Paper by Victoria Neumann et al: “In recent years, researchers have begun to explore the use of Distributed Ledger Technologies (DLT), also known as blockchain, in health data sharing contexts. However, there is a significant lack of research that examines public attitudes towards the use of this technology. In this paper, we begin to address this issue and present results from a series of focus groups which explored public views and concerns about engaging with new models of personal health data sharing in the UK. We found that participants were broadly in favour of a shift towards new decentralised models of data sharing. Retaining ‘proof’ of health information stored about patients and the capacity to provide permanent audit trails, enabled by immutable and transparent properties of DLT, were regarded as particularly valuable for our participants and prospective data custodians. Participants also identified other potential benefits such as supporting people to become more health data literate and enabling patients to make informed decisions about how their data was shared and with whom. However, participants also voiced concerns about the potential to further exacerbate existing health and digital inequalities. Participants were also apprehensive about the removal of intermediaries in the design of personal health informatics systems…(More)”.
Why Does Open Data Get Underused? A Focus on the Role of (Open) Data Literacy
Paper by Gema Santos-Hermosa et al: “Open data has been conceptualised as a strategic form of public knowledge. Tightly connected with the developments in open government and open science, the main claim is that access to open data (OD) might be a catalyser of social innovation and citizen empowerment. Nevertheless, the so-called (open) data divide, as a problem connected to the situation of OD usage and engagement, is a concern.
In this chapter, we introduce the OD usage trends, focusing on the role played by (open) data literacy amongst either users or producers: citizens, professionals, and researchers. Indeed, we attempted to cover the problem of OD through a holistic approach including two areas of research and practice: open government data (OGD) and open research data (ORD). After uncovering several factors blocking OD consumption, we point out that more OD is being published (albeit with low usage), and we overview the research on data literacy. While the intentions of stakeholders are driven by many motivations, the abilities that put them in the condition to enhance OD might require further attention. In the end, we focus on several lifelong learning activities supporting open data literacy, uncovering the challenges ahead to unleash the power of OD in society…(More)”.
Innovation Power: Why Technology Will Define the Future of Geopolitics
Essay by Eric Schmidt: “When Russian forces marched on Kyiv in February 2022, few thought Ukraine could survive. Russia had more than twice as many soldiers as Ukraine. Its military budget was more than ten times as large. The U.S. intelligence community estimated that Kyiv would fall within one to two weeks at most.
Outgunned and outmanned, Ukraine turned to one area in which it held an advantage over the enemy: technology. Shortly after the invasion, the Ukrainian government uploaded all its critical data to the cloud, so that it could safeguard information and keep functioning even if Russian missiles turned its ministerial offices into rubble. The country’s Ministry of Digital Transformation, which Ukrainian President Volodymyr Zelensky had established just two years earlier, repurposed its e-government mobile app, Diia, for open-source intelligence collection, so that citizens could upload photos and videos of enemy military units. With their communications infrastructure in jeopardy, the Ukrainians turned to Starlink satellites and ground stations provided by SpaceX to stay connected. When Russia sent Iranian-made drones across the border, Ukraine acquired its own drones specially designed to intercept their attacks—while its military learned how to use unfamiliar weapons supplied by Western allies. In the cat-and-mouse game of innovation, Ukraine simply proved nimbler. And so what Russia had imagined would be a quick and easy invasion has turned out to be anything but.
Ukraine’s success can be credited in part to the resolve of the Ukrainian people, the weakness of the Russian military, and the strength of Western support. But it also owes to the defining new force of international politics: innovation power. Innovation power is the ability to invent, adopt, and adapt new technologies. It contributes to both hard and soft power. High-tech weapons systems increase military might, new platforms and the standards that govern them provide economic leverage, and cutting-edge research and technologies enhance global appeal. There is a long tradition of states harnessing innovation to project power abroad, but what has changed is the self-perpetuating nature of scientific advances. Developments in artificial intelligence in particular not only unlock new areas of scientific discovery; they also speed up that very process. Artificial intelligence supercharges the ability of scientists and engineers to discover ever more powerful technologies, fostering advances in artificial intelligence itself as well as in other fields—and reshaping the world in the process…(More)”.
