How credit unions could help people make the most of personal data

Curated on Posted on by Stefaan Verhulst

Dylan Walsh at MIT Sloan: “In May of 2018, the EU adopted the General Data Protection Regulation, referred to by The New York Timesas “the world’s toughest rules to protect people’s online data.” Among its many safeguards, the GDPR gave individuals ownership of their personal data and thereby restricted its collection and use by businesses.

“That’s a good first start,” said Alex Pentland, a co-creator of the MIT Media Lab who played a foundational role in the development of the GDPR. “But ownership isn’t enough. Simply having the rights to your data doesn’t allow you to do much with it.” In response to this shortcoming, Pentland and his team have proposed the establishment of data cooperatives.

The idea is conceptually straightforward: Individuals would pool their personal data in a single institution — just as they pool money in banks — and that institution would both protect the data and put it to use. Pentland and his team suggest credit unions as one type of organization that could fill this role. And while companies would need to request permission to use consumer data, consumers themselves could request analytic insights from the cooperative. Lyft drivers, for instance, might compare their respective incomes across routes, and ride-share passengers could compare how much they pay relative to other cooperative members….

Several states have now asked credit unions to look into the idea of data cooperatives, but the model has yet to gain a foothold. “Credit unions are conservative,” Pentland said. But assuming the idea gains traction, the infrastructure won’t be difficult to build. Technology exists to automatically record and organize all the data that we give to companies; and credit unions, which have 100 million members nationwide, possess charters readymade to take on data management….(More)”.

Mobile phone data’s potential for informing infrastructure planning in developing countries

Curated on Posted on by Stefaan Verhulst

Paper by Hadrien Salat, Zbigniew Smoreda, and Markus Schläpfer: “High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a go to proxy to evaluate population density, activity and social characteristics. They offer additional advantages for infrastructure planning such as being updated in real-time, including mobility information and recording temporary visitors’ activity. We combine various data sets from Senegal to evaluate mobile phone data’s potential to replace insufficient census data for infrastructure planning in developing countries. As an applied case, we test their ability at predicting accurately domestic electricity consumption. We show that, contrary to common belief, average mobile phone activity is not well correlated with population density. However, it can provide better electricity consumption estimates than basic census data. More importantly, we successfully use curve and network clustering techniques to enhance the accuracy of the predictions, to recover good population mapping potential and to reduce the collection of informative data for planning to substantially smaller samples….(More)”.

Sharing data can help prevent public health emergencies in Africa

Curated on Posted on by Stefaan Verhulst

Moses John Bockarie at The Conversation: “Global collaboration and sharing data on public health emergencies is important to fight the spread of infectious diseases. If scientists and health workers can openly share their data across regions and organisations, countries can be better prepared and respond faster to disease outbreaks.

This was the case in with the 2014 Ebola outbreak in West Africa. Close to 100 scientists, clinicians, health workers and data analysts from around the world worked together to help contain the spread of the disease.

But there’s a lack of trust when it comes to sharing data in north-south collaborations. African researchers are suspicious that their northern partners could publish data without acknowledging the input from the less resourced southern institutions where the data was first generated. Until recently, the authorship of key scientific publications, based on collaborative work in Africa, was dominated by scientists from outside Africa.

The Global Research Collaboration for Infectious Disease Preparedness, an international network of major research funding organisations, recently published a roadmap to data sharing. This may go some way to address the data sharing challenges. Members of the network are expected to encourage their grantees to be inclusive and publish their results in open access journals. The network includes major funders of research in Africa like the European Commission, Bill & Melinda Gates Foundation and Wellcome Trust.

The roadmap provides a guide on how funders can accelerate research data sharing by the scientists they fund. It recommends that research funding institutions make real-time, external data sharing a requirement. And that research needs to be part of a multi-disciplinary disease network to advance public health emergencies responses.

In addition, funding should focus on strengthening institutions’ capacity on a number of fronts. This includes data management, improving data policies, building trust and aligning tools for data sharing.

Allowing researchers to freely access data generated by global academic counterparts is critical for rapidly informing disease control strategies in public health emergencies….(More)”.

