Decoding the Future for National Security

Curated on Posted on by Stefaan Verhulst

George I. Seffers at Signal: “U.S. intelligence agencies are in the business of predicting the future, but no one has systematically evaluated the accuracy of those predictions—until now. The intelligence community’s cutting-edge research and development agency uses a handful of predictive analytics programs to measure and improve the ability to forecast major events, including political upheavals, disease outbreaks, insider threats and cyber attacks.

The Office for Anticipating Surprise at the Intelligence Advanced Research Projects Activity (IARPA) is a place where crystal balls come in the form of software, tournaments and throngs of people. The office sponsors eight programs designed to improve predictive analytics, which uses a variety of data to forecast events. The programs all focus on incidents outside of the United States, and the information is anonymized to protect privacy. The programs are in different stages, some having recently ended as others are preparing to award contracts.

But they all have one more thing in common: They use tournaments to advance the state of the predictive analytic arts. “We decided to run a series of forecasting tournaments in which people from around the world generate forecasts about, now, thousands of real-world events,” says Jason Matheny, IARPA’s new director. “All of our programs on predictive analytics do use this tournament style of funding and evaluating research.” The Open Source Indicators program used a crowdsourcing technique in which people across the globe offered their predictions on such events as political uprisings, disease outbreaks and elections.

The data analyzed included social media trends, Web search queries and even cancelled dinner reservations—an indication that people are sick. “The methods applied to this were all automated. They used machine learning to comb through billions of pieces of data to look for that signal, that leading indicator, that an event was about to happen,” Matheny explains. “And they made amazing progress. They were able to predict disease outbreaks weeks earlier than traditional reporting.” The recently completed Aggregative Contingent Estimation (ACE) program also used a crowdsourcing competition in which people predicted events, including whether weapons would be tested, treaties would be signed or armed conflict would break out along certain borders. Volunteers were asked to provide information about their own background and what sources they used. IARPA also tested participants’ cognitive reasoning abilities. Volunteers provided their forecasts every day, and IARPA personnel kept score. Interestingly, they discovered the “deep domain” experts were not the best at predicting events. Instead, people with a certain style of thinking came out the winners. “They read a lot, not just from one source, but from multiple sources that come from different viewpoints. They have different sources of data, and they revise their judgments when presented with new information. They don’t stick to their guns,” Matheny reveals. …

The ACE research also contributed to a recently released book, Superforecasting: The Art and Science of Prediction, according to the IARPA director. The book was co-authored, along with Dan Gardner, by Philip Tetlock, the Annenberg University professor of psychology and management at the University of Pennsylvania who also served as a principal investigator for the ACE program. Like ACE, the Crowdsourcing Evidence, Argumentation, Thinking and Evaluation program uses the forecasting tournament format, but it also requires participants to explain and defend their reasoning. The initiative aims to improve analytic thinking by combining structured reasoning techniques with crowdsourcing.

Meanwhile, the Foresight and Understanding from Scientific Exposition (FUSE) program forecasts science and technology breakthroughs….(More)”

Sharing Information

Curated on Posted on by Stefaan Verhulst

An Ericsson Consumer Insight Summary Report: “In the age of the internet we often hear how companies, authorities and other organizations get access to our personal information. As a result, the topic of privacy is frequently debated. What is sometimes overlooked is how we as individuals watch in return. We observe not only each other, but also companies and authorities – and we share what we see. Your neighbor searches the net about the family that just moved in next door. The traveler films his hotel and shares the video with other potential holidaymakers. A friend shares her experience about her employer on a social network. As sharing online continues to grow, we are starting to see the impact on both our individual lives and society. In this report we begin to uncover how consumers perceive their influence – but also some issues that arise as a result….