Ten lessons for data sharing with a data commons
Article by Robert L. Grossman: “..Lesson 1. Build a commons for a specific community with a specific set of research challenges
Although there are a few data repositories that serve the general scientific community that have proved successful, in general data commons that target a specific user community have proven to be the most successful. The first lesson is to build a data commons for a specific research community that is struggling to answer specific research challenges with data. As a consequence, a data commons is a partnership between the data scientists developing and supporting the commons and the disciplinary scientists with the research challenges.
Lesson 2. Successful commons curate and harmonize the data
Successful commons curate and harmonize the data and produce data products of broad interest to the community. It’s time consuming, expensive, and labor intensive to curate and harmonize data, by much of the value of data commons is centralizing this work so that it can be done once instead of many times by each group that needs the data. These days, it is very easy to think of a data commons as a platform containing data, not spend the time curating or harmonizing it, and then be surprised that the data in the commons is not used more widely used and its impact is not as high as expected.
Lesson 3. It’s ultimately about the data and its value to generate new research discoveries
Despite the importance of a study, few scientists will try to replicate previously published studies. Instead, data is usually accessed if it can lead to a new high impact paper. For this reason, data commons play two different but related roles. First, they preserve data for reproducible science. This is a small fraction of the data access, but plays a critical role in reproducible science. Second, data commons make data available for new high value science.
Lesson 4. Reduce barriers to access to increase usage
A useful rule of thumb is that every barrier to data access cuts down access by a factor of 10. Common barriers that reduce use of a commons include: registration vs no-registration; open access vs controlled access; click through agreements vs signing of data usage agreements and approval by data access committees; license restrictions on the use of the data vs no license restrictions…(More)”.
‘Neurorights’ and the next flashpoint of medical privacy
Article by Alex LaCasse: “Around the world, leading neuroscientists, neuroethicists, privacy advocates and legal minds are taking greater interest in brain data and its potential.
Opinions vary widely on the long-term advancements in technology designed to measure brain activity and their impacts on society, as new products trickle out of clinical settings and gain traction for commercial applications.
Some say alarm bells should already be sounding and argue the technology could have corrosive effects on democratic society. Others counter such claims are hyperbolic, given the uncertainty that technology can even measure certain brain activities in the purported way.
Today, neurotechnology is primarily confined to medical and research settings, with the use of various clinical-grade devices to monitor the brain activity of patients who may suffer from mental illnesses or paralysis to gauge muscle movement and record electroencephalography (the measurement of electrical activity and motor function in the brain)….
“I intentionally don’t call this neurorights or brain rights. I call it cognitive liberty,” Duke University Law and Philosophy Professor Nita Farahany said during a LinkedIn Live session. “There is promise of this technology, not only for people who are struggling with a loss of speech and loss of motor activity, but for everyday people.”
The jumping-off point of the panel centered around Farahany’s new book, “The Battle for Your Brain: The Ability to Think Freely in the Age of Neurotechnology,” which examines the neurotechnology landscape and potential negative outcomes without regulatory oversight.
Farahany was motivated to write the book because she saw a “chasm” between what she thought neurotechnology was capable of and the reality of some companies working to one day decode people’s inner thoughts on some level…(More)” (Book)”.
