data – Page 7 – The Living Library

Towards Digital Enlightenment: Essays on the Dark and Light Sides of the Digital Revolution

Curated on September 1, 2018 by Stefaan Verhulst

Book edited by Dirk Helbing: “This new collection of essays follows in the footsteps of the successful volume Thinking Ahead – Essays on Big Data, Digital Revolution, and Participatory Market Society, published at a time when our societies were on a path to technological totalitarianism, as exemplified by mass surveillance reported by Edward Snowden and others.

Meanwhile the threats have diversified and tech companies have gathered enough data to create detailed profiles about almost everyone living in the modern world – profiles that can predict our behavior better than our friends, families, or even partners. This is not only used to manipulate peoples’ opinions and voting behaviors, but more generally to influence consumer behavior at all levels. It is becoming increasingly clear that we are rapidly heading towards a cybernetic society, in which algorithms and social bots aim to control both the societal dynamics and individual behaviors….(More)”.

The Copenhagen Letter (on ethical technology)

Curated on September 1, 2018 by Stefaan Verhulst

Open Letter: “To everyone who shapes technology today

We live in a world where technology is consuming society, ethics, and our core existence.

It is time to take responsibility for the world we are creating. Time to put humans before business. Time to replace the empty rhetoric of “building a better world” with a commitment to real action. It is time to organize, and to hold each other accountable.

Tech is not above us. It should be governed by all of us, by our democratic institutions. It should play by the rules of our societies. It should serve our needs, both individual and collective, as much as our wants.

Progress is more than innovation. We are builders at heart. Let us create a new Renaissance. We will open and nourish honest public conversation about the power of technology. We are ready to serve our societies. We will apply the means at our disposal to move our societies and their institutions forward.

Let us build from trust. Let us build for true transparency. We need digital citizens, not mere consumers. We all depend on transparency to understand how technology shapes us, which data we share, and who has access to it. Treating each other as commodities from which to extract maximum economic value is bad, not only for society as a complex, interconnected whole but for each and every one of us.

Design open to scrutiny. We must encourage a continuous, public, and critical reflection on our definition of success as it defines how we build and design for others. We must seek to design with those for whom we are designing. We will not tolerate design for addiction, deception, or control. We must design tools that we would love our loved ones to use. We must question our intent and listen to our hearts.

Let us move from human-centered design to humanity-centered design.
We are a community that exerts great influence. We must protect and nurture the potential to do good with it. We must do this with attention to inequality, with humility, and with love. In the end, our reward will be to know that we have done everything in our power to leave our garden patch a little greener than we found it….(More)”.

Making a Smart City a Fairer City: Chicago’s Technologists Address Issues of Privacy, Ethics, and Equity, 2011-2018

Curated on August 31, 2018 by Stefaan Verhulst

Case study by Gabriel Kuris and Steven S. Strauss at Innovations for Successful Societies: “In 2011, voters in Chicago elected Rahm Emanuel, a 51-year-old former Chicago congressman, as their new mayor. Emanuel inherited a city on the upswing after years of decline but still marked by high rates of crime and poverty, racial segregation, and public distrust in government. The Emanuel administration hoped to harness the city’s trove of digital data to improve Chicagoans’ health, safety, and quality of life. During the next several years, Chief Data Officer Brett Goldstein and his successor Tom Schenk led innovative uses of city data, ranging from crisis management to the statistical targeting of restaurant inspections and pest extermination. As their teams took on more-sophisticated projects that predicted lead-poisoning risks and Escherichia coli outbreaks and created a citywide network of ambient sensors, the two faced new concerns about normative issues like privacy, ethics, and equity. By 2018, Chicago had won acclaim as a smarter city, but was it a fairer city? This case study discusses some of the approaches the city developed to address those challenges and manage the societal implications of cutting-edge technologies….(More)”.

Protecting the Confidentiality of America’s Statistics: Adopting Modern Disclosure Avoidance Methods at the Census Bureau

Curated on August 31, 2018 by Stefaan Verhulst

John Abowd at US Census: “…Throughout our history, we have been leaders in statistical data protection, which we call disclosure avoidance. Other statistical agencies use the terms “disclosure limitation” and “disclosure control.” These terms are all synonymous. Disclosure avoidance methods have evolved since the censuses of the early 1800s, when the only protection used was simply removing names. Executive orders, and a series of laws modified the legal basis for these protections, which were finally codified in the 1954 Census Act (13 U.S.C. Sections 8(b) and 9). We have continually added better and stronger protections to keep the data we publish anonymous and underlying records confidential.

However, historical methods cannot completely defend against the threats posed by today’s technology. Growth in computing power, advances in mathematics, and easy access to large, public databases pose a significant threat to confidentiality. These forces have made it possible for sophisticated users to ferret out common data points between databases using only our published statistics. If left unchecked, those users might be able to stitch together these common threads to identify the people or businesses behind the statistics as was done in the case of the Netflix Challenge.

The Census Bureau has been addressing these issues from every feasible angle and changing rapidly with the times to ensure that we protect the data our census and survey respondents provide us. We are doing this by moving to a new, advanced, and far more powerful confidentiality protection system, which uses a rigorous mathematical process that protects respondents’ information and identity in all of our publications.

