralization, and inabilities to adapt to changing environments, which stifle innovation. This paper explores DAOs as complex systems and applies complexity science to explain their inefficiencies. In particular, we discuss DAO challenges, their complex nature, and introduce the self-organization mechanisms of collective intelligence, digital democracy, and adaptation. By applying these mechansims to improve DAO design and construction, a practical design framework for DAOs is created. This contribution lays a foundation for future research at the intersection of complexity science and DAOs…(More)”.
Using internet search data as part of medical research
Blog by Susan Thomas and Matthew Thompson: “…In the UK, almost 50 million health-related searches are made using Google per year. Globally there are 100s of millions of health-related searches every day. And, of course, people are doing these searches in real-time, looking for answers to their concerns in the moment. It’s also possible that, even if people aren’t noticing and searching about changes to their health, their behaviour is changing. Maybe they are searching more at night because they are having difficulty sleeping or maybe they are spending more (or less) time online. Maybe an individual’s search history could actually be really useful for researchers. This realisation has led medical researchers to start to explore whether individuals’ online search activity could help provide those subtle, almost unnoticeable signals that point to the beginning of a serious illness.
Our recent review found 23 studies have been published so far that have done exactly this. These studies suggest that online search activity among people later diagnosed with a variety of conditions ranging from pancreatic cancer and stroke to mood disorders, was different to people who did not have one of these conditions.
One of these studies was published by researchers at Imperial College London, who used online search activity to identify signals of women with gynaecological malignancies. They found that women with malignant (e.g. ovarian cancer) and benign conditions had different search patterns, up to two months prior to a GP referral.
Pause for a moment, and think about what this could mean. Ovarian cancer is one of the most devastating cancers women get. It’s desperately hard to detect early – and yet there are signals of this cancer visible in women’s internet searches months before diagnosis?…(More)”.
Even laypeople use legalese
Paper by Eric Martínez, Francis Mollica and Edward Gibson: “Whereas principles of communicative efficiency and legal doctrine dictate that laws be comprehensible to the common world, empirical evidence suggests legal documents are largely incomprehensible to lawyers and laypeople alike. Here, a corpus analysis (n = 59) million words) first replicated and extended prior work revealing laws to contain strikingly higher rates of complex syntactic structures relative to six baseline genres of English. Next, two preregistered text generation experiments (n = 286) tested two leading hypotheses regarding how these complex structures enter into legal documents in the first place. In line with the magic spell hypothesis, we found people tasked with writing official laws wrote in a more convoluted manner than when tasked with writing unofficial legal texts of equivalent conceptual complexity. Contrary to the copy-and-edit hypothesis, we did not find evidence that people editing a legal document wrote in a more convoluted manner than when writing the same document from scratch. From a cognitive perspective, these results suggest law to be a rare exception to the general tendency in human language toward communicative efficiency. In particular, these findings indicate law’s complexity to be derived from its performativity, whereby low-frequency structures may be inserted to signal law’s authoritative, world-state-altering nature, at the cost of increased processing demands on readers. From a law and policy perspective, these results suggest that the tension between the ubiquity and impenetrability of the law is not an inherent one, and that laws can be simplified without a loss or distortion of communicative content…(More)”.
Regulating the Direction of Innovation
Paper by Joshua S. Gans: “This paper examines the regulation of technological innovation direction under uncertainty about potential harms. We develop a model with two competing technological paths and analyze various regulatory interventions. Our findings show that market forces tend to inefficiently concentrate research on leading paths. We demonstrate that ex post regulatory instruments, particularly liability regimes, outperform ex ante restrictions in most scenarios. The optimal regulatory approach depends critically on the magnitude of potential harm relative to technological benefits. Our analysis reveals subtle insurance motives in resource allocation across research paths, challenging common intuitions about diversification. These insights have important implications for regulating emerging technologies like artificial intelligence, suggesting the need for flexible, adaptive regulatory frameworks…(More)”.
Rejecting Public Utility Data Monopolies
Paper by Amy L. Stein: “The threat of monopoly power looms large today. Although not the telecommunications and tobacco monopolies of old, the Goliaths of Big Tech have become today’s target for potential antitrust violations. It is not only their control over the social media infrastructure and digital advertising technologies that gives people pause, but their monopolistic collection, use, and sale of customer data. But large technology companies are not the only private companies that have exclusive access to your data; that can crowd out competitors; and that can hold, use, or sell your data with little to no regulation. These other private companies are not data companies, platforms, or even brokers. They are public utilities.
