The Data Revolution

Curated on Posted on by Stefaan Verhulst

Review of Rob Kitchin’s The Data Revolution: Big Data, Open Data, Data Infrastructures & their Consequences by David Moats in Theory, Culture and Society: “…As an industry, academia is not immune to cycles of hype and fashion. Terms like ‘postmodernism’, ‘globalisation’, and ‘new media’ have each had their turn filling the top line of funding proposals. Although they are each grounded in tangible shifts, these terms become stretched and fudged to the point of becoming almost meaningless. Yet, they elicit strong, polarised reactions. For at least the past few years, ‘big data’ seems to be the buzzword, which elicits funding, as well as the ire of many in the social sciences and humanities.

Rob Kitchin’s book The Data Revolution is one of the first systematic attempts to strip back the hype surrounding our current data deluge and take stock of what is really going on. This is crucial because this hype is underpinned by very real societal change, threats to personal privacy and shifts in store for research methods. The book acts as a helpful wayfinding device in an unfamiliar terrain, which is still being reshaped, and is admirably written in a language relevant to social scientists, comprehensible to policy makers and accessible even to the less tech savvy among us.

The Data Revolution seems to present itself as the definitive account of this phenomena but in filling this role ends up adopting a somewhat diplomatic posture. Kitchin takes all the correct and reasonable stances on the matter and advocates all the right courses of action but he is not able to, in the context of this book, pursue these propositions fully. This review will attempt to tease out some of these latent potentials and how they might be pushed in future work, in particular the implications of the ‘performative’ character of both big data narratives and data infrastructures for social science research.

Kitchin’s book starts with the observation that ‘data’ is a misnomer – etymologically data should refer to phenomena in the world which can be abstracted, measured etc. as opposed to the representations and measurements themselves, which should by all rights be called ‘capta’. This is ironic because the worst offenders in what Kitchin calls “data boosterism” seem to conflate data with ‘reality’, unmooring data from its conditions of production and making relationship between the two given or natural.

As Kitchin notes, following Bowker (2005), ‘raw data’ is an oxymoron: data are not so much mined as produced and are necessarily framed technically, ethically, temporally, spatially and philosophically. This is the central thesis of the book, that data and data infrastructures are not neutral and technical but also social and political phenomena. For those at the critical end of research with data, this is a starting assumption, but one which not enough practitioners heed. Most of the book is thus an attempt to flesh out these rapidly expanding data infrastructures and their politics….

Kitchin is at his best when revealing the gap between the narratives and the reality of data analysis such as the fallacy of empiricism – the assertion that, given the granularity and completeness of big data sets and the availability of machine learning algorithms which identify patterns within data (with or without the supervision of human coders), data can “speak for themselves”. Kitchin reminds us that no data set is complete and even these out-of-the-box algorithms are underpinned by theories and assumptions in their creation, and require context specific knowledge to unpack their findings. Kitchin also rightly raises concerns about the limits of big data, that access and interoperability of data is not given and that these gaps and silences are also patterned (Twitter is biased as a sample towards middle class, white, tech savy people). Yet, this language of veracity and reliability seems to suggest that big data is being conceptualised in relation to traditional surveys, or that our population is still the nation state, when big data could helpfully force us to reimagine our analytic objects and truth conditions and more pressingly, our ethics (Rieder, 2013).

However, performativity may again complicate things. As Kitchin observes, supermarket loyalty cards do not just create data about shopping, they encourage particular sorts of shopping; when research subjects change their behaviour to cater to the metrics and surveillance apparatuses built into platforms like Facebook (Bucher, 2012), then these are no longer just data points representing the social, but partially constitutive of new forms of sociality (this is also true of other types of data as discussed by Savage (2010), but in perhaps less obvious ways). This might have implications for how we interpret data, the distribution between quantitative and qualitative approaches (Latour et al., 2012) or even more radical experiments (Wilkie et al., 2014). Kitchin is relatively cautious about proposing these sorts of possibilities, which is not the remit of the book, though it clearly leaves the door open…(More)”

When Guarding Student Data Endangers Valuable Research

Curated on Posted on by Stefaan Verhulst

Susan M. Dynarski  in the New York Times: “There is widespread concern over threats to privacy posed by the extensive personal data collected by private companies and public agencies.