Mapping and Comparing Data Governance Frameworks: A benchmarking exercise to inform global data governance deliberations
Paper by Sara Marcucci, Natalia Gonzalez Alarcon, Stefaan G. Verhulst, Elena Wullhorst: “Data has become a critical resource for organizations and society. Yet, it is not always as valuable as it could be since there is no well-defined approach to managing and using it. This article explores the increasing importance of global data governance due to the rapid growth of data and the need for responsible data use and protection. While historically associated with private organizational governance, data governance has evolved to include governmental and institutional bodies. However, the lack of a global consensus and fragmentation in policies and practices pose challenges to the development of a common framework. The purpose of this report is to compare approaches and identify patterns in the emergent and fragmented data governance ecosystem within sectors close to the international development field, ultimately presenting key takeaways and reflections on when and why a global data governance framework may be needed. Overall, the report highlights the need for a more holistic, coordinated transnational approach to data governance to manage the global flow of data responsibly and for the public interest. The article begins by giving an overview of the current fragmented data governance ecology, to then proceed to illustrate the methodology used. Subsequently, the paper illustrates the most relevant findings stemming from the research. These are organized according to six key elements: (a) purpose, (b) principles, (c) anchoring documents, (d) data description and lifecycle, (e) processes, and (f) practices. Finally, the article closes with a series of key takeaways and final reflections…(More)”.
Big data for whom? Data-driven estimates to prioritize the recovery needs of vulnerable populations after a disaster
Blog and paper by Sabine Loos and David Lallemant: “For years, international agencies have been effusing the benefits of big data for sustainable development. Emerging technology–such as crowdsourcing, satellite imagery, and machine learning–have the power to better inform decision-making, especially those that support the 17 Sustainable Development Goals. When a disaster occurs, overwhelming amounts of big data from emerging technology are produced with the intention to support disaster responders. We are seeing this now with the recent earthquakes in Turkey and Syria: space agencies are processing satellite imagery to map faults and building damage or digital humanitarians are crowdsourcing baseline data like roads and buildings.
Eight years ago, the Nepal 2015 earthquake was no exception–emergency managers received maps of shaking or crowdsourced maps of affected people’s needs from diverse sources. A year later, I began research with a team of folks involved during the response to the earthquake, and I was determined to understand how big data produced after disasters were connected to the long-term effects of the earthquake. Our research team found that a lot of data that was used to guide the recovery focused on building damage, which was often viewed as a proxy for population needs. While building damage information is useful, it does not capture the full array of social, environmental, and physical factors that will lead to disparities in long-term recovery. I assumed information would have been available immediately after the earthquake that was aimed at supporting vulnerable populations. However, as I spent time in Nepal during the years after the 2015 earthquake, speaking with government officials and nongovernmental organizations involved in the response and recovery, I found they lacked key information about the needs of the most vulnerable households–those who would face the greatest obstacles during the recovery from the earthquake. While governmental and nongovernmental actors prioritized the needs of vulnerable households as best as possible with the information available, I was inspired to pursue research that could provide better information more quickly after an earthquake, to inform recovery efforts.
In our paper published in Communications Earth and Environment [link], we develop a data-driven approach to rapidly estimate which areas are likely to fall behind during recovery due to physical, environmental, and social obstacles. This approach leverages survey data on recovery progress combined with geospatial datasets that would be readily available after an event that represent factors expected to impede recovery. To identify communities with disproportionate needs long after a disaster, we propose focusing on those who fall behind in recovery over time, or non-recovery. We focus on non-recovery since it places attention on those who do not recover rather than delineating the characteristics of successful recovery. In addition, in speaking to several groups in Nepal involved in the recovery, they understood vulnerability–a concept that is place-based and can change over time–as those who would not be able to recover due to the earthquake…(More)”
Organizing for Collective Action: Olson Revisited
Paper by Marco Battaglini & Thomas R. Palfrey: “We study a standard collective action problem in which successful achievement of a group interest requires costly participation by some fraction of its members. How should we model the internal organization of these groups when there is asymmetric information about the preferences of their members? How effective should we expect it to be as we increase the group’s size n? We model it as an optimal honest and obedient communication mechanism and we show that for large n it can be implemented with a very simple mechanism that we call the Voluntary Based Organization. Two new results emerge from this analysis. Independently of the assumptions on the underlying technology, the limit probability of success in the best honest and obedient mechanism is the same as in an unorganized group, a result that is not generally true if obedience is omitted. An optimal organization, however, provides a key advantage: when the probability of success converges to zero, it does so at a much slower rate than in an unorganized group. Because of this, significant probabilities of success are achievable with simple honest and obedient organizations even in very large groups…(More)”.
