Stefaan Verhulst

DCMS (UK): “…With the increasing ascendance of data, it has become ever-more important that the government removes the unnecessary barriers that prevent businesses and organisations from accessing such information.

The importance of data sharing was demonstrated during the first few months of the coronavirus pandemic, when government departments, local authorities, charities and the private sector came together to provide essential services. One notable example is the Vulnerable Person Service, which in a very short space of time enabled secure data-sharing across the public and private sectors to provide millions of food deliveries and access to priority supermarket delivery slots for clinically extremely vulnerable people.

Aggregation of data from different sources can also lead to new insights that otherwise would not have been possible. For example, the Connected Health Cities project anonymises and links data from different health and social care services, providing new insights into the way services are used.

Vitally, data sharing can also fuel growth and innovation.²⁰ For new and innovating organisations, increasing data availability will mean that they, too, will be able to gain better insights from their work and access new markets – from charities able to pool beneficiary data to better evaluate the effectiveness of interventions, to new entrants able to access new markets. Often this happens as part of commercial arrangements; in other instances government has sought to intervene where there are clear consumer benefits, such as in relation to Open Banking and Smart Data. Government has also invested in the research and development of new mechanisms for better data sharing, such as the Office for AI and Innovate UK’s partnership with the Open Data Institute to explore data trusts.²¹

However, our call for evidence, along with engagement with stakeholders, has identified a range of barriers to data availability, including:

• a culture of risk aversion
• issues with current licensing regulations
• market barriers to greater re-use, including data hoarding and differential market power
• inconsistent formatting of public sector data
• issues pertaining to the discoverability of data
• privacy and security concerns
• the benefits relating to increased data sharing not always being felt by the organisation incurring the cost of collection and maintenance

This is a complex environment, and heavy-handed intervention may have the unwanted effect of reducing incentives to collect, maintain and share data for the benefit of the UK. It is clear that any way forward must be carefully considered to avoid unintended negative consequences. There is a balance to be struck between maintaining appropriate commercial incentives to collect data, while ensuring that data can be used widely for the benefit of the UK. For personal data, we must also take account of the balance between individual rights and public benefit.

This is a new issue for all digital economies that has come to the fore as data has become a significant modern, economic asset. Our approach will take account of those incentives, and consider how innovation can overcome perceived barriers to availability. For example, it can be limited to users with specific characteristics, by licence or regulator accreditation; it can be shared within a collaborating group of organisations; there may also be value in creating and sharing synthetic data to support research and innovation, as well as other privacy-enhancing technologies and techniques….(More)”.

 Zeynep Tufekci in the Atlantic: “In Michigan, a small liberal-arts college is requiring students to install an app called Aura, which tracks their location in real time, before they come to campus. Oakland University, also in Michigan, announced a mandatory wearable that would track symptoms, but, facing a student-led petition, then said it would be optional. The University of Missouri, too, has an app that tracks when students enter and exit classrooms. This practice is spreading: In an attempt to open during the pandemic, many universities and colleges around the country are forcing students to download location-tracking apps, sometimes as a condition of enrollment. Many of these apps function via Bluetooth sensors or Wi-Fi networks. When students enter a classroom, their phone informs a sensor that’s been installed in the room, or the app checks the Wi-Fi networks nearby to determine the phone’s location.

As a university professor, I’ve seen surveillance like this before. Many of these apps replicate the tracking system sometimes installed on the phones of student athletes, for whom it is often mandatory. That system tells us a lot about what we can expect with these apps.

There is a widespread charade in the United States that university athletes, especially those who play high-profile sports such as football and basketball, are just students who happen to be playing sports as amateurs “in their free time.” The reality is that these college athletes in high-level sports, who are aggressively recruited by schools, bring prestige and financial resources to universities, under a regime that requires them to train like professional athletes despite their lack of salary. However, making the most of one’s college education and training at that level are virtually incompatible, simply because the day is 24 hours long and the body, even that of a young, healthy athlete, can only take so much when training so hard. Worse, many of these athletes are minority students, specifically Black men, who were underserved during their whole K–12 education and faced the same challenge then as they do now: Train hard in hopes of a scholarship and try to study with what little time is left, often despite being enrolled in schools with mediocre resources. Many of them arrive at college with an athletic scholarship but not enough academic preparation compared with their peers who went to better schools and could also concentrate on schooling….(More)”

Paper by O.B Leal-Neto et al: “Participatory surveillance has shown promising results from its conception to its application in several public health events. The use of a collaborative information pathway provides a rapid way for the data collection on symptomatic individuals in the territory, to complement traditional health surveillance systems. In Brazil, this methodology has been used at the national level since 2014 during mass gatherings events since they have great importance for monitoring public health emergencies.