Some of the potential danger comes from the government: The National Security Agency has swept up the telephone records of millions of people, in what it describes as a search for terrorists. Other threats are posed by hackers, who have exploited security gaps to steal data from retail giantslike Target and from the federal Office of Personnel Management.

Resistance to data collection was inevitable — and it has been particularly intense in education.

Privacy laws have already been strengthened in some states, and multiple bills now pending in state legislatures and in Congress would tighten the security and privacy of student data. Some of this proposed legislation is so broadly written, however, that it could unintentionally choke off the use of student data for its original purpose: assessing and improving education. This data has already exposed inequities, allowing researchers and advocates to pinpoint where poor, nonwhite and non-English-speaking children have been educated inadequately by their schools.

Data gathering in education is indeed extensive: Across the United States, large, comprehensive administrative data sets now track the academic progress of tens of millions of students. Educators parse this data to understand what is working in their schools. Advocates plumb the data to expose unfair disparities in test scores and graduation rates, building cases to target more resources for the poor. Researchers rely on this data when measuring the effectiveness of education interventions.

To my knowledge there has been no large-scale, Target-like theft of private student records — probably because students’ test scores don’t have the market value of consumers’ credit card numbers. Parents’ concerns have mainly centered not on theft, but on the sharing of student data with third parties, including education technology companies. Last year, parentsresisted efforts by the tech start-up InBloom to draw data on millions of students into the cloud and return it to schools as teacher-friendly “data dashboards.” Parents were deeply uncomfortable with a third party receiving and analyzing data about their children.

In response to such concerns, some pending legislation would scale back the authority of schools, districts and states to share student data with third parties, including researchers. Perhaps the most stringent of these proposals, sponsored by Senator David Vitter, a Louisiana Republican, would effectively end the analysis of student data by outside social scientists. This legislation would have banned recent prominent research documenting the benefits of smaller classes, the value of excellent teachersand the varied performance of charter schools.

Under current law, education agencies can share data with outside researchers only to benefit students and improve education. Collaborations with researchers allow districts and states to tap specialized expertise that they otherwise couldn’t afford. The Boston public school district, for example, has teamed up with early-childhood experts at Harvard to plan and evaluate its universal prekindergarten program.

In one of the longest-standing research partnerships, the University of Chicago works with the Chicago Public Schools to improve education. Partnerships like Chicago’s exist across the nation, funded by foundations and the United States Department of Education. In one initiative, a Chicago research consortium compiled reports showing high school principals that many of the seniors they had sent off to college swiftly dropped out without earning a degree. This information spurred efforts to improve high school counseling and college placement.

Specific, tailored information in the hands of teachers, principals or superintendents empowers them to do better by their students. No national survey could have told Chicago’s principals how their students were doing in college. Administrative data can provide this information, cheaply and accurately…(More)”

In The Information Debate, Openness and Privacy Are The Same Thing

Curated on Posted on by Stefaan Verhulst

 at TechCrunch: “We’ve been framing the debate between openness and privacy the wrong way.

Rather than positioning privacy and openness as opposing forces, the fact is they’re different sides of the same coin – and equally important. This might seem simple, but it might also be the key to moving things forward around this crucial debate.

Open data advocates often suggest that openness should be the default for all human knowledge. We should share, re-use and compare data freely and in doing so reap the benefits of innovation, cost savings and increased citizen participation — to name a just a few gains.

