Paper by Alissa Brauneck et al : “In recent years, interest in machine learning (ML) as well as in multi-institutional collaborations has grown, especially in the medical field. However, strict application of data-protection laws reduces the size of training datasets, hurts the performance of ML systems and, in the worst case, can prevent the implementation of research insights in clinical practice. Federated learning can help overcome this bottleneck through decentralised training of ML models within the local data environment, while maintaining the predictive performance of ‘classical’ ML. Thus, federated learning provides immense benefits for cross-institutional collaboration by avoiding the sharing of sensitive personal data(Fig. 1; refs.). Because existing regulations (especially the General Data Protection Regulation 2016/679 of the European Union, or GDPR) set stringent requirements for medical data and rather vague rules for ML systems, researchers are faced with uncertainty. In this comment, we provide recommendations for researchers who intend to use federated learning, a privacy-preserving ML technique, in their research. We also point to areas where regulations are lacking, discussing some fundamental conceptual problems with ML regulation through the GDPR, related especially to notions of transparency, fairness and error-free data. We then provide an outlook on how implications from data-protection laws can be directly incorporated into federated learning tools…(More)”.
Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France
Paper by Rahul Goel, Angelo Furno, and Rajesh Sharma: “Socio-economic indicators provide context for assessing a country’s overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is critical for social research and government policing. Most data sources available today, such as censuses, have sparse population coverage or are updated infrequently. Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators.
This work investigates mobile app data to predict socio-economic features. We present a large-scale study using data that captures the traffic of thousands of mobile applications by approximately 30 million users distributed over 550,000 km square and served by over 25,000 base stations. The dataset covers the whole France territory and spans more than 2.5 months, starting from 16th March 2019 to 6th June 2019. Using the app usage patterns, our best model can estimate socio-economic indicators (attaining an R-squared score upto 0.66). Furthermore, using models’ explainability, we discover that mobile app usage patterns have the potential to reveal socio-economic disparities in IRIS. Insights of this study provide several avenues for future interventions, including users’ temporal network analysis and exploration of alternative data sources…(More)”.
Global Renewables Watch
About: “The Global Renewables Watch is a first-of-its-kind living atlas intended to map and measure all utility-scale solar and wind installations on Earth using artificial intelligence (AI) and satellite imagery, allowing users to evaluate clean energy transition progress and track trends over time. It also provides unique spatial data on land use trends to help achieve the dual aims of the environmental protection and increasing renewable energy capacity….(More)”
Who owns the map? Data sovereignty and government spatial data collection, use, and dissemination
Paper by Peter A. Johnson and Teresa Scassa: “Maps, created through the collection, assembly, and analysis of spatial data are used to support government planning and decision-making. Traditionally, spatial data used to create maps are collected, controlled, and disseminated by government, although over time, this role has shifted. This shift has been driven by the availability of alternate sources of data collected by private sector companies, and data contributed by volunteers to open mapping platforms, such as OpenStreetMap. In theorizing this shift, we provide examples of how governments use data sovereignty as a tool to shape spatial data collection, use, and sharing. We frame four models of how governments may navigate shifting spatial data sovereignty regimes; first, with government retaining complete control over data collection; second, with government contracting a third party to provide specific data collection services, but with data ownership and dissemination responsibilities resting with government; third, with government purchasing data under terms of access set by third party data collectors, who disseminate data to several parties, and finally, with government retreating from or relinquishing data sovereignty altogether. Within this rapidly changing landscape of data providers, we propose that governments must consider how to address data sovereignty concerns to retain their ability to control data use in the public interest…(More)”.
Data Free Flow with Trust: Overcoming Barriers to Cross-Border Data Flows
Briefing Paper by the WEF: “The movement of data across country borders is essential to the global economy. When data flows across borders, it is possible to deliver more to more people and produce more benefits for people and planet. This briefing paper highlights the importance of such data flows and urges global leaders in the public and private sectors to take collective action to work towards a shared understanding of them with a view to implementing “Data Free Flow with Trust” (DFFT) – an umbrella concept for facilitating trust-based data exchanges. This paper reviews the current challenges facing DFFT, take stock of progress made so far, offer direction for policy mechanisms and concrete tools for businesses and, more importantly, promote global discussions about how to realize DFFT from the perspectives of policy and business…(More)”.
Civic Switchboard: Connecting Libraries and Community Information Networks
“Civic Switchboard is an Institute of Museum and Library Services supported effort that aims to develop the capacity of academic and public libraries in civic data ecosystems.
We encourage partnerships between libraries and local data intermediaries that will better serve data users, further democratize data, and support equitable access to information. Our project is created an online guide and toolkit for libraries interested in expanding (or beginning) their role around civic information…(More)”.
Why we need to unlock health data to beat disease worldwide
Article by Takanori Fujita, Masayasu Okajima and Hiroyuki Miuchi: “The digital revolution in healthcare offers the promise of better health and longer lives for people around the world. New digital tools can help doctors and patients to predict, prevent and treat disease, opening the door to personalised medical care that is cost-efficient and highly effective.
Digitization across the entire healthcare sector — from hospital operations to the production of medical devices, vaccines and other pharmaceuticals — stands to benefit everyone, through improved efficiency at medical institutions, better care at home and stronger support for everyday health and wellbeing.