With the occurrence of the COVID-19 pandemic, and the limitation of the main non-pharmaceutical interventions for epidemic control – in this case, testing and social isolation – added to the challenge of existing underreporting of cases and delay of notifications, there is a demand on alternative sources of up to date information to complement the current system for disease surveillance. Several studies have demonstrated the benefits of participatory surveillance in coping with COVID-19, reinforcing the opportunity to modernize the way health surveillance has been carried out. Additionally, spatial scanning techniques have been used to understand syndromic scenarios, investigate outbreaks, and analyze epidemiological risk, constituting relevant tools for health management. While there are limitations in the quality of traditional health systems, the data generated by participatory surveillance reveals an interesting application combining traditional techniques to clarify epidemiological risks that demand urgency in decision-making. Moreover, with the limitations of testing available, the identification of priority areas for intervention is an important activity in the early response to public health emergencies. This study aimed to describe and analyze priority areas for COVID-19 testing combining data from participatory surveillance and traditional surveillance for respiratory syndromes….(More)”.

Paper by Thibault Schrepel: “One may identify two current trends in the field of “Law and Technology.” The first trend concerns technological determinism. Some argue that technology is deterministic: the state of technological advancement is the determining factor of society. Others oppose that view, claiming it is the society that affects technology. The second trend concerns technological neutrality. some say that technology is neutral, meaning the effects of technology depend entirely on the social context. Others defend the opposite: they view the effects of technology as being inevitable (regardless of the society in which it is used).

While it is commonly accepted that technology is deterministic, I am under the impression that a majority of “Law and Technology” scholars also believe that technology is non-neutral. It follows that, according to this dominant view, (1) technology drives society in good or bad directions (determinism), and that (2) certain uses of technology may lead to the reduction or enhancement of the common good (non-neutrality). Consequently, this leads to top-down tech policies where the regulator has the impossible burden of helping society control and orient technology to the best possible extent.

This article is deterministic and non-neutral.

But, here’s the catch. Most of today’s doctrine focuses almost exclusively on the negativity brought by technology (read Nick Bostrom, Frank Pasquale, Evgeny Morozov). Sure, these authors mention a few positive aspects, but still end up focusing on the negative ones. They’re asking to constrain technology on that sole basis. With this article, I want to raise another point: technology determinism can also drive society by providing solutions to centuries-old problems. In and of itself. This is not technological solutionism, as I am not arguing that technology can solve all of mankind’s problems, but it is not anti-solutionism either. I fear the extremes, anyway.

To make my point, I will discuss the issue addressed by Albert Hirschman in his famous book Exit, Voice, and Loyalty (Harvard University Press, 1970). Hirschman, at the time Professor of Economics at Harvard University, introduces the distinction between “exit” and “voice.” With exit, an individual exhibits her or his disagreement as a member of a group by leaving the group. With voice, the individual stays in the group but expresses her or his dissatisfaction in the hope of changing its functioning. Hirschman summarizes his theory on page 121, with the understanding that the optimal situation for any individual is to be capable of both “exit” and “voice“….(More)”.

Paper by Scheel, Anne M., Leonid Tiokhin, Peder M. Isager, and Daniel Lakens: “For almost half a century, Paul Meehl educated psychologists about how the mindless use of null-hypothesis significance tests made research on theories in the social sciences basically uninterpretable (Meehl, 1990). In response to the replication crisis, reforms in psychology have focused on formalising procedures for testing hypotheses. These reforms were necessary and impactful. However, as an unexpected consequence, psychologists have begun to realise that they may not be ready to test hypotheses. Forcing researchers to prematurely test hypotheses before they have established a sound ‘derivation chain’ between test and theory is counterproductive. Instead, various non-confirmatory research activities should be used to obtain the inputs necessary to make hypothesis tests informative.

Before testing hypotheses, researchers should spend more time forming concepts, developing valid measures, establishing the causal relationships between concepts and their functional form, and identifying boundary conditions and auxiliary assumptions. Providing these inputs should be recognised and incentivised as a crucial goal in and of itself.