Although termed “public utilities,” these entities are overwhelmingly private, shareholder-owned entities. Like private Big Tech, utilities gather incredible amounts of data from customers and use this data in various ways. And like private Big Tech, these utilities can exercise exclusionary and self-dealing anticompetitive behavior with respect to customer data. But there is one critical difference— unlike Big Tech, utilities enjoy an implied immunity from antitrust laws. This state action immunity has historically applied to utility provision of essential services like electricity and heat. As utilities find themselves in the position of unsuspecting data stewards, however, there is a real and unexplored question about whether their long- enjoyed antitrust immunity should extend to their data practices.
As the first exploration of this question, this Article tests the continuing application and rationale of the state action immunity doctrine to the evolving services that a utility provides as the grid becomes digitized. It demonstrates the importance of staunching the creep of state action immunity over utility data practices. And it recognizes the challenges of developing remedies for such data practices that do not disrupt the state-sanctioned monopoly powers of utilities over the provision of essential services. This Article analyzes both antitrust and regulatory remedies, including a new customer- focused “data duty,” as possible mechanisms to enhance consumer (ratepayer) welfare in this space. Exposing utility data practices to potential antitrust liability may be just the lever that is needed to motivate states, public utility commissions, and utilities to develop a more robust marketplace for energy data…(More)”.
Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It
Paper by Philipp Hacker, Frederik Zuiderveen Borgesius, Brent Mittelstadt and Sandra Wachter: “Generative AI (genAI) technologies, while beneficial, risk increasing discrimination by producing demeaning content and subtle biases through inadequate representation of protected groups. This chapter examines these issues, categorizing problematic outputs into three legal categories: discriminatory content; harassment; and legally hard cases like harmful stereotypes. It argues for holding genAI providers and deployers liable for discriminatory outputs and highlights the inadequacy of traditional legal frameworks to address genAI-specific issues. The chapter suggests updating EU laws to mitigate biases in training and input data, mandating testing and auditing, and evolving legislation to enforce standards for bias mitigation and inclusivity as technology advances…(More)”.
The problem of ‘model collapse’: how a lack of human data limits AI progress
Article by Michael Peel: “The use of computer-generated data to train artificial intelligence models risks causing them to produce nonsensical results, according to new research that highlights looming challenges to the emerging technology.
Leading AI companies, including OpenAI and Microsoft, have tested the use of “synthetic” data — information created by AI systems to then also train large language models (LLMs) — as they reach the limits of human-made material that can improve the cutting-edge technology.
Research published in Nature on Wednesday suggests the use of such data could lead to the rapid degradation of AI models. One trial using synthetic input text about medieval architecture descended into a discussion of jackrabbits after fewer than 10 generations of output.
The work underlines why AI developers have hurried to buy troves of human-generated data for training — and raises questions of what will happen once those finite sources are exhausted.
“Synthetic data is amazing if we manage to make it work,” said Ilia Shumailov, lead author of the research. “But what we are saying is that our current synthetic data is probably erroneous in some ways. The most surprising thing is how quickly this stuff happens.”
The paper explores the tendency of AI models to collapse over time because of the inevitable accumulation and amplification of mistakes from successive generations of training.
The speed of the deterioration is related to the severity of shortcomings in the design of the model, the learning process and the quality of data used.
The early stages of collapse typically involve a “loss of variance”, which means majority subpopulations in the data become progressively over-represented at the expense of minority groups. In late-stage collapse, all parts of the data may descend into gibberish…(More)”.
Anonymization: The imperfect science of using data while preserving privacy
Paper by Andrea Gadotti et al: “Information about us, our actions, and our preferences is created at scale through surveys or scientific studies or as a result of our interaction with digital devices such as smartphones and fitness trackers. The ability to safely share and analyze such data is key for scientific and societal progress. Anonymization is considered by scientists and policy-makers as one of the main ways to share data while minimizing privacy risks. In this review, we offer a pragmatic perspective on the modern literature on privacy attacks and anonymization techniques. We discuss traditional de-identification techniques and their strong limitations in the age of big data. We then turn our attention to modern approaches to share anonymous aggregate data, such as data query systems, synthetic data, and differential privacy. We find that, although no perfect solution exists, applying modern techniques while auditing their guarantees against attacks is the best approach to safely use and share data today…(More)”.