By sharing more information than ever, smartphone owners are increasingly acting like citizen journalists > Over 70 percent of all smartphone users share personal photos regularly. 69 percent share more than they did 2 years ago

> 69 percent also read or watch other people’s shared content more than they did 2 years ago

People report wrongdoings by businesses and authorities online

> 34 percent of smartphone owners who have had bad experiences with companies say they usually share their experiences online. 27 percent repost other consumers’ complaints on a weekly basis

> Over half of smartphone users surveyed believe that being able to express opinions online about companies has increased their influence

Consumers expect shared information to have an effect on society and the world

> 54 percent believe that the internet has increased the possibility for whistleblowers to expose corrupt and illicit behavior in companies and organizations

> Furthermore, 37 percent of smartphone users believe that sharing information about a corrupt company online has greater impact than going to the police

With new power comes new challenges

> 46 percent of smartphone users would like a verification service to check the authenticity of an online posting or news clip

> 64 percent would like to be able to stop negative information about themselves circulating online

> 1 in 2 says protecting personal information should be a priority on the political agenda, although only 1 in 4 says it is not”…(More)”

Meeting the Challenges of Big Data

Curated on Posted on by Stefaan Verhulst

Opinion by the European Data Protection Supervisor: “Big data, if done responsibly, can deliver significant benefits and efficiencies for society and individuals not only in health, scientific research, the environment and other specific areas. But there are serious concerns with the actual and potential impact of processing of huge amounts of data on the rights and freedoms of individuals, including their right to privacy. The challenges and risks of big data therefore call for more effective data protection.

Technology should not dictate our values and rights, but neither should promoting innovation and preserving fundamental rights be perceived as incompatible. New business models exploiting new capabilities for the massive collection, instantaneous transmission, combination and reuse of personal information for unforeseen purposes have placed the principles of data protection under new strains, which calls for thorough consideration on how they are applied.

European data protection law has been developed to protect our fundamental rights and values, including our right to privacy. The question is not whether to apply data protection law to big data, but rather how to apply it innovatively in new environments. Our current data protection principles, including transparency, proportionality and purpose limitation, provide the base line we will need to protect more dynamically our fundamental rights in the world of big data. They must, however, be complemented by ‘new’ principles which have developed over the years such as accountability and privacy by design and by default. The EU data protection reform package is expected to strengthen and modernise the regulatory framework .

The EU intends to maximise growth and competitiveness by exploiting big data. But the Digital Single Market cannot uncritically import the data-driven technologies and business models which have become economic mainstream in other areas of the world. Instead it needs to show leadership in developing accountable personal data processing. The internet has evolved in a way that surveillance – tracking people’s behaviour – is considered as the indispensable revenue model for some of the most successful companies. This development calls for critical assessment and search for other options.

In any event, and irrespective of the business models chosen, organisations that process large volumes of personal information must comply with applicable data protection law. The European Data Protection Supervisor (EDPS) believes that responsible and sustainable development of big data must rely on four essential elements:

  • organisations must be much more transparent about how they process personal data;
  • afford users a higher degree of control over how their data is used;
  • design user friendly data protection into their products and services; and;
  • become more accountable for what they do….(More)

Big Data and Privacy: Emerging Issues

Curated on Posted on by Stefaan Verhulst

O’Leary, Daniel E. at Intelligent Systems, IEEE : “The goals of big data and privacy are fundamentally opposed to each other. Big data and knowledge discovery are aimed reducing information asymmetries between organizations and the data sources, whereas privacy is aimed at maintaining information asymmetries of data sources. A number of different definitions of privacy are used to investigate some of the tensions between different characteristics of big data and potential privacy concerns. Specifically, the author examines the consequences of unevenness in big data, digital data going from local controlled settings to uncontrolled global settings, privacy effects of reputation monitoring systems, and inferring knowledge from social media. In addition, the author briefly analyzes two other emerging sources of big data: police cameras and stingray for location information….(More)”

Analyzing 1.1 Billion NYC Taxi and Uber Trips

Curated on Posted on by Stefaan Verhulst

Todd W. Schneider: “The New York City Taxi & Limousine Commission has released a staggeringly detailed historical dataset covering over 1.1 billion individual taxi trips in the city from January 2009 through June 2015. Taken as a whole, the detailed trip-level data is more than just a vast list of taxi pickup and drop off coordinates: it’s a story of New York. How bad is the rush hour traffic from Midtown to JFK? Where does the Bridge and Tunnel crowd hang out on Saturday nights? What time do investment bankers get to work? How has Uber changed the landscape for taxis? And could Bruce Willis and Samuel L. Jackson have made it from 72nd and Broadway to Wall Street in less than 30 minutes? The dataset addresses all of these questions and many more.