In this article, we discuss how shifting the focus to non-confirmatory research can tie together many loose ends of psychology’s reform movement and help us lay the foundation to develop strong, testable theories, as Paul Meehl urged us to….(More)”

Blog by Katherine Flaschen and Ben Castleman: “In order to create the most effective solutions, policymakers and educators need to better understand a fundamental question: Why do so many of these students, many of whom have already made substantial progress toward their degree, fail to return to college and graduate? …

With a better understanding of the barriers preventing people who intend to finish their degree from following through, policymakers and colleges can create solutions that meaningfully meet students’ needs and help them re-enroll. As states across the country face rising unemployment rates, it’s critical to design and test interventions that address these behavioral barriers and help thousands of citizens who are out of work due to the COVID-19 crisis consider their options for going back to school.

For example, colleges could provide monetary incentives to students for taking actions related to re-enrollment that overcome these barriers, such as speaking with an advisor, reviewing upcoming recommended courses and developing a course plan, and making an active choice about when to return to college. In addition, SCND students could be paired with current students to serve as peer mentors, both to provide support with the re-enrollment process and to hold them accountable for degree completion (especially if faced with difficult remaining classes). Community colleges could also encourage major employers of the SCND population in high-demand fields, like health care, to provide options for employees to finish their degree while working (e.g., via tuition reimbursement programs), translate degree attainment into concrete career returns, and identify representatives within the company, such as recent graduates, to promote re-enrollment and make it a more salient opportunity….(More)”.

Paper by Aline Blankertz: “A small number of large digital platforms increasingly shape the space for most online interactions around the globe and they often act with hardly any constraint from competing services. The lack of competition puts those platforms in a powerful position that may allow them to exploit consumers and offer them limited choice. Privacy is increasingly considered one area in which the lack of competition may create harm. Because of these concerns, governments and other institutions are developing proposals to expand the scope for competition authorities to intervene to limit the power of the large platforms and to revive competition.  

The first case that has explicitly addressed anticompetitive harm to privacy is the German Bundeskartellamt’s case against Facebook in which the authority argues that imposing bad privacy terms can amount to an abuse of dominance. Since that case started in 2016, more cases deal with the link between competition and privacy. For example, the proposed Google/Fitbit merger has raised concerns about sensitive health data being merged with existing Google profiles and Apple is under scrutiny for not sharing certain personal data while using it for its own services.

However, addressing bad privacy outcomes through competition policy is effective only if those outcomes are caused, at least partly, by a lack of competition. Six distinct mechanisms can be distinguished through which competition may affect privacy, as summarized in Table 1. These mechanisms constitute different hypotheses through which less competition may influence privacy outcomes and lead either to worse privacy in different ways (mechanisms 1-5) or even better privacy (mechanism 6). The table also summarizes the available evidence on whether and to what extent the hypothesized effects are present in actual markets….(More)”.

Karen Walker at Gov.UK: “Defence generates and holds a lot of data. We want to be able to get the best out of it, unlocking new insights that aren’t currently visible, through the use of innovative data science and analytics techniques tailored to defence’s specific needs. But this can be difficult because our data is often sensitive for a variety of reasons. For example, this might include information about the performance of particular vehicles, or personnel’s operational deployment details.

It is therefore often challenging to share data with experts who sit outside the Ministry of Defence, particularly amongst the wider data science community in government, small companies and academia. The use of synthetic data gives us a way to address this challenge and to benefit from the expertise of a wider range of people by creating datasets which aren’t sensitive. We have recently published a report from this work….(More)”.

Book edited by Maggie Walter, Tahu Kukutai, Stephanie Russo Carroll and Desi Rodriguez-Lonebear: “This book examines how Indigenous Peoples around the world are demanding greater data sovereignty, and challenging the ways in which governments have historically used Indigenous data to develop policies and programs.

In the digital age, governments are increasingly dependent on data and data analytics to inform their policies and decision-making. However, Indigenous Peoples have often been the unwilling targets of policy interventions and have had little say over the collection, use and application of data about them, their lands and cultures. At the heart of Indigenous Peoples’ demands for change are the enduring aspirations of self-determination over their institutions, resources, knowledge and information systems.

With contributors from Australia, Aotearoa New Zealand, North and South America and Europe, this book offers a rich account of the potential for Indigenous data sovereignty to support human flourishing and to protect against the ever-growing threats of data-related risks and harms….(More)”.

Book edited by Markus D. Dubber, Frank Pasquale, and Sunit Das: “This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches.

The term “A.I.” is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and ‘intelligence’, presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether “human” or “A.I.”…(More)”.