privacy

Reclaiming personal data for the common good

Curated on September 20, 2017August 3, 2018 by Stefaan Verhulst

Theo Bass at Nesta: “…The argument of our new report for DECODE is that more of the social value of personal data can be discovered by tools and platforms that give people the power to decide how their data is used. We need to flip the current model on its head, giving people back full control and respecting our data protection and fundamental rights framework.

The report describes how this might pave the way for a fairer distribution of the value generated by data, while opening up new use-cases that are valuable to government, society and individuals themselves. In order to achieve this vision, the DECODE project will develop and test the following:

Flexible rules to give people full control: There is currently a lack of technical and legal norms that would allow people to control and share data on their own terms. If this were possible, then people might be able to share their data for the public good, or publish it as anonymised open data under specific conditions, or for specific use-cases (say, non-commercial purposes). DECODE is working with the Making Sense project and Barcelona City Council to assist local communities with new forms of citizen sensing. The pilots will tackle the challenges of collating, storing and sharing data anonymously to influence policy on the city’s digital democracy platform Decidim (part of the D-CENT toolkit).

Trusted platforms to realise the collective value of data: Much of the opportunity will only be realised where individuals are able to pool their data together to leverage its potential economic and social value. Platform cooperatives offer a feasible model, highlighting the potential of digital technologies to help members collectively govern themselves. Effective data sharing has to be underpinned by high levels of user trust, and platform co-ops achieve this by embedding openness, respect for individual users’ privacy, and democratic participation over how decisions are made. DECODE is working with two platform co-ops – a neighbourhood social networking site called Gebied Online; and a democratic alternative to Airbnb in Amsterdam called FairBnB – to test new privacy-preserving features and granular data sharing options….(More)”

Free Speech in the Algorithmic Society: Big Data, Private Governance, and New School Speech Regulation

Curated on September 20, 2017August 3, 2018 by Stefaan Verhulst

Paper by Jack Balkin: “We have now moved from the early days of the Internet to the Algorithmic Society. The Algorithmic Society features the use of algorithms, artificial intelligence agents, and Big Data to govern populations. It also features digital infrastructure companies, large multi-national social media platforms, and search engines that sit between traditional nation states and ordinary individuals, and serve as special-purpose governors of speech.

The Algorithmic Society presents two central problems for freedom of expression. First, Big Data allows new forms of manipulation and control, which private companies will attempt to legitimate and insulate from regulation by invoking free speech principles. Here First Amendment arguments will likely be employed to forestall digital privacy guarantees and prevent consumer protection regulation. Second, privately owned digital infrastructure companies and online platforms govern speech much as nation states once did. Here the First Amendment, as normally construed, is simply inadequate to protect the practical ability to speak.

The first part of the essay describes how to regulate online businesses that employ Big Data and algorithmic decision making consistent with free speech principles. Some of these businesses are “information fiduciaries” toward their end-users; they must exercise duties of good faith and non-manipulation. Other businesses who are not information fiduciaries have a duty not to engage in “algorithmic nuisance”: they may not externalize the costs of their analysis and use of Big Data onto innocent third parties.

The second part of the essay turns to the emerging pluralist model of online speech regulation. This pluralist model contrasts with the traditional dyadic model in which nation states regulated the speech of their citizens.

In the pluralist model, territorial governments continue to regulate the speech directly. But they also attempt to coerce or co-opt owners of digital infrastructure to regulate the speech of others. This is “new school” speech regulation….(More)”.

The Promise of Evidence-Based Policymaking

Curated on September 14, 2017October 9, 2018 by Stefaan Verhulst

Final Report by the Commission on Evidence-Based Policymaking: “…There are many barriers to the efective use of government data to generate evidence. Better access to these data holds the potential for substantial gains for society. The Commission’s recommendations recognize that the country’s laws and practices are not currently optimized to support the use of data for evidence building, nor in a manner that best protects privacy. To correct these problems, the Commission makes the following recommendations:

Establish a National Secure Data Service to facilitate access to data for evidence building while ensuring privacy and transparency in how those data are used. As a state-of-the-art resource for improving government’s capacity to use the data it already collects, the National Secure Data Service will be able to temporarily link existing data and provide secure access to those data for exclusively statistical purposes in connection with approved projects. The National Secure Data Service will do this without creating a data clearinghouse or warehouse.
Require stringent privacy qualifcations for acquiring and combining data for statistical purposes at the National Secure Data Service to ensure that data continue to be efectively protected while improving the government’s ability to understand the impacts of programs on a wider range of outcomes. At the same time, consider additional statutory changes to enable ongoing statistical production that, under the same stringent privacy qualifcations, may make use of combined data.
Review and, where needed, revise laws authorizing Federal data collection and use to ensure that limited access to administrative and survey data is possible to return benefts to the public through improved programs and policies, but only under strict privacy controls.
Ensure state-collected quarterly earnings data are available for statistical purposes, including to support the many evidence-building activities for which earnings are an important outcome.
Make additional state-collected data about Federal programs available for evidence building. Where appropriate, states that administer programs with substantial Federal investment should in return provide the data necessary for evidence building.
Develop a uniform process for external researchers to apply and qualify for secure access to confdential government data for evidence-building purposes while protecting privacy by carefully restricting data access to qualifed and approved researchers…(More)”

Harnessing the Data Revolution to Achieve the Sustainable Development Goals

Curated on September 11, 2017August 3, 2018 by Stefaan Verhulst

Erol Yayboke et al at CSIS: “Functioning societies collect accurate data and utilize the evidence to inform policy. The use of evidence derived from data in policymaking requires the capability to collect and analyze accurate data, clear administrative channels through which timely evidence is made available to decisionmakers, and the political will to rely on—and ideally share—the evidence. The collection of accurate and timely data, especially in the developing world, is often logistically difficult, not politically expedient, and/or expensive.

Before launching its second round of global goals—the Sustainable Development Goals (SDGs)—the United Nations convened a High-Level Panel of Eminent Persons on the Post-2015 Development Agenda. As part of its final report, the Panel called for a “data revolution” and recommended the formation of an independent body to lead the charge.¹The report resulted in the creation of the Global Partnership for Sustainable Development Data (GPSDD)—an independent group of countries, companies, data communities, and NGOs—and the SDG Data Labs, a private initiative partnered with the GPSDD. In doing so the United Nations and its partners signaled broad interest in data and evidence-based policymaking at a high level. In fact, the GPSDD calls for the “revolution in data” by addressing the “crisis of non-existent, inaccessible or unreliable data.”²As this report shows, this is easier said than done.

This report defines the data revolution as an unprecedented increase in the volume and types of data—and the subsequent demand for them—thanks to the ongoing yet uneven proliferation of new technologies. This revolution is allowing governments, companies, researchers, and citizens to monitor progress and drive action, often with real-time, dynamic, disaggregated data. Much work will be needed to make sure the data revolution reaches developing countries facing difficult challenges (i.e., before the data revolution fully becomes the data revolution for sustainable development). It is important to think of the revolution as a multistep process, beginning with building basic knowledge and awareness of the value of data. This is followed by a more specific focus on public private partnerships, opportunities, and constraints regarding collection and utilization of data for evidence-based policy decisions….

This report provides the following recommendations to the international community to play a constructive role in the data revolution:

Don’t fixate on big data alone. Focus on the foundation necessary to facilitate leapfrogs around all types of data: small, big, and everywhere in between.
Increase funding for capacity building as part of an expansion of broader educational development priorities.
Highlight, share, and support enlightened government-driven approaches to data.
Increase funding for the data revolution and coordinate donor efforts.
Coordinate UN data revolution-related activities closely with an expanded GPSDD.
Secure consensus on data sharing, ownership, and privacy-related international standards….(More)”.

Owned: Property, Privacy, and the New Digital Serfdom Read

Curated on September 10, 2017August 3, 2018 by Stefaan Verhulst

Book by Joshua A. T. Fairfield: “… explains the crisis of digital ownership – how and why we no longer control our smartphones or software-enable devices, which are effectively owned by software and content companies. In two years we will not own our ‘smart’ televisions which will also be used by advertisers to listen in to our living rooms. In the coming decade, if we do not take back our ownership rights, the same will be said of our self-driving cars and software-enabled homes. We risk becoming digital peasants, owned by software and advertising companies, not to mention overreaching governments. Owned should be read by anyone wanting to know more about the loss of our property rights, the implications for our privacy rights and how we can regain control of both….(More)”.

What does it mean to be differentially private?

Curated on August 17, 2017August 3, 2018 by Stefaan Verhulst

Paul Francis at IAPP: “Back in June 2016, Apple announced it will use differential privacy to protect individual privacy for certain data that it collects. Though already a hot research topic for over a decade, this announcement introduced differential privacy to the broader public. Before that announcement, Google had already been using differential privacy for collecting Chrome usage statistics. And within the last month, Uber announced that they too are using differential privacy.

