Balancing Act: Innovation vs. Privacy in the Age of Data Portability


Thursday, July 12, 2018 @ 2 MetroTech Center, Brooklyn, NY 11201

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The ability of people to move or copy data about themselves from one service to another — data portability — has been hailed as a way of increasing competition and driving innovation. In many areas, such as through the Open Banking initiative in the United Kingdom, the practice of data portability is fully underway and propagating. The launch of GDPR in Europe has also elevated the issue among companies and individuals alike. But recent online security breaches and other experiences of personal data being transferred surreptitiously from private companies, (e.g., Cambridge Analytica’s appropriation of Facebook data), highlight how data portability can also undermine people’s privacy.

The GovLab at the NYU Tandon School of Engineering is pleased to present Jeni Tennison, CEO of the Open Data Institute, for its next Ideas Lunch, where she will discuss how data portability has been regulated in the UK and Europe, and what governments, businesses and people need to do to strike the balance between its risks and benefits.

Jeni Tennison is the CEO of the Open Data Institute. She gained her PhD from the University of Nottingham then worked as an independent consultant, specialising in open data publishing and consumption, before joining the ODI in 2012. Jeni was awarded an OBE for services to technology and open data in the 2014 New Year Honours.

Before joining the ODI, Jeni was the technical architect and lead developer for legislation.gov.uk. She worked on the early linked data work on data.gov.uk, including helping to engineer new standards for publishing statistics as linked data. She continues her work within the UK’s public sector as a member of the Open Standards Board.

Jeni also works on international web standards. She was appointed to serve on the W3C’s Technical Architecture Group from 2011 to 2015 and in 2014 she started to co-chair the W3C’s CSV on the Web Working Group. She also sits on the Advisory Boards for Open Contracting Partnership and the Data Transparency Lab.

Twitter handle: @JeniT

Data Governance in the Digital Age


Centre for International Governance Innovation: “Data is being hailed as “the new oil.” The analogy seems appropriate given the growing amount of data being collected, and the advances made in its gathering, storage, manipulation and use for commercial, social and political purposes.

Big data and its application in artificial intelligence, for example, promises to transform the way we live and work — and will generate considerable wealth in the process. But data’s transformative nature also raises important questions around how the benefits are shared, privacy, public security, openness and democracy, and the institutions that will govern the data revolution.

The delicate interplay between these considerations means that they have to be treated jointly, and at every level of the governance process, from local communities to the international arena. This series of essays by leading scholars and practitioners, which is also published as a special report, will explore topics including the rationale for a data strategy, the role of a data strategy for Canadian industries, and policy considerations for domestic and international data governance…

RATIONALE OF A DATA STRATEGY

THE ROLE OF A DATA STRATEGY FOR CANADIAN INDUSTRIES

BALANCING PRIVACY AND COMMERCIAL VALUES

DOMESTIC POLICY FOR DATA GOVERNANCE

INTERNATIONAL POLICY CONSIDERATIONS

EPILOGUE

Ten Reasons Not to Measure Impact—and What to Do Instead


Essay by Mary Kay Gugerty & Dean Karlan in the Stanford Social Innovation Review: “Good impact evaluations—those that answer policy-relevant questions with rigor—have improved development knowledge, policy, and practice. For example, the NGO Living Goods conducted a rigorous evaluation to measure the impact of its community health model based on door-to-door sales and promotions. The evidence of impact was strong: Their model generated a 27-percent reduction in child mortality. This evidence subsequently persuaded policy makers, replication partners, and major funders to support the rapid expansion of Living Goods’ reach to five million people. Meanwhile, rigorous evidence continues to further validate the model and help to make it work even better.