And although it might sound a little utopian, the promise is being realized in many corners of the world….But as we all know, even if we accept all the possible benefits of open data, concerns about privacy, especially personal information, still exist as a counter weight to the open data evangelists. People worry that the path of openness could lead to an Orwellian world where all our information is shared with everyone, permanently.

There is a way to turn the conversation from the face-value clash between openness and privacy to how they can be complementary forces. Gus Hosein, CEO of Privacy International, has explained that privacy is “the governing framework to control access to, collection and usage of information.” Basically, privacy laws enable knowledge and control of data about citizens and their surroundings.

Even if we accept all the possible benefits of open data, concerns about privacy, especially personal information, still exist as a counter weight to the open data evangelists.

This is strikingly similar to the argument that open data increases service delivery efficiency and personalization. Openness and privacy both share the same impulse: I want to be in control of my life, I want to know and choose whether a hospital or school is a good hospital or school and be in control of my choice of services.

Another strong thread in conversations around open data is that transparency should be proportionate to power. This makes sense on one level and seems simple enough: Politicians should be held accountable which means a heightened level of transparency.

But who is ‘powerful’, how do you define ‘power’ and who is in charge of defining this?

Politicians have chosen to run for public office and submit themselves to public scrutiny, but what about the CEO of a listed company, the leader of a charity, the anonymous owner of a Cayman-islands’ registered corporation? In practice, it is very difficult to apply the ‘transparency is proportionate to power’ rule outside democratic politics.

We need to stop making a binary distinction between freedom of information laws and data protection; between open data policies and privacy policies. We need one single policy framework that controls as well as encourages the use ‘open’ data.

The closest we get is with so-called PEPs (politically exposed persons) databases: Individuals who are the close family and kin, and close business associates of politicians. But even that defines power as derivative from political power, and not commercial, social or other forms of power.

 And what about personal data?  Should personal data ever be open?

Omidyar Network asked this question to 200 guests at a convention on openness and privacy last year. The audience was split down the middle: 50% thought personal data could never be open data. 50% thought that it should, and that foregoing the opportunity to release it would block the promise of economic gains, better services and other benefits. Open data experts, including the 1,000 who attended a recent meeting in Ottawa, ultimately disagree on this fundamental issue.

Herein lies the challenge. Many of us, including the general public, are uncomfortable with open personal data, even despite the gains it can bring….(More)”

The privacy paradox: The privacy benefits of privacy threats

Curated on Posted on by Stefaan Verhulst

Paper by Benjamin Wittes and Jodie Liu: “In this paper, Wittes and Liu argue that how we balance the relative value of different forms of privacy is a function of how much we fear the potential audiences from whom we want to keep certain information secret.

Some basic principles these authors propose regarding the nature of privacy are as follows:

  1. Most new technologies often both enhance and diminish privacy depending on how it is used, who is using it, and what sorts of privacy that person values.
  2. Individual concern with privacy often will not involve privacy in the abstract, but rather vis à vis specific audiences – that is to say that the question of privacyfrom whom matters.
  3. At least some modern technologies that we commonly think of as privacy-eroding may in fact enhance privacy from the people in our immediate surroundings.

From Google searches to online shopping to Kindle readers, the privacy equation is seldom as simple as a trade of convenience for privacy. It is far more often a tradeoff among different types of privacy, Wittes and Liu suggest. In conclusion, the privacy debate does not pay much attention to aggregated consumer preferences as a metric against which to measure privacy, and the authors venture to suggest that it should….(More)”

Social Dimensions of Privacy

Curated on Posted on by Stefaan Verhulst

New book edited by Dorota Mokrosinska and Beate Roessler: “Written by a select international group of leading privacy scholars, Social Dimensions of Privacy endorses and develops an innovative approach to privacy. By debating topical privacy cases in their specific research areas, the contributors explore the new privacy-sensitive areas: legal scholars and political theorists discuss the European and American approaches to privacy regulation; sociologists explore new forms of surveillance and privacy on social network sites; and philosophers revisit feminist critiques of privacy, discuss markets in personal data, issues of privacy in health care and democratic politics. The broad interdisciplinary character of the volume will be of interest to readers from a variety of scientific disciplines who are concerned with privacy and data protection issues.

