Politics and Technology in the Post-Truth Era

Curated on Posted on by Stefaan Verhulst

Book edited by Anna Visvizi and Miltiadis D. Lytras: “Advances in information and communication technology (ICT) have directly impacted the way in which politics operates today. Bringing together research on Europe, the US, South America, the Middle East, Asia and Africa, this book examines the relationship between ICT and politics in a global perspective.

Technological innovations such as big data, data mining, sentiment analysis, cognitive computing, artificial intelligence, virtual reality, augmented reality, social media and blockchain technology are reshaping the way ICT intersects with politics and in this collection contributors examine these developments, demonstrating their impact on the political landscape. Chapters examine topics such as cyberwarfare and propaganda, post-Soviet space, Snowden, US national security, e-government, GDPR, democratization in Africa and internet freedom.


Providing an overview of new research on the emerging relationship between the promise and potential inherent in ICT and its impact on politics, this edited collection will prove an invaluable text for students, researchers and practitioners working in the fields of Politics, International Relations and Computer Science…..(More)”

Introducing the Contractual Wheel of Data Collaboration

Curated on Posted on by Stefaan Verhulst

Blog by Andrew Young and Stefaan Verhulst: “Earlier this year we launched the Contracts for Data Collaboration (C4DC) initiative — an open collaborative with charter members from The GovLab, UN SDSN Thematic Research Network on Data and Statistics (TReNDS), University of Washington and the World Economic Forum. C4DC seeks to address the inefficiencies of developing contractual agreements for public-private data collaboration by informing and guiding those seeking to establish a data collaborative by developing and making available a shared repository of relevant contractual clauses taken from existing legal agreements. Today TReNDS published “Partnerships Founded on Trust,” a brief capturing some initial findings from the C4DC initiative.

The Contractual Wheel of Data Collaboration [beta]

The Contractual Wheel of Data Collaboration [beta] — Stefaan G. Verhulst and Andrew Young, The GovLab

As part of the C4DC effort, and to support Data Stewards in the private sector and decision-makers in the public and civil sectors seeking to establish Data Collaboratives, The GovLab developed the Contractual Wheel of Data Collaboration [beta]. The Wheel seeks to capture key elements involved in data collaboration while demystifying contracts and moving beyond the type of legalese that can create confusion and barriers to experimentation.

The Wheel was developed based on an assessment of existing legal agreements, engagement with The GovLab-facilitated Data Stewards Network, and analysis of the key elements of our Data Collaboratives Methodology. It features 22 legal considerations organized across 6 operational categories that can act as a checklist for the development of a legal agreement between parties participating in a Data Collaborative:…(More)”.

Drones to deliver medicines to 12m people in Ghana

Curated on Posted on by Stefaan Verhulst

Neil Munshi in the Financial Times: “The world’s largest drone delivery network, ferrying 150 different medicines and vaccines, as well as blood, to 2,000 clinics in remote parts of Ghana, is set to be announced on Wednesday.

The network represents a big expansion for the Silicon Valley start-up Zipline, which began delivering blood in Rwanda in 2016 using pilotless, preprogrammed aircraft. The move, along with a new agreement in Rwanda signed in December, takes the company beyond simple blood distribution to more complicated vaccine and plasma deliveries.

“What this is going to show is that you can reach every GPS co-ordinate, you can serve everybody,” said Keller Rinaudo, Zipline chief executive. “Every human in that region or country [can be] within a 15-25 minute delivery of any essential medical product — it’s a different way of thinking about universal coverage.”

Zipline will deliver vaccines for yellow fever, polio, diptheria and tetanus which are provided by the World Health Organisation’s Expanded Project on Immunisation. The WHO will also use the company’s system for future mass immunisation programmes in Ghana.

Later this year, Zipline has plans to start operations in the US, in North Carolina, and in south-east Asia. The company said it will be able to serve 100m people within a year, up from the 22m that its projects in Ghana and Rwanda will cover.