The essential ingredient in digital healthcare is data. Developers and service providers need health data to build and deploy effective solutions. So far, unfortunately, the potential benefits of digital healthcare have been under-realized, in large part because of data chokepoints…
It should go without saying that the ‘reward’ for sharing data is better health. Lifestyle-related diseases, which are more prevalent in ageing populations, often do not become symptomatic until they have progressed to a dangerous level. That makes timely monitoring and assessment crucial. In a world where people are living longer and longer— ‘100-year societies,’ as we say in Japan — data-enabled early detection is perhaps the best tool we have to stave off age-related health crises.
Abstract arguments, however, rarely convince people to consent to sharing personal data. Special efforts are needed to show specific, individual benefits and make people feel a tangible sense of control.
In Japan, the city of Arao is conducting an experiment to enable patients and their families to check information on electronic health records (EHRs) using their smartphones when they visit affiliated hospitals. Test results, prescribed medications and other information can be monitored. The system is expected to reduce costs for municipalities that are struggling to fund medical and nursing care for growing elderly populations. The money saved can be diverted to programs that help people live healthier lives, creating a virtuous cycle….Digital healthcare isn’t just a matter for patients and medical professionals. Lifestyle data with implications for health is broadly distributed, so the non-medical field needs to be involved as well. Takamatsu, another Japanese city, is attempting to address this difficult issue by building a common data collaboration infrastructure for the public and private sectors.
SOMPO Light Vortex, a subsidiary of SOMPO Holdings, a Japanese insurance and nursing care company, has created an app for Covid-19 vaccination certification and personal health records (PHRs) that is connected to Takamatsu’s municipal data infrastructure. Combining a range of data on health and lifestyle patterns in a trusted platform overseen by local government is expected to offer benefits in areas ranging from disaster prevention to wellbeing…(More)”.
Measuring the value of data and data flows
Report by the OECD: “Data have become a key input into the production of many goods and services. But just how important? What is the value of data – their contribution to economic growth and well-being? This report discusses different approaches to data valuation, their advantages and shortcomings and their applicability in different contexts. It argues that the value of data depends to a large extent on the data governance framework determining how they can be created, shared and used. In addition, the report provides estimates of the value of data and data flows. Its focus is on the monetary valuation of data produced by private economic actors and their recording in economic statistics. Finally, the report puts forward a draft measurement agenda for the future…(More)”.
Filling Public Data Gaps
Report by Judah Axelrod, Karolina Ramos, and Rebecca Bullied: “Data are central to understanding the lived experiences of different people and communities and can serve as a powerful force for promoting racial equity. Although public data, including foundational sources for policymaking such as the US Census Bureau’s American Community Survey (ACS), offer accessible information on a range of topics, challenges of timeliness, granularity, representativeness, and degrees of disaggregation can limit those data’s utility for real-time analysis. Private data—data produced by private-sector organizations either through standard business or to market as an asset for purchase—can serve as a richer, more granular, and higher-frequency supplement or alternative to public data sources. This raises questions about how well private data assets can offer race-disaggregated insights that can inform policymaking.
In this report, we explore the current landscape of public-private data sharing partnerships that address topic areas where racial equity research faces data gaps: wealth and assets, financial well-being and income, and employment and job quality. We held 20 semistructured interviews with current producers and users of private-sector data and subject matter experts in the areas of data-sharing models and ethical data usage. Our findings are divided into five key themes:
- Incentives and disincentives, benefits, and risks to public-private data sharing
Agreements with prestigious public partners can bolster credibility for private firms and broaden their customer base, while public partners benefit from access to real-time, granular, rich data sources. But data sharing is often time and labor intensive, and firms can be concerned with conflicting business interests or diluting the value of proprietary data assets. - Availability of race-disaggregated data sources
We found no examples in our interviews of race-disaggregated data sources related to our thematic focus areas that are available externally. However, there are promising methods for data imputation, linkage, and augmentation through internal surveys. - Data collaboratives in practice
Most public-private data sharing agreements we learned about are between two parties and entail free or “freemium” access. However, we found promising examples of multilateral agreements that diversify the data-sharing landscape. - From data champions to data stewards
We found many examples of informal data champions who bear responsibility for relationship-building and securing data partnerships. This role has yet to mature to an institutionalized data steward within private firms we interviewed, which can make data sharing a fickle process. - Considerations for ethical data usage
Data privacy and transparency about how data are accessed and used are prominent concerns among prospective data users. Interviewees also stressed the importance of not privileging existing quantitative data above qualitative insights in cases where communities have offered long-standing feedback and narratives about their own experiences facing racial inequities, and that policymakers should not use a need to collect more data as an excuse for delaying policy action.
Our research yielded several recommendations for data producers and users that engage in data sharing, and for funders seeking to advance data-sharing efforts and promote racial equity…(More)”
We need data infrastructure as well as data sharing – conflicts of interest in video game research
Article by David Zendle & Heather Wardle: “Industry data sharing has the potential to revolutionise evidence on video gaming and mental health, as well as a host of other critical topics. However, collaborative data sharing agreements between academics and industry partners may also afford industry enormous power in steering the development of this evidence base. In this paper, we outline how nonfinancial conflicts of interest may emerge when industry share data with academics. We then go on to describe ways in which such conflicts may affect the quality of the evidence base. Finally, we suggest strategies for mitigating this impact and preserving research independence. We focus on the development of data infrastructure: technological, social, and educational architecture that facilitates unfettered and free access to the kinds of high-quality data that industry hold, but without industry involvement…(More)”.