  • Takes an innovative approach to privacy which focuses on the social dimensions and value of privacy in contrast to the value of privacy for individuals
  • Addresses readers from a variety of disciplines, including law, philosophy, media studies, gender studies and political science
  • Addresses new privacy-sensitive areas triggered by recent technological developments (More)”

CMS announces entrepreneurs and innovators to access Medicare data

Curated on Posted on by Stefaan Verhulst

Centers for Medicare and Medicaid Services Press Release: “…the acting Centers for Medicare & Medicaid Services (CMS) Administrator, Andy Slavitt, announced a new policy that for the first time will allow innovators and entrepreneurs to access CMS data, such as Medicare claims. As part of the Administration’s commitment to use of data and information to drive transformation of the healthcare delivery system, CMS will allow innovators and entrepreneurs to conduct approved research that will ultimately improve care and provide better tools that should benefit health care consumers through a greater understanding of what the data says works best in health care. The data will not allow the patient’s identity to be determined, but will provide the identity of the providers of care. CMS will begin accepting innovator research requests in September 2015.

“Data is the essential ingredient to building a better, smarter, healthier system. Today’s announcement is aimed directly at shaking up health care innovation and setting a new standard for data transparency,” said acting CMS Administrator Andy Slavitt. “We expect a stream of new tools for beneficiaries and care providers that improve care and personalize decision-making.”

Innovators and entrepreneurs will access data via the CMS Virtual Research Data Center (VRDC) which provides access to granular CMS program data, including Medicare fee-for-service claims data, in an efficient and cost effective manner. Researchers working in the CMS VRDC have direct access to approved privacy-protected data files and are able to conduct their analysis within a secure CMS environment….

Examples of tools or products that innovators and entrepreneurs might develop include care management or predictive modeling tools, which could greatly benefit the healthcare system, in the form of healthier people, better quality, or lower cost of care. Even though all data is privacy-protected, researchers also will not be allowed to remove patient-level data from the VRDC. They will only be able to download aggregated, privacy-protected reports and results to their own personal workstation.  …(More)”

Open data could save the NHS hundreds of millions, says top UK scientist

Curated on Posted on by Stefaan Verhulst

The Guardian: “The UK government must open up and highlight the power of more basic data sets to improve patient care in the NHS and save hundreds of millions of pounds a year, Nigel Shadbolt, chairman of the Open Data Institute (ODI) has urged.

The UK government topped the first league table for open data (paywall)produced by the ODI last year but Shadbolt warns that ministers’ open data responsibilities have not yet been satisfied.

Basic data on prescription administration is now published on a monthly basis but Shadbolt said medical practitioners must be educated about the power of this data to change prescribing habits across the country.

Other data sets, such as trusts’ opening times, consultant lists and details of services, that are promised to make the NHS more accessible are not currently available in a form that is machine-readable.

“These basic sets of information about the processes, the people and places in the health system are all fragmented and fractured and many of them are not available as registers that you can go to,” Shadbolt said.

“Whenever you talk about health data people think you must be talking about personal data and patient data and there are issues, obviously, of absolutely protecting privacy there. But there’s lots of data in the health service that is not about personal patient data at all that would be hugely useful to just have available as machine-readable data for apps to use.”

The UK government has led the way in recent years in encouraging transparency and accountability within the NHS by opening league tables. The publication of league tables on MRSA was followed by a 76-79% drop in infections.

Shadbolt said: “Those hospitals that were worst in their league table don’t like to be there and there was a very rapid diffusion of understanding of best practice across them that you can quantify. It’s many millions of pounds being saved.”

The artificial intelligence and open data expert said the next big area for open data improvement in the NHS is around prescriptions.