In Ghana, Zipline said health workers will receive deliveries via a parachute drop within about 30 minutes of placing their orders by text message….(More)”.

Technology-facilitated Societal Consensus

Curated on Posted on by Stefaan Verhulst

Paper by Timotheus Kampik and Amro Najjar: “The spread of radical opinions, facilitated by homophilic Internet communities (echo chambers), has become a threat to the stability of societies around the globe. The concept of choice architecture–the design of choice information for consumers with the goal of facilitating societally beneficial decisions–provides a promising (although not uncontroversial) general concept to address this problem.

The choice architecture approach is reflected in recent proposals advocating for recommender systems that consider the societal impact of their recommendations and not only strive to optimize revenue streams.

However, the precise nature of the goal state such systems should work towards remains an open question. In this paper, we suggest that this goal state can be defined by considering target opinion spread in a society on different topics of interest as a multivariate normal distribution; i.e., while there is a diversity of opinions, most people have similar opinions on most topics. We explain why this approach is promising, and list a set of crossdisciplinary research challenges that need to be solved to advance the idea….(More)”.

How Recommendation Algorithms Run the World

Curated on Posted on by Stefaan Verhulst

Article by Zeynep Tufekci: “What should you watch? What should you read? What’s news? What’s trending? Wherever you go online, companies have come up with very particular, imperfect ways of answering these questions. Everywhere you look, recommendation engines offer striking examples of how values and judgments become embedded in algorithms and how algorithms can be gamed by strategic actors.

Consider a common, seemingly straightforward method of making suggestions: a recommendation based on what people “like you” have read, watched, or shopped for. What exactly is a person like me? Which dimension of me? Is it someone of the same age, gender, race, or location? Do they share my interests? My eye color? My height? Or is their resemblance to me determined by a whole mess of “big data” (aka surveillance) crunched by a machine-learning algorithm?

Deep down, behind every “people like you” recommendation is a computational method for distilling stereotypes through data. Even when these methods work, they can help entrench the stereotypes they’re mobilizing. They might easily recommend books about coding to boys and books about fashion to girls, simply by tracking the next most likely click. Of course, that creates a feedback cycle: If you keep being shown coding books, you’re probably more likely to eventually check one out.

Another common method for generating recommendations is to extrapolate from patterns in how people consume things. People who watched this then watched that; shoppers who purchased this item also added that one to their shopping cart. Amazon uses this method a lot, and I admit, it’s often quite useful. Buy an electric toothbrush? How nice that the correct replacement head appears in your recommendations. Congratulations on your new vacuum cleaner: Here are some bags that fit your machine.

But these recommendations can also be revealing in ways that are creepy. …

One final method for generating recommendations is to identify what’s “trending” and push that to a broader user base. But this, too, involves making a lot of judgments….(More)”.

The Importance of Data Access Regimes for Artificial Intelligence and Machine Learning

Curated on Posted on by Stefaan Verhulst

JRC Digital Economy Working Paper by Bertin Martens: “Digitization triggered a steep drop in the cost of information. The resulting data glut created a bottleneck because human cognitive capacity is unable to cope with large amounts of information. Artificial intelligence and machine learning (AI/ML) triggered a similar drop in the cost of machine-based decision-making and helps in overcoming this bottleneck. Substantial change in the relative price of resources puts pressure on ownership and access rights to these resources. This explains pressure on access rights to data. ML thrives on access to big and varied datasets. We discuss the implications of access regimes for the development of AI in its current form of ML. The economic characteristics of data (non-rivalry, economies of scale and scope) favour data aggregation in big datasets. Non-rivalry implies the need for exclusive rights in order to incentivise data production when it is costly. The balance between access and exclusion is at the centre of the debate on data regimes. We explore the economic implications of several modalities for access to data, ranging from exclusive monopolistic control to monopolistic competition and free access. Regulatory intervention may push the market beyond voluntary exchanges, either towards more openness or reduced access. This may generate private costs for firms and individuals. Society can choose to do so if the social benefits of this intervention outweigh the private costs.

