Inclusive Cyber Policy Making

Curated on Posted on by Stefaan Verhulst

Toolkit by Global Digital Partnership: “Marginalised perspectives, particularly from women and LGBTQ+ communities, are largely absent in current cyber norm discussions. This is a serious issue, as marginalised groups often face elevated and specific threats in cyberspace

Our bespoke toolkit provides policymakers and other stakeholders with a range of resources to address this lack of inclusion, including:

  • A how-to guide on developing an inclusive process to develop a cybernorm or implement existing agreed norms
  • An introduction to key terms and concepts relevant to inclusivity and cybernorms
  • Key questions for facilitating inclusive stakeholder mapping processes
  • A mapping of regional and global cybernorm processes…(More)”.

Cross-Border Data Policy Index

Curated on Posted on by Stefaan Verhulst

Report by the Global Data Alliance: “The ability to responsibly transfer data around the globe supports cross-border economic opportunity, cross-border technological and scientific progress, and cross-border digital transformation and inclusion, among other public policy objectives. To assess where policies have helped create an enabling environment for cross-border data and its associated benefits, the Global Data Alliance has developed the Cross-Border Data Policy Index.

The Cross-Border Data Policy Index offers a quantitative and qualitative assessment of the relative openness or restrictiveness of cross-border data policies across nearly 100 economies. Global economies are classified into four levels. At Level 1 are economies that impose relatively fewer limits on the cross-border access to knowledge, information, digital tools, and economic opportunity for their citizens and legal persons. Economies’ restrictiveness scores increase as they are found to impose greater limits on cross-border data, thereby eroding opportunities for digital transformation while also impeding other policy objectives relating to health, safety, security, and the environment…(More)”.

Patients are Pooling Data to Make Diabetes Research More Representative

Curated on Posted on by Stefaan Verhulst

Blog by Tracy Kariuki: “Saira Khan-Gallo knows how overwhelming managing and living healthily with diabetes can be. As a person living with type 1 diabetes for over two decades, she understands how tracking glucose levels, blood pressure, blood cholesterol, insulin intake, and, and, and…could all feel like drowning in an infinite pool of numbers.

But that doesn’t need to be the case. This is why Tidepool, a non-profit tech organization composed of caregivers and other people living with diabetes such as Gallo, is transforming diabetes data management. Its data visualization platform enables users to make sense of the data and derive insights into their health status….

Through its Big Data Donation Project, Tidepool has been supporting the advancement of diabetes research by sharing anonymized data from people living with diabetes with researchers.

To date, more than 40,000 individuals have chosen to donate data uploaded from their diabetes devices like blood glucose meters, insulin pumps and continuous glucose monitors, which is then shared by Tidepool with students, academics, researchers, and industry partners — Making the database larger than many clinical trials. For instance, Oregon Health and Science University have used datasets collected from Tidepool to build an algorithm that predicts hypoglycemia, which is low blood sugar, with the goal of advancing closed loop therapy for diabetes management…(More)”.

What prevents us from reusing medical real-world data in research

Curated on Posted on by Stefaan Verhulst

Paper by Julia Gehrmann, Edit Herczog, Stefan Decker & Oya Beyan: “Recent studies show that Medical Data Science (MDS) carries great potential to improve healthcare. Thereby, considering data from several medical areas and of different types, i.e. using multimodal data, significantly increases the quality of the research results. On the other hand, the inclusion of more features in an MDS analysis means that more medical cases are required to represent the full range of possible feature combinations in a quantity that would be sufficient for a meaningful analysis. Historically, data acquisition in medical research applies prospective data collection, e.g. in clinical studies. However, prospectively collecting the amount of data needed for advanced multimodal data analyses is not feasible for two reasons. Firstly, such a data collection process would cost an enormous amount of money. Secondly, it would take decades to generate enough data for longitudinal analyses, while the results are needed now. A worthwhile alternative is using real-world data (RWD) from clinical systems of e.g. university hospitals. This data is immediately accessible in large quantities, providing full flexibility in the choice of the analyzed research questions. However, when compared to prospectively curated data, medical RWD usually lacks quality due to the specificities of medical RWD outlined in section 2. The reduced quality makes its preparation for analysis more challenging…(More)”.