Shadbolt pointed to the publication of data about the prescription of statins,which has helped identify savings worth hundreds of millions of pounds: “There is little doubt that this pattern is likely to exist across the whole of the prescribing space.”…(More)”

Protecting Privacy in Data Release

Curated on Posted on by Stefaan Verhulst

Book by Giovanni Livraga: “This book presents a comprehensive approach to protecting sensitive information when large data collections are released by their owners. It addresses three key requirements of data privacy: the protection of data explicitly released, the protection of information not explicitly released but potentially vulnerable due to a release of other data, and the enforcement of owner-defined access restrictions to the released data. It is also the first book with a complete examination of how to enforce dynamic read and write access authorizations on released data, applicable to the emerging data outsourcing and cloud computing situations. Private companies, public organizations and final users are releasing, sharing, and disseminating their data to take reciprocal advantage of the great benefits of making their data available to others. This book weighs these benefits against the potential privacy risks. A detailed analysis of recent techniques for privacy protection in data release and case studies illustrate crucial scenarios. Protecting Privacy in Data Release targets researchers, professionals and government employees working in security and privacy. Advanced-level students in computer science and electrical engineering will also find this book useful as a secondary text or reference….(More)”

Big Data. Big Obstacles.

Curated on Posted on by Stefaan Verhulst

Dalton Conley et al. in the Chronicle of Higher Education: “After decades of fretting over declining response rates to traditional surveys (the mainstay of 20th-century social research), an exciting new era would appear to be dawning thanks to the rise of big data. Social contagion can be studied by scraping Twitter feeds; peer effects are tested on Facebook; long-term trends in inequality and mobility can be assessed by linking tax records across years and generations; social-psychology experiments can be run on Amazon’s Mechanical Turk service; and cultural change can be mapped by studying the rise and fall of specific Google search terms. In many ways there has been no better time to be a scholar in sociology, political science, economics, or related fields.

However, what should be an opportunity for social science is now threatened by a three-headed monster of privatization, amateurization, and Balkanization. A coordinated public effort is needed to overcome all of these obstacles.

While the availability of social-media data may obviate the problem of declining response rates, it introduces all sorts of problems with the level of access that researchers enjoy. Although some data can be culled from the web—Twitter feeds and Google searches—other data sit behind proprietary firewalls. And as individual users tune up their privacy settings, the typical university or independent researcher is increasingly locked out. Unlike federally funded studies, there is no mandate for Yahoo or Alibaba to make its data publicly available. The result, we fear, is a two-tiered system of research. Scientists working for or with big Internet companies will feast on humongous data sets—and even conduct experiments—and scholars who do not work in Silicon Valley (or Alley) will be left with proverbial scraps….

To address this triple threat of privatization, amateurization, and Balkanization, public social science needs to be bolstered for the 21st century. In the current political and economic climate, social scientists are not waiting for huge government investment like we saw during the Cold War. Instead, researchers have started to knit together disparate data sources by scraping, harmonizing, and geo­coding any and all information they can get their hands on.

Currently, many firms employ some well-trained social and behavioral scientists free to pursue their own research; likewise, some companies have programs by which scholars can apply to be in residence or work with their data extramurally. However, as Facebook states, its program is “by invitation only and requires an internal Facebook champion.” And while Google provides services like Ngram to the public, such limited efforts at data sharing are not enough for truly transparent and replicable science….(More)”

 

Selected Readings on Data Governance

Curated on Posted on by Jos Berens

Jos Berens (Centre for Innovation, Leiden University) and Stefaan G. Verhulst (GovLab)

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data governance was originally published in 2015.

Context
The field of Data Collaboratives is premised on the idea that sharing and opening-up private sector datasets has great – and yet untapped – potential for promoting social good. At the same time, the potential of data collaboratives depends on the level of societal trust in the exchange, analysis and use of the data exchanged. Strong data governance frameworks are essential to ensure responsible data use. Without such governance regimes, the emergent data ecosystem will be hampered and the (perceived) risks will dominate the (perceived) benefits. Further, without adopting a human-centered approach to the design of data governance frameworks, including iterative prototyping and careful consideration of the experience, the responses may fail to be flexible and targeted to real needs.