The new tool is based on the concept known in scientific and academic circles as “differential privacy.” It is also called “formal privacy” because it provides provable mathematical guarantees, similar to those found in modern cryptography, about the confidentiality protections that can be independently verified without compromising the underlying protections.

“Differential privacy” is based on the cryptographic principle that an attacker should not be able to learn any more about you from the statistics we publish using your data than from statistics that did not use your data. After tabulating the data, we apply carefully constructed algorithms to modify the statistics in a way that protects individuals while continuing to yield accurate results. We assume that everyone’s data are vulnerable and provide the same strong, state-of-the-art protection to every record in our database.

The Census Bureau did not invent the science behind differential privacy. However, we were the first organization anywhere to use it when we incorporated differential privacy into the OnTheMap application in 2008. It was used in this event to protect block-level residential population data. Recently, Google, Apple, Microsoft, and Uber have all followed the Census Bureau’s lead, adopting differentially privacy systems as the standard for protecting user data confidentiality inside their browsers (Chrome), products (iPhones), operating systems (Windows 10), and apps (Uber)….(More)”.

Origin Privacy: Protecting Privacy in the Big-Data Era

Curated on August 30, 2018 by Stefaan Verhulst

Paper by Helen Nissenbaum, Sebastian Benthall, Anupam Datta, Michael Carl Tschantz, and Piot Mardziel: “Machine learning over big data poses challenges for our conceptualization of privacy. Such techniques can discover surprising and counteractive associations that take innocent looking data and turns it into important inferences about a person. For example, the buying carbon monoxide monitors has been linked to paying credit card bills, while buying chrome-skull car accessories predicts not doing so. Also, Target may have used the buying of scent-free hand lotion and vitamins as a sign that the buyer is pregnant. If we take pregnancy status to be private and assume that we should prohibit the sharing information that can reveal that fact, then we have created an unworkable notion of privacy, one in which sharing any scrap of data may violate privacy.

Prior technical specifications of privacy depend on the classification of certain types of information as private or sensitive; privacy policies in these frameworks limit access to data that allow inference of this sensitive information. As the above examples show, today’s data rich world creates a new kind of problem: it is difficult if not impossible to guarantee that information does notallow inference of sensitive topics. This makes information flow rules based on information topic unstable.

We address the problem of providing a workable definition of private data that takes into account emerging threats to privacy from large-scale data collection systems. We build on Contextual Integrity and its claim that privacy is appropriate information flow, or flow according to socially or legally specified rules.

As in other adaptations of Contextual Integrity (CI) to computer science, the parameterization of social norms in CI is translated into a logical specification. In this work, we depart from CI by considering rules that restrict information flow based on its origin and provenance, instead of on it’s type, topic, or subject.

We call this concept of privacy as adherence to origin-based rules Origin Privacy. Origin Privacy rules can be found in some existing data protection laws. This motivates the computational implementation of origin-based rules for the simple purpose of compliance engineering. We also formally model origin privacy to determine what security properties it guarantees relative to the concerns that motivate it….(More)”.

Sharing the benefits: How to use data effectively in the public sector

Curated on August 29, 2018May 29, 2019 by Stefaan Verhulst

Report by Sarah Timmis, Luke Heselwood and Eleonora Harwich (for Reform UK): “This report demonstrates the potential of data sharing to transform the delivery of public services and improve outcomes for citizens. It explores how government can overcome various challenges to ‘get data right’ and enable better use of personal data within and between public-sector organisations.

Ambition meets reality

Government is set on using data more effectively to help deliver better public services. Better use of data can improve the design, efficiency and outcomes of services. For example, sharing data digitally between GPs and hospitals can enable early identification of patients most at risk of hospital admission, which has reduced admissions by up to 30 per cent in Somerset. Bristol’s Homeless Health Service allows access to medical, psychiatric, social and prison data, helping to provide a clearer picture of the complex issues facing the city’s homeless population. However, government has not yet created a clear data infrastructure, which would allow data to be shared across multiple public services, meaning efforts on the ground have not always delivered results.

The data: sticking points

Several technical challenges must be overcome to create the right data infrastructure. Individual pieces of data must be presented in standard formats to enable sharing within and across services. Data quality can be improved at the point of data collection, through better monitoring of data quality and standards within public-sector organisations and through data-curation-processes. Personal data also needs to be presented in a given format so linking data is possible in certain instances to identify individuals. Interoperability issues and legacy systems act as significant barriers to data linking. The London Metropolitan Police alone use 750 different systems, many of which are incompatible. Technical solutions, such as Application Programming Interfaces (APIs) can be overlaid on top of legacy systems to improve interoperability and enable data sharing. However, this is only possible with the right standards and a solid new data model. To encourage competition and improve interoperability in the longer term, procurement rules should make interoperability a prerequisite for competing companies, allowing customers to integrate their choices of the most appropriate products from different vendors.