The role of comparative city policy data in assessing progress toward the urban SDG targets

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VeronikaRozhenkova et al in Cities: “As part of the UN Sustainable Development Goals, all countries have agreed to “make cities and human settlements inclusive, safe, resilient and sustainable”. We argue that there is a critical need for large-scale comparative city policy data that, when linked with outcome data, could be used to identify where policies are working and where they could be improved. In an assessment of the landscape of existing city policy data, based on a comprehensive scoping review, we find that existing databases are insufficient for the purposes of comparative analysis. We then describe what an “ideal” city policy database would look like, where it could be housed, and how it could be developed. Such a database could be a key tool for achieving SDG 11, the urban Sustainable Development Goal….(More)”.

Open Data for Development: The World Bank, Aid Transparency, and the Good Governance of International Financial Institutions

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Chapter by Catherine E. Weaver in Good Governance and Modern International Financial Institutions: “Development scholars and practitioners today see progressive access to information and transparency policies as necessary preconditions for improved effectiveness of international development aid and the legitimacy of modern international financial institutions. This chapter examines the evolution of access to information and broader open data policies in international development institutions. Drawing from the case of the World Bank as a “first mover,” this chapter examines the complex internal processes and factors that shape the adoption and implementation of access to information policy reforms. While challenges to achieving robust information disclosure and open data policies across all multilateral and bilateral aid agencies persist, transparency is now a benchmark for good governance in global development finance and the proverbial genie that cannot be put back in the bottle….(More)”.

The Landscape of Rights and Licensing Initiatives for Data Sharing

Curated on Posted on by Stefaan Verhulst

Paper by Sam Grabus and Jane Greenberg: “Over the last twenty years, a wide variety of resources have been developed to address the rights and licensing problems inherent with contemporary data sharing practices. The landscape of developments is this area is increasingly confusing and difficult to navigate, due to the complexity of intellectual property and ethics issues associated with sharing sensitive data. This paper seeks to address this challenge, examining the landscape and presenting a Version 1.0 directory of resources. A multi-method study was pursued, with an environmental scan examining 20 resources, resulting in three high-level categories: standards, tools, and community initiatives; and a content analysis revealing the subcategories of rights, licensing, metadata & ontologies. A timeline confirms a shift in licensing standardization priorities from open data to more nuanced and technologically robust solutions, over time, to accommodate for more sensitive data types. This paper reports on the research undertaking, and comments on the potential for using license-specific metadata supplements and developing data-centric rights and licensing ontologies….(More)”.

The Lives and After Lives of Data

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Paper by Christine L. Borgman: “The most elusive term in data science is ‘data.’ While often treated as objects to be computed upon, data is a theory-laden concept with a long history. Data exist within knowledge infrastructures that govern how they are created, managed, and interpreted. By comparing models of data life cycles, implicit assumptions about data become apparent. In linear models, data pass through stages from beginning to end of life, which suggest that data can be recreated as needed. Cyclical models, in which data flow in a virtuous circle of uses and reuses, are better suited for irreplaceable observational data that may retain value indefinitely. In astronomy, for example, observations from one generation of telescopes may become calibration and modeling data for the next generation, whether digital sky surveys or glass plates. The value and reusability of data can be enhanced through investments in knowledge infrastructures, especially digital curation and preservation. Determining what data to keep, why, how, and for how long, is the challenge of our day…(More)”.

Urbanism Under Google: Lessons from Sidewalk Toronto

Curated on Posted on by Stefaan Verhulst

Paper by Ellen P. Goodman and Julia Powles: “Cities around the world are rapidly adopting digital technologies, data analytics, and the trappings of “smart” infrastructure. No company is more ambitious about exploring data flows and seeking to dominate networks of information than Google. In October 2017, Google affiliate Sidewalk Labs embarked on its first prototype smart city in Toronto, Canada, planning a new kind of data-driven urban environment: “the world’s first neighborhood built from the internet up.” Although the vision is for an urban district foregrounding progressive ideals of inclusivity, for the crucial first 18 months of the venture, many of the most consequential features of the project were hidden from view and unavailable for serious scrutiny. The players defied public accountability on questions about data collection and surveillance, governance, privacy, competition, and procurement. Even more basic questions about the use of public space went unanswered: privatized services, land ownership, infrastructure deployment and, in all cases, the question of who is in control. What was hidden in this first stage, and what was revealed, suggest that the imagined smart city may be incompatible with democratic processes, sustained public governance, and the public interest.