If you’ve done a little homework on differential privacy, you may have learned that it provides provable guarantees of privacy and concluded that a database that is differentially private is, well, private — in other words, that it protects individual privacy. But that isn’t necessarily the case. When someone says, “a database is differentially private,” they don’t mean that the database is private. Rather, they mean, “the privacy of the database can be measured.”

Really, it is like saying that “a bridge is weight limited.” If you know the weight limit of a bridge, then yes, you can use the bridge safely. But the bridge isn’t safe under all conditions. You can exceed the weight limit and hurt yourself.

The weight limit of bridges is expressed in tons, kilograms or number of people. Simplifying here a bit, the amount of privacy afforded by a differentially private database is expressed as a number, by convention labeled ε (epsilon). Lower ε means more private.

All bridges have a weight limit. Everybody knows this, so it sounds dumb to say, “a bridge is weight limited.” And guess what? All databases are differentially private. Or, more precisely, all databases have an ε. A database with no privacy protections at all has an ε of infinity. It is pretty misleading to call such a database differentially private, but mathematically speaking, it is not incorrect to do so. A database that can’t be queried at all has an ε of zero. Private, but useless.

In their paper on differential privacy for statistics, Cynthia Dwork and Adam Smith write, “The choice of ε is essentially a social question. We tend to think of ε as, say, 0.01, 0.1, or in some cases, ln 2 or ln 3.” The natural logarithm of 3 (ln 3) is around 1.1….(More)”.

Artificial Intelligence for Citizen Services and Government

Curated on August 14, 2017August 3, 2018 by Stefaan Verhulst

Paper by Hila Mehr: “From online services like Netflix and Facebook, to chatbots on our phones and in our homes like Siri and Alexa, we are beginning to interact with artificial intelligence (AI) on a near daily basis. AI is the programming or training of a computer to do tasks typically reserved for human intelligence, whether it is recommending which movie to watch next or answering technical questions. Soon, AI will permeate the ways we interact with our government, too. From small cities in the US to countries like Japan, government agencies are looking to AI to improve citizen services.

While the potential future use cases of AI in government remain bounded by government resources and the limits of both human creativity and trust in government, the most obvious and immediately beneficial opportunities are those where AI can reduce administrative burdens, help resolve resource allocation problems, and take on significantly complex tasks. Many AI case studies in citizen services today fall into five categories: answering questions, filling out and searching documents, routing requests, translation, and drafting documents. These applications could make government work more efficient while freeing up time for employees to build better relationships with citizens. With citizen satisfaction with digital government offerings leaving much to be desired, AI may be one way to bridge the gap while improving citizen engagement and service delivery.

Despite the clear opportunities, AI will not solve systemic problems in government, and could potentially exacerbate issues around service delivery, privacy, and ethics if not implemented thoughtfully and strategically. Agencies interested in implementing AI can learn from previous government transformation efforts, as well as private-sector implementation of AI. Government offices should consider these six strategies for applying AI to their work: make AI a part of a goals-based, citizen-centric program; get citizen input; build upon existing resources; be data-prepared and tread carefully with privacy; mitigate ethical risks and avoid AI decision making; and, augment employees, do not replace them.

This paper explores the various types of AI applications, and current and future uses of AI in government delivery of citizen services, with a focus on citizen inquiries and information. It also offers strategies for governments as they consider implementing AI….(More)”

Mastercard’s Big Data For Good Initiative: Data Philanthropy On The Front Lines

Curated on August 10, 2017August 3, 2018 by Stefaan Verhulst

Interview by Randy Bean of Shamina Singh: Much has been written about big data initiatives and the efforts of market leaders to derive critical business insights faster. Less has been written about initiatives by some of these same firms to apply big data and analytics to a different set of issues, which are not solely focused on revenue growth or bottom line profitability. While the focus of most writing has been on the use of data for competitive advantage, a small set of companies has been undertaking, with much less fanfare, a range of initiatives designed to ensure that data can be applied not just for corporate good, but also for social good.

One such firm is Mastercard, which describes itself as a technology company in the payments industry, which connects buyers and sellers in 210 countries and territories across the globe. In 2013 Mastercard launched the Mastercard Center for Inclusive Growth, which operates as an independent subsidiary of Mastercard and is focused on the application of data to a range of issues for social benefit….