Building trustworthiness

The ability to share data at scale through the internet has brought new threats to the security and privacy of personal information that amplifies the need for trust between government and citizens and across government departments. Currently, just 9 per cent of people feel that the Government has their best interests at heart when data sharing, and only 15 per cent are confident that government organisations would deal well with a cyber-attack. Considering attitudes towards data sharing are time and context dependent, better engagement with citizens and clearer explanations of when and why data is used can help build confidence. Auditability is also key to help people and organisations track how data is used to ensure every interaction with personal data is auditable, transparent and secure. …(More)”.

Biometric Mirror

Curated on August 29, 2018July 18, 2019 by Stefaan Verhulst

University of Melbourne: “Biometric Mirror exposes the possibilities of artificial intelligence and facial analysis in public space. The aim is to investigate the attitudes that emerge as people are presented with different perspectives on their own, anonymised biometric data distinguished from a single photograph of their face. It sheds light on the specific data that people oppose and approve, the sentiments it evokes, and the underlying reasoning. Biometric Mirror also presents an opportunity to reflect on whether the plausible future of artificial intelligence is a future we want to see take shape.

Big data and artificial intelligence are some of today’s most popular buzzwords. Both are promised to help deliver insights that were previously too complex for computer systems to calculate. With examples ranging from personalised recommendation systems to automatic facial analyses, user-generated data is now analysed by algorithms to identify patterns and predict outcomes. And the common view is that these developments will have a positive impact on society.

Within the realm of artificial intelligence (AI), facial analysis gains popularity. Today, CCTV cameras and advertising screens increasingly link with analysis systems that are able to detect emotions, age, gender and demographic information of people passing by. It has proven to increase advertising effectiveness in retail environments, since campaigns can now be tailored to specific audience profiles and situations. But facial analysis models are also being developed to predict your aggression level, sexual preference, life expectancy and likeliness of being a terrorist (or an academic) by simply monitoring surveillance camera footage or analysing a single photograph. Some of these developments have gained widespread media coverage for their innovative nature, but often the ethical and social impact is only a side thought.

Current technological developments approach ethical boundaries of the artificial intelligence age. Facial recognition and analysis in public space raise concerns as people are photographed without prior consent, and their photos disappear into a commercial operator’s infrastructure. It remains unclear how the data is processed, how the data is tailored for specific purposes and how the data is retained or disposed of. People also do not have the opportunity to review or amend their facial recognition data. Perhaps most worryingly, artificial intelligence systems may make decisions or deliver feedback based on the data, regardless of its accuracy or completeness. While facial recognition and analysis may be harmless for tailored advertising in retail environments or to unlock your phone, it quickly pushes ethical boundaries when the general purpose is to more closely monitor society… (More).

How to Prevent Winner-Takes-All Democracy

Curated on August 28, 2018May 29, 2019 by Stefaan Verhulst

Kaushik Basu at Project Syndicate: “Democracy is in crisis. Fake news – and fake allegations of fake news – now plagues civil discourse, and political parties have proved increasingly willing to use xenophobia and other malign strategies to win elections. At the same time, revisionist powers like Vladimir Putin’s Russia have been stepping up their efforts to interfere in elections across the West. Rarely has the United States witnessed such brazen attacks on its political system; and rarely has the world seen such lows during peacetime….

How can all of this be happening in democracies, and what can be done about it?

On the first question, one hypothesis is that new digital technologies are changing the structural incentives for corporations, political parties, and other major institutions. Consider the case of corporations. The wealth of proprietary data on consumer preferences and behavior is producing such massive returns to scale that a few giants are monopolizing markets. In other words, markets are increasingly geared toward a winner-take-all game: multiple corporations can compete, but to the victor go the spoils.1

Electoral democracy is drifting in the same direction. The benefits of winning an election have become so large that political parties will stoop to new lows to clinch a victory. And, as with corporations, they can do so with the help of data on electoral preferences and behavior, and with new strategies to target key constituencies.

This poses a dilemma for well-meaning democratic parties and politicians. If a “bad” party is willing to foment hate and racism to bolster its chances of winning, what is a “good” party to do? If it sticks to its principles, it could end up ceding victory to the “bad” party, which will do even more harm once it is in office. A “good” party may thus try to forestall that outcome by taking a step down the moral ladder, precipitating a race to the bottom. This is the problem with any winner-takes-all game. When second place confers no benefits, the cost of showing unilateral restraint can grow intolerably high.

But this problem is not as hopeless as it appears. In light of today’s crisis of democracy, we would do well to revisit Václav Havel’s seminal 1978 essay “The Power of the Powerless.” First published as samizdat that was smuggled out of Czechoslovakia, the essay makes a simple but compelling argument. Dictatorships and other seemingly omnipotent forms of authoritarianism may look like large, top-down structures, but in the final analysis, they are merely the outcome of ordinary individuals’ beliefs and choices. Havel did not have the tools of modern economic theory to demonstrate his argument formally. In my new book The Republic of Beliefs, I show that the essence of his argument can be given formal structure using elementary game theory. This, in turn, shows that ordinary individuals have moral options that may be unavailable to the big institutional players….(More)”.