Selected Readings List (in alphabetical order)

Annotated Selected Readings List (in alphabetical order)

Better Place Lab, “Privacy, Transparency and Trust.” Mozilla, 2015. Available from: http://www.betterplace-lab.org/privacy-report.

  • This report looks specifically at the risks involved in the social sector having access to datasets, and the main risks development organizations should focus on to develop a responsible data use practice.
  • Focusing on five specific countries (Brazil, China, Germany, India and Indonesia), the report displays specific country profiles, followed by a comparative analysis centering around the topics of privacy, transparency, online behavior and trust.
  • Some of the key findings mentioned are:
    • A general concern on the importance of privacy, with cultural differences influencing conception of what privacy is.
    • Cultural differences determining how transparency is perceived, and how much value is attached to achieving it.
    • To build trust, individuals need to feel a personal connection or get a personal recommendation – it is hard to build trust regarding automated processes.

Montjoye, Yves Alexandre de; Kendall, Jake and; Kerry, Cameron F. “Enabling Humanitarian Use of Mobile Phone Data.” The Brookings Institution, 2015. Available from: http://www.brookings.edu/research/papers/2014/11/12-enabling-humanitarian-use-mobile-phone-data.

  • Focussing in particular on mobile phone data, this paper explores ways of mitigating privacy harms involved in using call detail records for social good.
  • Key takeaways are the following recommendations for using data for social good:
    • Engaging companies, NGOs, researchers, privacy experts, and governments to agree on a set of best practices for new privacy-conscientious metadata sharing models.
    • Accepting that no framework for maximizing data for the public good will offer perfect protection for privacy, but there must be a balanced application of privacy concerns against the potential for social good.
    • Establishing systems and processes for recognizing trusted third-parties and systems to manage datasets, enable detailed audits, and control the use of data so as to combat the potential for data abuse and re-identification of anonymous data.
    • Simplifying the process among developing governments in regards to the collection and use of mobile phone metadata data for research and public good purposes.

Centre for Democracy and Technology, “Health Big Data in the Commercial Context.” Centre for Democracy and Technology, 2015. Available from: https://cdt.org/insight/health-big-data-in-the-commercial-context/.

  • Focusing particularly on the privacy issues related to using data generated by individuals, this paper explores the overlap in privacy questions this field has with other data uses.
  • The authors note that although the Health Insurance Portability and Accountability Act (HIPAA) has proven a successful approach in ensuring accountability for health data, most of these standards do not apply to developers of the new technologies used to collect these new data sets.
  • For non-HIPAA covered, customer facing technologies, the paper bases an alternative framework for consideration of privacy issues. The framework is based on the Fair Information Practice Principles, and three rounds of stakeholder consultations.

Center for Information Policy Leadership, “A Risk-based Approach to Privacy: Improving Effectiveness in Practice.” Centre for Information Policy Leadership, Hunton & Williams LLP, 2015. Available from: https://www.informationpolicycentre.com/uploads/5/7/1/0/57104281/white_paper_1-a_risk_based_approach_to_privacy_improving_effectiveness_in_practice.pdf.

  • This white paper is part of a project aiming to explain what is often referred to as a new, risk-based approach to privacy, and the development of a privacy risk framework and methodology.
  • With the pace of technological progress often outstripping the capabilities of privacy officers to keep up, this method aims to offer the ability to approach privacy matters in a structured way, assessing privacy implications from the perspective of possible negative impact on individuals.
  • With the intended outcomes of the project being “materials to help policy-makers and legislators to identify desired outcomes and shape rules for the future which are more effective and less burdensome”, insights from this paper might also feed into the development of innovative governance mechanisms aimed specifically at preventing individual harm.