We briefly discuss the main EU legal instruments that are relevant for data access and ownership, including the General Data Protection Regulation (GDPR) that defines the rights of data subjects with respect to their personal data and the Database Directive (DBD) that grants ownership rights to database producers. These two instruments leave a wide legal no-man’s land where data access is ruled by bilateral contracts and Technical Protection Measures that give exclusive control to de facto data holders, and by market forces that drive access, trade and pricing of data. The absence of exclusive rights might facilitate data sharing and access or it may result in a segmented data landscape where data aggregation for ML purposes is hard to achieve. It is unclear if incompletely specified ownership and access rights maximize the welfare of society and facilitate the development of AI/ML…(More)”

Crowdsourcing in medical research: concepts and applications

Curated on Posted on by Stefaan Verhulst

Paper by Joseph D. Tucker​, Suzanne Day, Weiming Tang, and Barry Bayus: “Crowdsourcing shifts medical research from a closed environment to an open collaboration between the public and researchers. We define crowdsourcing as an approach to problem solving which involves an organization having a large group attempt to solve a problem or part of a problem, then sharing solutions. Crowdsourcing allows large groups of individuals to participate in medical research through innovation challenges, hackathons, and related activities. The purpose of this literature review is to examine the definition, concepts, and applications of crowdsourcing in medicine.

This multi-disciplinary review defines crowdsourcing for medicine, identifies conceptual antecedents (collective intelligence and open source models), and explores implications of the approach. Several critiques of crowdsourcing are also examined. Although several crowdsourcing definitions exist, there are two essential elements: (1) having a large group of individuals, including those with skills and those without skills, propose potential solutions; (2) sharing solutions through implementation or open access materials. The public can be a central force in contributing to formative, pre-clinical, and clinical research. A growing evidence base suggests that crowdsourcing in medicine can result in high-quality outcomes, broad community engagement, and more open science….(More)”

Five myths about whistleblowers

Curated on Posted on by Stefaan Verhulst

Dana Gold in the Washington Post: “When a whistleblower revealed the Trump administration’s decision to overturn 25 security clearance denials, it was the latest in a long and storied history of insiders exposing significant abuses of public trust. Whistles were blown on U.S. involvement in Vietnam, the Watergate coverup, Enron’s financial fraud, the National Security Agency’s mass surveillance of domestic electronic communications and, during the Trump administration, the corruption of former Environmental Protection Agency chief Scott Pruitt , Cambridge Analytica’s theft of Facebook users’ data to develop targeted political ads, and harm to children posed by the “zero tolerance” immigration policy. Despite the essential role whistleblowers play in illuminating the truth and protecting the public interest, several myths persist about them, some pernicious.

MYTH NO. 1 Whistleblowers are employees who report problems externally….

MYTH NO. 2 Whistleblowers are either disloyal or heroes….

MYTH NO. 3 ‘Leaker’ is another term for ‘whistleblower.’…

MYTH NO. 4 Remaining anonymous is the best strategy for whistleblowing….

MYTH NO. 5 Julian Assange is a whistleblower….(More)”.

Illuminating Big Data will leave governments in the dark

Curated on Posted on by Stefaan Verhulst

Robin Wigglesworth in the Financial Times: “Imagine a world where interminable waits for backward-looking, frequently-revised economic data seem as archaically quaint as floppy disks, beepers and a civil internet. This fantasy realm may be closer than you think.

The Bureau of Economic Analysis will soon publish its preliminary estimate for US economic growth in the first three months of the year, finally catching up on its regular schedule after a government shutdown paralysed the agency. But other data are still delayed, and the final official result for US gross domestic product won’t be available until July. Along the way there are likely to be many tweaks.

Collecting timely and accurate data are a Herculean task, especially for an economy as vast and varied as the US’s. But last week’s World Bank-International Monetary Fund’s annual spring meetings offered some clues on a brighter, more digital future for economic data.