Of course, not all rigorous research offers such quick and rosy results. Consider the many studies required to discover a successful drug and the lengthy process of seeking regulatory approval and adoption by the healthcare system. The same holds true for fighting poverty: Innovations for Poverty Action (IPA), a research and policy nonprofit that promotes impact evaluations for finding solutions to global poverty, has conducted more than 650 randomized controlled trials (RCTs) since its inception in 2002. These studies have sometimes provided evidence about how best to use scarce resources (e.g., give away bed nets for free to fight malaria), as well as how to avoid wasting them (e.g., don’t expand traditional microcredit). But the vast majority of studies did not paint a clear picture that led to immediate policy changes. Developing an evidence base is more like building a mosaic: Each individual piece does not make the picture, but bit by bit a picture becomes clearer and clearer.

How do these investments in evidence pay off? IPA estimated the benefits of its research by looking at its return on investment—the ratio of the benefit from the scale-up of the demonstrated large-scale successes divided by the total costs since IPA’s founding. The ratio was 74x—a huge result. But this is far from a precise measure of impact, since IPA cannot establish what would have happened had IPA never existed. (Yes, IPA recognizes the irony of advocating for RCTs while being unable to subject its own operations to that standard. Yet IPA’s approach is intellectually consistent: Many questions and circumstances do not call for RCTs.)

Even so, a simple thought exercise helps to demonstrate the potential payoff. IPA never works alone—all evaluations and policy engagements are conducted in partnership with academics and implementing organizations, and increasingly with governments. Moving from an idea to the research phase to policy takes multiple steps and actors, often over many years. But even if IPA deserves only 10 percent of the credit for the policy changes behind the benefits calculated above, the ratio of benefits to costs is still 7.4x. That is a solid return on investment.

Despite the demonstrated value of high-quality impact evaluations, a great deal of money and time has been wasted on poorly designed, poorly implemented, and poorly conceived impact evaluations. Perhaps some studies had too small of a sample or paid insufficient attention to establishing causality and quality data, and hence any results should be ignored; others perhaps failed to engage stakeholders appropriately, and as a consequence useful results were never put to use.

The push for more and more impact measurement can not only lead to poor studies and wasted money, but also distract and take resources from collecting data that can actually help improve the performance of an effort. To address these difficulties, we wrote a book, The Goldilocks Challenge, to help guide organizations in designing “right-fit” evidence strategies. The struggle to find the right fit in evidence resembles the predicament that Goldilocks faces in the classic children’s fable. Goldilocks, lost in the forest, finds an empty house with a large number of options: chairs, bowls of porridge, and beds of all sizes. She tries each but finds that most do not suit her: The porridge is too hot or too cold, the bed too hard or too soft—she struggles to find options that are “just right.” Like Goldilocks, the social sector has to navigate many choices and challenges to build monitoring and evaluation systems that fit their needs. Some will push for more and more data; others will not push for enough….(More)”.

City Data Exchange – Lessons Learned From A Public/Private Data Collaboration


Report by the Municipality of Copenhagen: “The City Data Exchange (CDE) is the product of a collaborative project between the Municipality of Copenhagen, the Capital Region of Denmark, and Hitachi. The purpose of the project is to examine the possibilities of creating a marketplace for the exchange of data between public and private organizations.

The CDE consists of three parts:

  • A collaboration between the different partners on supply, and demand of specific data;
  • A platform for selling and purchasing data aimed at both public, and private organizations;
  • An effort to establish further experience in the field of data exchange between public, and private organizations.

In 2013, the City of Copenhagen, and the Copenhagen Region decided to invest in the creation of a marketplace for the exchange of public, and private sector data. The initial investment was meant as a seed towards a self-sustained marketplace. This was an innovative approach to test the readiness of the market to deliver new data-sharing solutions.

The CDE is the result of a tender by the Municipality of Copenhagen and the Capital Region of Denmark in 2015. Hitachi Consulting won the tender and has invested, and worked with the Municipality of Copenhagen, and the Capital Region of Denmark to establish an organization and a technical platform.