Centre for Information Policy Leadership, “Data Governance for the Evolving Digital Market Place”, Centre for Information Policy Leadership, Hunton & Williams LLP, 2011. Available from: http://www.huntonfiles.com/files/webupload/CIPL_Centre_Accountability_Data_Governance_Paper_2011.pdf.

  • This paper argues that as a result of the proliferation of large scale data analytics, new models governing data inferred from society will shift responsibility to the side of organizations deriving and creating value from that data.
  • It is noted that, with the reality of the challenge corporations face of enabling agile and innovative data use “In exchange for increased corporate responsibility, accountability [and the governance models it mandates, ed.] allows for more flexible use of data.”
  • Proposed as a means to shift responsibility to the side of data-users, the accountability principle has been researched by a worldwide group of policymakers. Tailing the history of the accountability principle, the paper argues that it “(…) requires that companies implement programs that foster compliance with data protection principles, and be able to describe how those programs provide the required protections for individuals.”
  • The following essential elements of accountability are listed:
    • Organisation commitment to accountability and adoption of internal policies consistent with external criteria
    • Mechanisms to put privacy policies into effect, including tools, training and education
    • Systems for internal, ongoing oversight and assurance reviews and external verification
    • Transparency and mechanisms for individual participation
    • Means of remediation and external enforcement

Crawford, Kate; Schulz, Jason. “Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harm.” NYU School of Law, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2325784&download=yes.

  • Considering the privacy implications of large-scale analysis of numerous data sources, this paper proposes the implementation of a ‘procedural data due process’ mechanism to arm data subjects against potential privacy intrusions.
  • The authors acknowledge that some privacy protection structures already know similar mechanisms. However, due to the “inherent analytical assumptions and methodological biases” of big data systems, the authors argue for a more rigorous framework.

Letouze, Emmanuel, and; Vinck, Patrick. “The Ethics and Politics of Call Data Analytics”, DataPop Alliance, 2015. Available from: http://static1.squarespace.com/static/531a2b4be4b009ca7e474c05/t/54b97f82e4b0ff9569874fe9/1421442946517/WhitePaperCDRsEthicFrameworkDec10-2014Draft-2.pdf.

  • Focusing on the use of Call Detail Records (CDRs) for social good in development contexts, this whitepaper explores both the potential of these datasets – in part by detailing recent successful efforts in the space – and political and ethical constraints to their use.
  • Drawing from the Menlo Report Ethical Principles Guiding ICT Research, the paper explores how these principles might be unpacked to inform an ethics framework for the analysis of CDRs.

Data for Development External Ethics Panel, “Report of the External Ethics Review Panel.” Orange, 2015. Available from: http://www.d4d.orange.com/fr/content/download/43823/426571/version/2/file/D4D_Challenge_DEEP_Report_IBE.pdf.

  • This report presents the findings of the external expert panel overseeing the Orange Data for Development Challenge.
  • Several types of issues faced by the panel are described, along with the various ways in which the panel dealt with those issues.

Federal Trade Commission Staff Report, “Mobile Privacy Disclosures: Building Trust Through Transparency.” Federal Trade Commission, 2013. Available from: www.ftc.gov/os/2013/02/130201mobileprivacyreport.pdf.

  • This report looks at ways to address privacy concerns regarding mobile phone data use. Specific advise is provided for the following actors:
    • Platforms, or operating systems providers
    • App developers
    • Advertising networks and other third parties
    • App developer trade associations, along with academics, usability experts and privacy researchers

Mirani, Leo. “How to use mobile phone data for good without invading anyone’s privacy.” Quartz, 2015. Available from: http://qz.com/398257/how-to-use-mobile-phone-data-for-good-without-invading-anyones-privacy/.