The IMF hosted a series of seminars and discussions exploring how the hot new world of Big Data could be harnessed to produce more timely economic figures — and improve economic forecasts. Jiaxiong Yao, an IMF official in its African department, explained how it could use satellites to measure the intensity of night-time lights, and derive a real-time gauge of economic health.

“If a country gets brighter over time, it is growing. If it is getting darker then it probably needs an IMF programme,” he noted. Further sessions explored how the IMF could use machine learning — a popular field of artificial intelligence — to improve its influential but often faulty economic forecasts; and real-time shipping data to map global trade flows.

Sophisticated hedge funds have been mining some of these new “alternative” data sets for some time, but statistical agencies, central banks and multinational organisations such as the IMF and the World Bank are also starting to embrace the potential.

The amount of digital data around the world is already unimaginably vast. As more of our social and economic activity migrates online, the quantity and quality is going to increase exponentially. The potential is mind-boggling. Setting aside the obvious and thorny privacy issues, it is likely to lead to a revolution in the world of economic statistics. …

Yet the biggest issues are not the weaknesses of these new data sets — all statistics have inherent flaws — but their nature and location.

Firstly, it depends on the lax regulatory and personal attitudes towards personal data continuing, and there are signs of a (healthy) backlash brewing.

Secondly, almost all of this alternative data is being generated and stored in the private sector, not by government bodies such as the Bureau of Economic Analysis, Eurostat or the UK’s Office for National Statistics.

Public bodies are generally too poorly funded to buy or clean all this data themselves, meaning hedge funds will benefit from better economic data than the broader public. We might, in fact, need legislation mandating that statistical agencies receive free access to any aggregated private sector data sets that might be useful to their work.

That would ensure that our economic officials and policymakers don’t fly blind in an increasingly illuminated world….(More)”.

Synthetic data: innovation for public good

Curated on Posted on by Stefaan Verhulst

Blog Post by Catrin Cheung: “What is synthetic data, and how can it be used for public good? ….Synthetic data are artificially generated data that have the look and structure of real data, but do not contain any information on individuals. They also contain more general characteristics that are used to find patterns in the data.

They are modelled on real data, but designed in a way which safeguards the legal, ethical and confidentiality requirements of the original data. Given their resemblance to the original data, synthetic data are useful in a range of situations, for example when data is sensitive or missing. They are used widely as teaching materials, to test code or mathematical models, or as training data for machine learning models….

There’s currently a wealth of research emerging from the health sector, as the nature of data published is often sensitive. Public Health England have synthesised cancer data which can be freely accessed online. NHS Scotland are making advances in cutting-edge machine learning methods such as Variational Auto Encoders and Generative Adversarial Networks (GANs).

There is growing interest in this area of research, and its influence extends beyond the statistical community. While the Data Science Campus have also used GANs to generate synthetic data in their latest research, its power is not limited to data generation. It can be trained to construct features almost identical to our own across imagery, music, speech and text. In fact, GANs have been used to create a painting of Edmond de Belamy, which sold for $432,500 in 2018!

Within the ONS, a pilot to create synthetic versions of securely held Labour Force Survey data has been carried out using a package in R called “synthpop”. This synthetic dataset can be shared with approved researchers to de-bug codes, prior to analysis of data held in the Secure Research Service….

Although much progress is done in this field, one challenge that persists is guaranteeing the accuracy of synthetic data. We must ensure that the statistical properties of synthetic data match properties of the original data.

Additional features, such as the presence of non-numerical data, add to this difficult task. For example, if something is listed as “animal” and can take the possible values “dog”,”cat” or “elephant”, it is difficult to convert this information into a format suitable for precise calculations. Furthermore, given that datasets have different characteristics, there is no straightforward solution that can be applied to all types of data….particular focus was also placed on the use of synthetic data in the field of privacy, following from the challenges and opportunities identified by the National Statistician’s Quality Review of privacy and data confidentiality methods published in December 2018….(More)”.