Data-driven research and healthcare: public trust, data governance and the NHS

Curated on Posted on by Stefaan Verhulst

Paper by Angeliki Kerasidou & Charalampia (Xaroula) Kerasidou: “It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced in recent years. In this paper, we examine the question of, what is it about sharing NHS data for research and innovation with for-profit companies that challenges public trust? To address this question, we draw from political theory to provide an account of public trust that helps better understand the relationship between the public and the NHS within a democratic context, as well as, the kind of obligations and expectations that govern this relationship. Then we examine whether the way in which the NHS is managing patient data and its collaboration with the private sector fit under this trust-based relationship. We argue that the datafication of healthcare and the broader ‘health and wealth’ agenda adopted by consecutive UK governments represent a major shift in the institutional character of the NHS, which brings into question the meaning of public good the NHS is expected to provide, challenging public trust. We conclude by suggesting that to address the problem of public trust, a theoretical and empirical examination of the benefits but also the costs associated with this shift needs to take place, as well as an open conversation at public level to determine what values should be promoted by a public institution like the NHS….(More)”.

Setting data free: The politics of open data for food and agriculture

Curated on Posted on by Stefaan Verhulst

Paper by M. Fairbairn, and Z. Kish: “Open data is increasingly being promoted as a route to achieve food security and agricultural development. This article critically examines the promotion of open agri-food data for development through a document-based case study of the Global Open Data for Agriculture and Nutrition (GODAN) initiative as well as through interviews with open data practitioners and participant observation at open data events. While the concept of openness is striking for its ideological flexibility, we argue that GODAN propagates an anti-political, neoliberal vision for how open data can enhance agricultural development. This approach centers values such as private innovation, increased production, efficiency, and individual empowerment, in contrast to more political and collectivist approaches to openness practiced by some agri-food social movements. We further argue that open agri-food data projects, in general, have a tendency to reproduce elements of “data colonialism,” extracting data with minimal consideration for the collective harms that may result, and embedding their own values within universalizing information infrastructures…(More)”.

Unleashing the power of data for electric vehicles and charging infrastructure

Curated on Posted on by Stefaan Verhulst

Report by Thomas Deloison: “As the world moves toward widespread electric vehicle (EV) adoption, a key challenge lies ahead: deploying charging infrastructure rapidly and effectively. Solving this challenge will be essential to decarbonize transport, which has a higher reliance on fossil fuels than any other sector and accounts for a fifth of global carbon emissions. However, the companies and governments investing in charging infrastructure face significant hurdles, including high initial capital costs and difficulties related to infrastructure planning, permitting, grid connections and grid capacity development.

Data has the power to facilitate these processes: increased predictability and optimized planning and infrastructure management go a long way in easing investments and accelerating deployment. Last year, members of the World Business Council for Sustainable Development (WBCSD) demonstrated that digital solutions based on data sharing could reduce carbon emissions from charging by 15% and unlock crucial grid capacity and capital efficiency gains.

Exceptional advances in data, analytics and connectivity are making digital solutions a potent tool to plan and manage transport, energy and infrastructure. Thanks to the deployment of sensors and the rise of connectivity,  businesses are collecting information faster than ever before, allowing for data flows between physical assets. Charging infrastructure operators, automotive companies, fleet operators, energy providers, building managers and governments collect insights on all aspects of electric vehicle charging infrastructure (EVCI), from planning and design to charging experiences at the station.

The real value of data lies in its aggregation. This will require breaking down siloes across industries and enabling digital collaboration. A digital action framework released by WBCSD, in collaboration with Arcadis, Fujitsu and other member companies and partners, introduces a set of recommendations for companies and governments to realize the full potential of digital solutions and accelerate EVCI deployments:

  • Map proprietary data, knowledge gaps and digital capacity across the value chain to identify possible synergies. The highest value potential from digital solutions will lie at the nexus of infrastructure, consumer behavior insights, grid capacity and transport policy. For example, to ensure the deployment of charging stations where they will be most needed and at the right capacity level, it is crucial to plan investments within energy grid capacity, spatial constraints and local projected demand for EVs.
  • Develop internal data collection and storage capacity with due consideration for existing structures for data sharing. A variety of schemes allow actors to engage in data sharing or monetization. Yet, their use is limited by mismatched use of data standards and specification and process uncertainty. Companies must build a strong understanding of these structures internally by providing internal training and guidance, and invest in sound data collection, storage and analysis capacity.
  • Foster a policy environment that supports digital collaboration across sectors and industries. Digital policies must provide incentives and due diligence frameworks to guide data exchanges across industries and support the adoption of common standards and protocols. For instance, it will be crucial to integrate linkages with energy systems and infrastructure beyond roads in the rollout of the European mobility data space…(More)”.

Russia Is Trying to Leave the Internet and Build Its Own

Curated on Posted on by Stefaan Verhulst

Article by Timmy Broderick: “Last week the Russian government tried to disconnect its Internet infrastructure from the larger global Web. This test of Russia’s “sovereign Internet” seemingly failed, causing outages that suggest the system is not ready for practical use.

“Sovereign Internet is not really a whole different Internet; it is more like a project that uses various tools,” says Natalia Krapiva, tech-legal counsel at the international digital-rights nonprofit Access Now. “It involves technology like deep packet inspection, which allows major filtering of the Internet and gives governments the ability to throttle certain connections and websites.” By cutting off access to sites such as Western social media platforms, the Russian government could restrict residents from viewing any source of information other than the country’s accepted channels of influence.

This method of curtailing digital freedom goes beyond Russia: other countries are also attempting to develop their own nationwide Internet. And if successful, these endeavors could fragment the World Wide Web. Scientific American talked with Krapiva over Zoom about the implications of this latest test, the motive behind Russia’s actions and the ways the push for a sovereign Internet affect the digital rights of all users…(More)”.

Just Citation

Curated on Posted on by Stefaan Verhulst

Paper by Amanda Levendowski: “Contemporary citation practices are often unjust. Data cartels, like Google, Westlaw, and Lexis, prioritize profits and efficiency in ways that threaten people’s autonomy, particularly that of pregnant people and immigrants. Women and people of color have been legal scholars for more than a century, yet colleagues consistently under-cite and under-acknowledge their work. Other citations frequently lead to materials that cannot be accessed by disabled people, poor people or the public due to design, paywalls or link rot. Yet scholars and students often understand citation practices as “just” citation and perpetuate these practices unknowingly. This Article is an intervention. Using an intersectional feminist framework for understanding how cyberlaws oppress and liberate oppressed, an emerging movement known as feminist cyberlaw, this Article investigates problems posed by prevailing citation practices and introduces practical methods that bring citation into closer alignment with the feminist values of safety, equity, and accessibility. Escaping data cartels, engaging marginalized scholars, embracing free and public resources, and ensuring that those resources remain easily available represent small, radical shifts that promote just citation. This Article provides powerful, practical tools for pursuing all of them…(More)”.

Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology

Curated on Posted on by Stefaan Verhulst

Paper by Nikhil Agarwal, Alex Moehring, Pranav Rajpurkar & Tobias Salz: “While Artificial Intelligence (AI) algorithms have achieved performance levels comparable to human experts on various predictive tasks, human experts can still access valuable contextual information not yet incorporated into AI predictions. Humans assisted by AI predictions could outperform both human-alone or AI-alone. We conduct an experiment with professional radiologists that varies the availability of AI assistance and contextual information to study the effectiveness of human-AI collaboration and to investigate how to optimize it. Our findings reveal that (i) providing AI predictions does not uniformly increase diagnostic quality, and (ii) providing contextual information does increase quality. Radiologists do not fully capitalize on the potential gains from AI assistance because of large deviations from the benchmark Bayesian model with correct belief updating. The observed errors in belief updating can be explained by radiologists’ partially underweighting the AI’s information relative to their own and not accounting for the correlation between their own information and AI predictions. In light of these biases, we design a collaborative system between radiologists and AI. Our results demonstrate that, unless the documented mistakes can be corrected, the optimal solution involves assigning cases either to humans or to AI, but rarely to a human assisted by AI…(More)”.