The Meta Oversight Board’s First Term
Paper by Evelyn Douek: “The Meta Oversight Board was established to oversee one of the most expansive systems of speech regulation in history and to exercise independent review over “some of the most difficult and significant
content decisions” Meta makes. As a voluntary exercise in selfregulation, the Board exercises power over Meta only insofar and for as long as Meta permits it to. And yet, in its inaugural members’ first threeyear term, the Board has in many ways defied its skeptics. The Board has established itself as a regular part of conversations about content moderation governance, receiving significant academic and media attention. It has also instantiated meaningful reforms of Meta’s content moderation systems, and shed light on otherwise completely opaque decisionmaking processes within one of the world’s most powerful
speech regulators. But the Board has also consistently shied away from answering the hardest and most controversial questions that come before it—that is, the very questions it was set up to solve. Although the Board purported to evaluate Meta’s rules under international human rights law, it has almost entirely failed to engage with the necessary the normative question of how international law principles created to constrain governmental power over expression should apply to private content moderation systems. This Essay argues that the Board’s institutional incentives and desire for influence have made it prioritize consensus and simplicity over engagement with the fundamental normative questions that the quest for principled content moderation decisionmaking raises. The result is a tremendous missed opportunity that holds important lessons for the design of future content moderation oversight bodies…(More)”
Satellite data: The other type of smartphone data you might not know about
Article by Tommy Cooke et al: “Smartphones determine your location in several ways. The first way involves phones triangulating distances between cell towers or Wi-Fi routers.
The second way involves smartphones interacting with navigation satellites. When satellites pass overhead, they transmit signals to smartphones, which allows smartphones to calculate their own location. This process uses a specialized piece of hardware called the Global Navigation Satellite System (GNSS) chipset. Every smartphone has one.
When these GNSS chipsets calculate navigation satellite signals, they output data in two standardized formats (known as protocols or languages): the GNSS raw measurement protocol and the National Marine Electronics Association protocol (NMEA 0183).
GNSS raw measurements include data such as the distance between satellites and cellphones and measurements of the signal itself.
NMEA 0183 contains similar information to GNSS raw measurements, but also includes additional information such as satellite identification numbers, the number of satellites in a constellation, what country owns a satellite, and the position of a satellite.
NMEA 0183 was created and is governed by the NMEA, a not-for-profit lobby group that is also a marine electronics trade organization. The NMEA was formed at the 1957 New York Boat Show when boating equipment manufacturers decided to build stronger relationships within the electronic manufacturing industry.
In the decades since, the NMEA 0183 data standard has improved marine electronics communications and is now found on a wide variety of non-marine communications devices today, including smartphones…
It is difficult to know who has access to data produced by these protocols. Access to NMEA protocols is only available under licence to businesses for a fee.
GNSS raw measurements, on the other hand, are a universal standard and can be read by different devices in the same way without a license. In 2016, Google allowed industries to have open access to it to foster innovation around device tracking accuracy, precision, analytics about how we move in real-time, and predictions about our movements in the future.
While automated processes can quietly harvest location data — like when a French-based company extracted location data from Salaat First, a Muslim prayer app — these data don’t need to be taken directly from smartphones to be exploited.
Data can be modelled, experimented with, or emulated in licensed devices in labs for innovation and algorithmic development.
Satellite-driven raw measurements from our devices were used to power global surveillance networks like STRIKE3, a now defunct European-led initiative that monitored and reported perceived threats to navigation satellites…(More)”.