The City Data Exchange (CDE) has closed a gap in regional data infrastructure. Both public-and private sector organizations have used the CDE to gain insights into data use cases, new external data sources, GDPR issues, and to explore the value of their data. Before the CDE was launched, there were only a few options available to purchase or sell data.

The City and the Region of Copenhagen are utilizing the insights from the CDE project to improve their internal activities and to shape new policies. The lessons from the CDE also provide insights into a wider national infrastructure for effective data sharing. Based on the insights from approximately 1000 people that the CDE has been in contact with, the recommendations are:

  • Start with the use case, as it is key to engage the data community that will use the data;
  • Create a data competence hub, where the data community can meet and get support;
  • Create simple standards and guidelines for data publishing.

The following paper presents some of the key findings from our work with the CDE. It has been compiled by Smart City Insights on behalf of the partners of the City Data Exchange project…(More)”.

Free Speech is a Triangle


Essay by Jack Balkin: “The vision of free expression that characterized much of the twentieth century is inadequate to protect free expression today.

The twentieth century featured a dyadic or dualist model of speech regulation with two basic kinds of players: territorial governments on the one hand, and speakers on the other. The twenty-first century model is pluralist, with multiple players. It is easiest to think of it as a triangle. On one corner are nation states and the European Union. On the second corner are privately-owned Internet infrastructure companies, including social media companies, search engines, broadband providers, and electronic payment systems. On the third corner are many different kinds of speakers, legacy media, civil society organizations, hackers, and trolls.

Territorial goverments continue to regulate speakers and legacy media through traditional or “old-school” speech regulation. But nation states and the European Union also now employ “new-school” speech regulation that is aimed at Internet infrastructure owners and designed to get these private companies to surveil, censor, and regulate speakers for them. Finally, infrastructure companies like Facebook also regulate and govern speakers through techniques of private governance and surveillance.

The practical ability to speak in the digital world emerges from the struggle for power between these various forces, with old-school, new-school and private regulation directed at speakers, and both nation states and civil society organizations pressuring infrastructure owners to regulate speech.

If the characteristic feature of free speech regulation in our time is a triangle that combines new school speech regulation with private governance, then the best way to protect free speech values today is to combat and compensate for that triangle’s evolving logic of public and private regulation. The first goal is to prevent or ameliorate as much as possible collateral censorship and new forms of digital prior restraint. The second goal is to protect people from new methods of digital surveillance and manipulation—methods that emerged from the rise of large multinational companies that depend on data collection, surveillance, analysis, control, and distribution of personal data.

This essay describes how nation states should and should not regulate the digital infrastructure consistent with the values of freedom of speech and press; it emphasizes that different models of regulation are appropriate for different parts of the digital infrastructure. Some parts of the digital infrastructure are best regulated along the lines of common carriers or places of public accommodation. But governments should not impose First Amendment-style or common carriage obligations on social media and search engines. Rather, governments should require these companies to provide due process toward their end-users. Governments should also treat these companies as information fiduciaries who have duties of good faith and non-manipulation toward their end-users. Governments can implement all of these reforms—properly designed—consistent with constitutional guarantees of free speech and free press….(More)”.

Doing Research In and On the Digital: Research Methods across Fields of Inquiry


Book edited by Cristina Costa and Jenna Condie: “As a social space, the web provides researchers both with a tool and an environment to explore the intricacies of everyday life. As a site of mediated interactions and interrelationships, the ‘digital’ has evolved from being a space of information to a space of creation, thus providing new opportunities regarding how, where and, why to conduct social research.

Doing Research In and On the Digital aims to deliver on two fronts: first, by detailing how researchers are devising and applying innovative research methods for and within the digital sphere, and, secondly, by discussing the ethical challenges and issues implied and encountered in such approaches.

In two core Parts, this collection explores:

  • content collection: methods for harvesting digital data
  • engaging research informants: digital participatory methods and data stories .

With contributions from a diverse range of fields such as anthropology, sociology, education, healthcare and psychology, this volume will particularly appeal to post-graduate students and early career researchers who are navigating through new terrain in their digital-mediated research endeavours….(More)”.