This article analyzes the Sidewalk project in Toronto as it took shape in its first phase, prior to the release of the Master Innovation and Development Plan, exploring three major governance challenges posed by the imagined “city of the future”: privatization, platformization, and domination. The significance of this case study applies well beyond Toronto. Google and related companies are modeling future business growth embedded in cities and using projects like the one in Toronto as test beds. What happens in Toronto is designed to be replicated. We conclude with some lessons, highlighting the precarity of civic stewardship and public accountability when cities are confronted with tantalizing visions of privatized urban innovation…(More)”.

AI & the sustainable development goals: The state of play

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Report by 2030Vision: “…While the world is making progress in some areas, we are falling behind in delivering the SDGs overall. We need all actors – businesses, governments, academia, multilateral institutions, NGOs, and others – to accelerate and scale their efforts to deliver the SDGs, using every tool at their disposal, including artificial intelligence (AI).

In December 2017, 2030Vision published its first report, Uniting to Deliver Technology for the Global Goals, which addressed the role of digital technology – big data, robotics, internet of things, AI, and other technologies – in achieving the SDGs.

In this paper, we focus on AI for the SDGs. AI extends and amplifies the capacity of human beings to understand and solve complex, dynamic, and interconnected systems challenges like the SDGs. Our main objective was to survey the landscape of research and initiatives on AI and the SDGs to identify key themes and questions in need of further exploration. We also reviewed the state of AI and the SDGs in two sectors – food and agriculture and healthcare – to understand if and how AI is being deployed to address the SDGs and the challenges and opportunities in doing so….(More)”.

Why data ownership is the wrong approach to protecting privacy

Curated on Posted on by Stefaan Verhulst

Article by John B. Morris Jr. and Cameron F. Kerry: “It’s my data.” It’s an idea often expressed about information privacy.

Indeed, in congressional hearings last year, Mark Zuckerberg said multiple times that “people own all of their own content” on Facebook. A survey by Insights Network earlier this year found that 79% of consumers said they want compensation when their data is shared. Musician and tech entrepreneur will.i.am took to the website of The Economist to argue that payment for data is a way to “redress the balance” between individuals and “data monarchs.”

Some policymakers are taking such thinking to heart. Senator John Kennedy (R-LA) introduced a three-page bill, the “Own Your Own Data Act of 2019,” which declares that “each individual owns and has an exclusive property right in the data that individual generates on the internet” and requires that social media companies obtain licenses to use this data. Senators Mark Warner (D-VA) and Josh Hawley (R-MO) are filing legislation to require Facebook, Google, and other large collectors of data to disclose the value of personal data they collect, although the bill would not require payments. In California, Governor Gavin Newsome wants to pursue a “data dividend” designed to “share in the wealth that is created from [people’s] data.”

Treating our data as our property has understandable appeal. It touches what the foundational privacy thinker Alan Westin identified as an essential aspect of privacy, a right “to control, edit, manage, and delete information about [individuals] and decide when, how, and to what extent information is communicated to others.” It expresses the unfairness people feel about an asymmetrical marketplace in which we know little about the data we share but the companies that receive the data can profit by extracting marketable information.

The trouble is, it’s not your data; it’s not their data either.  Treating data like it is property fails to recognize either the value that varieties of personal information serve or the abiding interest that individuals have in their personal information even if they choose to “sell” it. Data is not a commodity. It is information. Any system of information rights—whether patents, copyrights, and other intellectual property, or privacy rights—presents some tension with strong interest in the free flow of information that is reflected by the First Amendment. Our personal information is in demand precisely because it has value to others and to society across a myriad of uses.

Treating personal information as property to be licensed or sold may induce people to trade away their privacy rights for very little value while injecting enormous friction into free flow of information. The better way to strengthen privacy is to ensure that individual privacy interests are respected as personal information flows to desirable uses, not to reduce personal data to a commodity….(More)”.