I mapped the coordinates of every trip to local census tracts and neighborhoods, then set about in an attempt to extract stories and meaning from the data. This post covers a lot, but for those who want to pursue more analysis on their own: everything in this post—the data, software, and code—is freely available. Full instructions to download and analyze the data for yourself are available on GitHub.

Table of Contents

  1. Maps
  2. The Data
  3. Borough Trends, and the Rise of Uber
  4. Airport Traffic
  5. On the Realism of Die Hard 3
  6. How Does Weather Affect Taxi and Uber Ridership?
  7. NYC Late Night Taxi Index
  8. The Bridge and Tunnel Crowd
  9. Northside Williamsburg
  10. Privacy Concerns
  11. Investment Bankers
  12. Parting Thoughts…(More)

Open government data: Out of the box

Curated on Posted on by Stefaan Verhulst

The Economist on “The open-data revolution has not lived up to expectations. But it is only getting started…

The app that helped save Mr Rich’s leg is one of many that incorporate government data—in this case, supplied by four health agencies. Six years ago America became the first country to make all data collected by its government “open by default”, except for personal information and that related to national security. Almost 200,000 datasets from 170 outfits have been posted on the data.gov website. Nearly 70 other countries have also made their data available: mostly rich, well-governed ones, but also a few that are not, such as India (see chart). The Open Knowledge Foundation, a London-based group, reckons that over 1m datasets have been published on open-data portals using its CKAN software, developed in 2010.

Questioning Smart Urbanism: Is Data-Driven Governance a Panacea?

Curated on Posted on by Stefaan Verhulst

Alice Tang at the Chicago Policy Review: “In the era of data explosion, urban planners are increasingly relying on real-time, streaming data generated by “smart” devices to assist with city management. “Smart cities,” referring to cities that implement pervasive and ubiquitous computing in urban planning, are widely discussed in academia, business, and government. These cities are characterized not only by their use of technology but also by their innovation-driven economies and collaborative, data-driven city governance. Smart urbanism can seem like an effective strategy to create more efficient, sustainable, productive, and open cities. However, there are emerging concerns about the potential risks in the long-term development of smart cities, including political neutrality of big data, technocratic governance, technological lock-ins, data and network security, and privacy risks.

In a study entitled, “The Real-Time City? Big Data and Smart Urbanism,” Rob Kitchin provides a critical reflection on the potential negative effects of data-driven city governance on social development—a topic he claims deserves greater governmental, academic, and social attention.

In contrast to traditional datasets that rely on samples or are aggregated to a coarse scale, “big data” is huge in volume, high in velocity, and diverse in variety. Since the early 2000s, there has been explosive growth in data volume due to the rapid development and implementation of technology infrastructure, including networks, information management, and data storage. Big data can be generated from directed, automated, and volunteered sources. Automated data generation is of particular interest to urban planners. One example Kitchin cites is urban sensor networks, which allow city governments to monitor the movements and statuses of individuals, materials, and structures throughout the urban environment by analyzing real-time data.

With the huge amount of streaming data collected by smart infrastructure, many city governments use real-time analysis to manage different aspects of city operations. There has been a recent trend in centralizing data streams into a single hub, integrating all kinds of surveillance and analytics. These one-stop data centers make it easier for analysts to cross-reference data, spot patterns, identify problems, and allocate resources. The data are also often accessible by field workers via operations platforms. In London and some other cities, real-time data are visualized on “city dashboards” and communicated to citizens, providing convenient access to city information.

However, the real-time city is not a flawless solution to all the problems faced by city managers. The primary concern is the politics of big, urban data. Although raw data are often perceived as neutral and objective, no data are free of bias; the collection of data is a subjective process that can be shaped by various confounding factors. The presentation of data can also be manipulated to answer a specific question or enact a particular political vision….(More)”

Build digital democracy

Curated on Posted on by Stefaan Verhulst

Dirk Helbing & Evangelos Pournaras in Nature: “Fridges, coffee machines, toothbrushes, phones and smart devices are all now equipped with communicating sensors. In ten years, 150 billion ‘things’ will connect with each other and with billions of people. The ‘Internet of Things’ will generate data volumes that double every 12 hours rather than every 12 months, as is the case now.