  • This paper considers the privacy implications of using call detail records for social good, and ways to mitigate risks of privacy intrusion.
  • Taking example of the Orange D4D challenge and the anonymization strategy that was employed there, the paper describes how classic ‘anonymization’ is often not enough. The paper then lists further measures that can be taken to ensure adequate privacy protection.

Bernholz, Lucy. “Several Examples of Digital Ethics and Proposed Practices” Stanford Ethics of Data conference, 2014, Available from: http://www.scribd.com/doc/237527226/Several-Examples-of-Digital-Ethics-and-Proposed-Practices.

  • This list of readings prepared for Stanford’s Ethics of Data conference lists some of the leading available literature regarding ethical data use.

Abrams, Martin. “A Unified Ethical Frame for Big Data Analysis.” The Information Accountability Foundation, 2014. Available from: http://www.privacyconference2014.org/media/17388/Plenary5-Martin-Abrams-Ethics-Fundamental-Rights-and-BigData.pdf.

  • Going beyond privacy, this paper discusses the following elements as central to developing a broad framework for data analysis:
    • Beneficial
    • Progressive
    • Sustainable
    • Respectful
    • Fair

Lane, Julia; Stodden, Victoria; Bender, Stefan, and; Nissenbaum, Helen, “Privacy, Big Data and the Public Good”, Cambridge University Press, 2014. Available from: http://www.dataprivacybook.org.

  • This book treats the privacy issues surrounding the use of big data for promoting the public good.
  • The questions being asked include the following:
    • What are the ethical and legal requirements for scientists and government officials seeking to serve the public good without harming individual citizens?
    • What are the rules of engagement?
    • What are the best ways to provide access while protecting confidentiality?
    • Are there reasonable mechanisms to compensate citizens for privacy loss?

Richards, Neil M, and; King, Jonathan H. “Big Data Ethics”. Wake Forest Law Review, 2014. Available from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2384174.

  • This paper describes the growing impact of big data analytics on society, and argues that because of this impact, a set of ethical principles to guide data use is called for.
  • The four proposed themes are: privacy, confidentiality, transparency and identity.
  • Finally, the paper discusses how big data can be integrated into society, going into multiple facets of this integration, including the law, roles of institutions and ethical principles.

OECD, “OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data”. Available from: http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm.

  • A globally used set of principles to inform thought about handling personal data, the OECD privacy guidelines serve as one the leading standards for informing privacy policies and data governance structures.
  • The basic principles of national application are the following:
    • Collection Limitation Principle
    • Data Quality Principle
    • Purpose Specification Principle
    • Use Limitation Principle
    • Security Safeguards Principle
    • Openness Principle
    • Individual Participation Principle
    • Accountability Principle

The White House Big Data and Privacy Working Group, “Big Data: Seizing Opportunities, Preserving Values”, White House, 2015. Available from: https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf.

  • Documenting the findings of the White House big data and privacy working group, this report lists i.a. the following key recommendations regarding data governance:
    • Bringing greater transparency to the data services industry
    • Stimulating international conversation on big data, with multiple stakeholders
    • With regard to educational data: ensuring data is used for the purpose it is collected for
    • Paying attention to the potential for big data to facilitate discrimination, and expanding technical understanding to stop discrimination

William Hoffman, “Pathways for Progress” World Economic Forum, 2015. Available from: http://www3.weforum.org/docs/WEFUSA_DataDrivenDevelopment_Report2015.pdf.

  • This paper treats i.a. the lack of well-defined and balanced governance mechanisms as one of the key obstacles preventing particularly corporate sector data from being shared in a controlled space.
  • An approach that balances the benefits against the risks of large scale data usage in a development context, building trust among all stake holders in the data ecosystem, is viewed as key.
  • Furthermore, this whitepaper notes that new governance models are required not just by the growing amount of data and analytical capacity, and more refined methods for analysis. The current “super-structure” of information flows between institutions is also seen as one of the key reasons to develop alternatives to the current – outdated – approaches to data governance.