In testimony before the Senate Committee on Foreign Affairs on May 4, 2017, Mastercard Vice Chairman Walt Macnee, who serves as the Chairman of the Center for Inclusive Growth, addressed issues of private sector engagement. Macnee noted, “The private sector and public sector can each serve as a force for good independently; however when the public and private sectors work together, they unlock the potential to achieve even more.” Macnee further commented, “We will continue to leverage our technology, data, and know-how in an effort to solve many of the world’s most pressing problems. It is the right thing to do, and it is also good for business.”…

Central to the mission of the Mastercard Center is the notion of “data philanthropy”. This term encompasses notions of data collaboration and data sharing and is at the heart of the initiatives that the Center is undertaking. The three cornerstones on the Center’s mandate are:

Sharing Data Insights– This is achieved through the concept of “data grants”, which entails granting access to proprietary insights in support of social initiatives in a way that fully protects consumer privacy.
Data Knowledge – The Mastercard Center undertakes collaborations with not-for-profit and governmental organizations on a range of initiatives. One such effort was in collaboration with the Obama White House’s Data-Driven Justice Initiative, by which data was used to help advance criminal justice reform. This initiative was then able, through the use of insights provided by Mastercard, to demonstrate the impact crime has on merchant locations and local job opportunities in Baltimore.
Leveraging Expertise – Similarly, the Mastercard Center has collaborated with private organizations such as DataKind, which undertakes data science initiatives for social good.Just this past month, the Mastercard Center released initial findings from its Data Exploration: Neighborhood Crime and Local Business initiative. This effort was focused on ways in which Mastercard’s proprietary insights could be combined with public data on commercial robberies to help understand the potential relationships between criminal activity and business closings. A preliminary analysis showed a spike in commercial robberies followed by an increase in bar and nightclub closings. These analyses help community and business leaders understand factors that can impact business success.Late last year, Ms. Singh issued A Call to Action on Data Philanthropy, in which she challenges her industry peers to look at ways in which they can make a difference — “I urge colleagues at other companies to review their data assets to see how they may be leveraged for the benefit of society.” She concludes, “the sheer abundance of data available today offers an unprecedented opportunity to transform the world for good.”….(More)

The Nudging Divide in the Digital Big Data Era

Curated on August 7, 2017August 3, 2018 by Stefaan Verhulst

Julia M. Puaschunder in the International Robotics & Automation Journal: “Since the end of the 1970ies a wide range of psychological, economic and sociological laboratory and field experiments proved human beings deviating from rational choices and standard neo-classical profit maximization axioms to fail to explain how human actually behave. Behavioral economists proposed to nudge and wink citizens to make better choices for them with many different applications. While the motivation behind nudging appears as a noble endeavor to foster peoples’ lives around the world in very many different applications, the nudging approach raises questions of social hierarchy and class division. The motivating force of the nudgital society may open a gate of exploitation of the populace and – based on privacy infringements – stripping them involuntarily from their own decision power in the shadow of legally-permitted libertarian paternalism and under the cloak of the noble goal of welfare-improving global governance. Nudging enables nudgers to plunder the simple uneducated citizen, who is neither aware of the nudging strategies nor able to oversee the tactics used by the nudgers.

The nudgers are thereby legally protected by democratically assigned positions they hold or by outsourcing strategies used, in which social media plays a crucial rule. Social media forces are captured as unfolding a class dividing nudgital society, in which the provider of social communication tools can reap surplus value from the information shared of social media users. The social media provider thereby becomes a capitalist-industrialist, who benefits from the information shared by social media users, or so-called consumer-workers, who share private information in their wish to interact with friends and communicate to public. The social media capitalist-industrialist reaps surplus value from the social media consumer-workers’ information sharing, which stems from nudging social media users. For one, social media space can be sold to marketers who can constantly penetrate the consumer-worker in a subliminal way with advertisements. But also nudging occurs as the big data compiled about the social media consumer-worker can be resold to marketers and technocrats to draw inferences about consumer choices, contemporary market trends or individual personality cues used for governance control, such as, for instance, border protection and tax compliance purposes.

The law of motion of the nudging societies holds an unequal concentration of power of those who have access to compiled data and who abuse their position under the cloak of hidden persuasion and in the shadow of paternalism. In the nudgital society, information, education and differing social classes determine who the nudgers and who the nudged are. Humans end in different silos or bubbles that differ in who has power and control and who is deceived and being ruled. The owners of the means of governance are able to reap a surplus value in a hidden persuasion, protected by the legal vacuum to curb libertarian paternalism, in the moral shadow of the unnoticeable guidance and under the cloak of the presumption that some know what is more rational than others. All these features lead to an unprecedented contemporary class struggle between the nudgers (those who nudge) and the nudged (those who are nudged), who are divided by the implicit means of governance in the digital scenery. In this light, governing our common welfare through deceptive means and outsourced governance on social media appears critical. In combination with the underlying assumption of the nudgers knowing better what is right, just and fair within society, the digital age and social media tools hold potential unprecedented ethical challenges….(More)”