Blinded by information, we need ‘digital sunglasses’. Whoever builds the filters to monetize this information determines what we see — Google and Facebook, for example. Many choices that people consider their own are already determined by algorithms. Such remote control weakens responsible, self-determined decision-making and thus society too.

The European Court of Justice’s ruling on 6 October that countries and companies must comply with European data-protection laws when transferring data outside the European Union demonstrates that a new digital paradigm is overdue. To ensure that no government, company or person with sole control of digital filters can manipulate our decisions, we need information systems that are transparent, trustworthy and user-controlled. Each of us must be able to choose, modify and build our own tools for winnowing information.

With this in mind, our research team at the Swiss Federal Institute of Technology in Zurich (ETH Zurich), alongside international partners, has started to create a distributed, privacy-preserving ‘digital nervous system’ called Nervousnet. Nervousnet uses the sensor networks that make up the Internet of Things, including those in smartphones, to measure the world around us and to build a collective ‘data commons’. The many challenges ahead will be best solved using an open, participatory platform, an approach that has proved successful for projects such as Wikipedia and the open-source operating system Linux.

A wise king?

The science of human decision-making is far from understood. Yet our habits, routines and social interactions are surprisingly predictable. Our behaviour is increasingly steered by personalized advertisements and search results, recommendation systems and emotion-tracking technologies. Thousands of pieces of metadata have been collected about every one of us (seego.nature.com/stoqsu). Companies and governments can increasingly manipulate our decisions, behaviour and feelings1.

Many policymakers believe that personal data may be used to ‘nudge’ people to make healthier and environmentally friendly decisions. Yet the same technology may also promote nationalism, fuel hate against minorities or skew election outcomes2 if ethical scrutiny, transparency and democratic control are lacking — as they are in most private companies and institutions that use ‘big data’. The combination of nudging with big data about everyone’s behaviour, feelings and interests (‘big nudging’, if you will) could eventually create close to totalitarian power.

Countries have long experimented with using data to run their societies. In the 1970s, Chilean President Salvador Allende created computer networks to optimize industrial productivity3. Today, Singapore considers itself a data-driven ‘social laboratory’4 and other countries seem keen to copy this model.

The Chinese government has begun rating the behaviour of its citizens5. Loans, jobs and travel visas will depend on an individual’s ‘citizen score’, their web history and political opinion. Meanwhile, Baidu — the Chinese equivalent of Google — is joining forces with the military for the ‘China brain project’, using ‘deep learning’ artificial-intelligence algorithms to predict the behaviour of people on the basis of their Internet activity6.

The intentions may be good: it is hoped that big data can improve governance by overcoming irrationality and partisan interests. But the situation also evokes the warning of the eighteenth-century philosopher Immanuel Kant, that the “sovereign acting … to make the people happy according to his notions … becomes a despot”. It is for this reason that the US Declaration of Independence emphasizes the pursuit of happiness of individuals.

Ruling like a ‘benevolent dictator’ or ‘wise king’ cannot work because there is no way to determine a single metric or goal that a leader should maximize. Should it be gross domestic product per capita or sustainability, power or peace, average life span or happiness, or something else?

Better is pluralism. It hedges risks, promotes innovation, collective intelligence and well-being. Approaching complex problems from varied perspectives also helps people to cope with rare and extreme events that are costly for society — such as natural disasters, blackouts or financial meltdowns.

Centralized, top-down control of data has various flaws. First, it will inevitably become corrupted or hacked by extremists or criminals. Second, owing to limitations in data-transmission rates and processing power, top-down solutions often fail to address local needs. Third, manipulating the search for information and intervening in individual choices undermines ‘collective intelligence’7. Fourth, personalized information creates ‘filter bubbles’8. People are exposed less to other opinions, which can increase polarization and conflict9.