Skills for a Lifetime


Nate Silver’s commencement address at Kenyon College: “….Power has shifted toward people and companies with a lot of proficiency in data science.

I obviously don’t think that’s entirely a bad thing. But it’s by no means entirely a good thing, either. You should still inherently harbor some suspicion of big, powerful institutions and their potentially self-serving and short-sighted motivations. Companies and governments that are capable of using data in powerful ways are also capable of abusing it.

What worries me the most, especially at companies like Facebook and at other Silicon Valley behemoths, is the idea that using data science allows one to remove human judgment from the equation. For instance, in announcing a recent change to Facebook’s News Feed algorithm, Mark Zuckerberg claimed that Facebook was not “comfortable” trying to come up with a way to determine which news organizations were most trustworthy; rather, the “most objective” solution was to have readers vote on trustworthiness instead. Maybe this is a good idea and maybe it isn’t — but what bothered me was in the notion that Facebook could avoid responsibility for its algorithm by outsourcing the judgment to its readers.

I also worry about this attitude when I hear people use terms such as “artificial intelligence” and “machine learning” (instead of simpler terms like “computer program”). Phrases like “machine learning” appeal to people’s notion of a push-button solution — meaning, push a button, and the computer does all your thinking for you, no human judgment required.

But the reality is that working with data requires lots of judgment. First, it requires critical judgment — and experience — when drawing inferences from data. And second, it requires moral judgment in deciding what your goals are and in establishing boundaries for your work.

Let’s talk about that first type of judgment — critical judgment. The more experience you have in working with different data sets, the more you’ll realize that the correct interpretation of the data is rarely obvious, and that the obvious-seeming interpretation isn’t always correct. Sometimes changing a single assumption or a single line of code can radically change your conclusion. In the 2016 U.S. presidential election, for instance, there were a series of models that all used almost exactly the same inputs — but they ranged in giving Trump as high as roughly a one-in-three chance of winning the presidency (that was FiveThirtyEight’s model) to as low as one chance in 100, based on fairly subtle aspects of how each algorithm was designed….(More)”.

The 2018 Atlas of Sustainable Development Goals: an all-new visual guide to data and development


World Bank Data Team: “We’re pleased to release the 2018 Atlas of Sustainable Development Goals. With over 180 maps and charts, the new publication shows the progress societies are making towards the 17 SDGs.

It’s filled with annotated data visualizations, which can be reproducibly built from source code and data. You can view the SDG Atlas onlinedownload the PDF publication (30Mb), and access the data and source code behind the figures.

This Atlas would not be possible without the efforts of statisticians and data scientists working in national and international agencies around the world. It is produced in collaboration with the professionals across the World Bank’s data and research groups, and our sectoral global practices.

Trends and analysis for the 17 SDGs

The Atlas draws on World Development Indicators, a database of over 1,400 indicators for more than 220 economies, many going back over 50 years. For example, the chapter on SDG4 includes data from the UNESCO Institute for Statistics on education and its impact around the world.

Throughout the Atlas, data are presented by country, region and income group and often disaggregated by sex, wealth and geography.

The Atlas also explores new data from scientists and researchers where standards for measuring SDG targets are still being developed. For example, the chapter on SDG14 features research led by Global Fishing Watch, published this year in Science. Their team has tracked over 70,000 industrial fishing vessels from 2012 to 2016, processed 22 billion automatic identification system messages to map and quantify fishing around the world….(More)”.

Digital Government Review of Colombia


OECD Report: “This review analyses the shift from e-government to digital government in Colombia. It looks at the governance framework for digital government, the use of digital platforms and open data to engage and collaborate with citizens, conditions for a data-driven public sector, and policy coherence in a context of significant regional disparities. It provides concrete policy recommendations on how digital technologies and data can be harnessed for citizen-driven policy making and public service delivery…(More)”.