Fifth, reducing pluralism is as bad as losing biodiversity, because our economies and societies are like ecosystems with millions of interdependencies. Historically, a reduction in diversity has often led to political instability, collapse or war. Finally, by altering the cultural cues that guide peoples’ decisions, everyday decision-making is disrupted, which undermines rather than bolsters social stability and order.

Big data should be used to solve the world’s problems, not for illegitimate manipulation. But the assumption that ‘more data equals more knowledge, power and success’ does not hold. Although we have never had so much information, we face ever more global threats, including climate change, unstable peace and socio-economic fragility, and political satisfaction is low worldwide. About 50% of today’s jobs will be lost in the next two decades as computers and robots take over tasks. But will we see the macroeconomic benefits that would justify such large-scale ‘creative destruction’? And how can we reinvent half of our economy?

The digital revolution will mainly benefit countries that achieve a ‘win–win–win’ situation for business, politics and citizens alike10. To mobilize the ideas, skills and resources of all, we must build information systems capable of bringing diverse knowledge and ideas together. Online deliberation platforms and reconfigurable networks of smart human minds and artificially intelligent systems can now be used to produce collective intelligence that can cope with the diverse and complex challenges surrounding us….(More)” See Nervousnet project

Building Trust and Protecting Privacy: Progress on the President’s Precision Medicine Initiative

Curated on Posted on by Stefaan Verhulst

The White House: “Today, the White House is releasing the Privacy and Trust Principles for the President’s Precision Medicine Initiative (PMI). These principles are a foundation for protecting participant privacy and building trust in activities within PMI.

PMI is a bold new research effort to transform how we characterize health and treat disease. PMI will pioneer a new model of patient-powered research that promises to accelerate biomedical discoveries and provide clinicians with new tools, knowledge, and therapies to select which treatments will work best for which patients. The initiative includes development of a new voluntary research cohort by the National Institutes of Health (NIH), a novel regulatory approach to genomic technologies by the Food and Drug Administration, and new cancer clinical trials by the National Cancer Institute at NIH.  In addition, PMI includes aligned efforts by the Federal government and private sector collaborators to pioneer a new approach for health research and healthcare delivery that prioritizes patient empowerment through access to information and policies that enable safe, effective, and innovative technologies to be tested and made available to the public.

Following President Obama’s launch of PMI in January 2015, the White House Office of Science and Technology Policy worked with an interagency group to develop the Privacy and Trust Principles that will guide the Precision Medicine effort. The White House convened experts from within and outside of government over the course of many months to discuss their individual viewpoints on the unique privacy challenges associated with large-scale health data collection, analysis, and sharing. This group reviewed the bioethics literature, analyzed privacy policies for large biobanks and research cohorts, and released a draft set of Principles for public comment in July 2015…..

The Privacy and Trust Principles are organized into 6 broad categories:

  1. Governance that is inclusive, collaborative, and adaptable;
  2. Transparency to participants and the public;
  3. Respecting participant preferences;
  4. Empowering participants through access to information;
  5. Ensuring appropriate data sharing, access, and use;
  6. Maintaining data quality and integrity….(More)”

Privacy in a Digital, Networked World: Technologies, Implications and Solutions

Curated on Posted on by Stefaan Verhulst

Book edited by Zeadally, Sherali and Badra, Mohamad: “This comprehensive textbook/reference presents a focused review of the state of the art in privacy research, encompassing a range of diverse topics. The first book of its kind designed specifically to cater to courses on privacy, this authoritative volume provides technical, legal, and ethical perspectives on privacy issues from a global selection of renowned experts. Features: examines privacy issues relating to databases, P2P networks, big data technologies, social networks, and digital information networks; describes the challenges of addressing privacy concerns in various areas; reviews topics of privacy in electronic health systems, smart grid technology, vehicular ad-hoc networks, mobile devices, location-based systems, and crowdsourcing platforms; investigates approaches for protecting privacy in cloud applications; discusses the regulation of personal information disclosure and the privacy of individuals; presents the tools and the evidence to better understand consumers’ privacy behaviors….(More)”