Data Collaboratives: exchanging data to create public value across Latin America and the Caribbean


Stefaan Verhulst, Andrew Young and Prianka Srinivasan at IADB’s Abierto al Publico: “Data is playing an ever-increasing role in bolstering businesses across Latin America – and the rest of the word. In Brazil, Mexico and Colombia alone, the revenue from Big Data is calculated at more than US$603.7 million, a market that is only set to increase as more companies across Latin America and the Caribbean embrace data-driven strategies to enhance their bottom-line. Brazilian banking giant Itau plans to create six data centers across the country, and already uses data collected from consumers online to improve cross-selling techniques and streamline their investments. Data from web-clicks, social media profiles, and telecommunication services is fueling a new generation of entrepreneurs keen to make big dollars from big data.

What if this same data could be used not just to improve business, but to improve the collective well-being of our communities, public spaces, and cities? Analysis of social media data can offer powerful insights to city officials into public trends and movements to better plan infrastructure and policies. Public health officials and humanitarian workers can use mobile phone data to, for instance, map human mobility and better target their interventions. By repurposing the data collected by companies for their business interests, governments, international organizations and NGOs can leverage big data insights for the greater public good.

Key question is thus: How to unlock useful data collected by corporations in a responsible manner and ensure its vast potential does not go to waste?

Data Collaboratives” are emerging as a possible answer. Data collaboratives are a new type of public-private partnerships aimed at creating public value by exchanging data across sectors.

Research conducted by the GovLab finds that Data Collaboratives offer several potential benefits across a number of sectors, including humanitarian and anti-poverty efforts, urban planning, natural resource stewardship, health, and disaster management. As a greater number of companies in Latin America look to data to spur business interests, our research suggests that some companies are also sharing and collaborating around data to confront some of society’s most pressing problems.

Consider the following Data Collaboratives that seek to enhance…(More)”

Trust in Social Dilemmas


Book edited by Paul A.M. Van Lange, Bettina Rockenbach, and Toshio Yamagishi: “One of the key scientific challenges is the puzzle of human cooperation. Why do people cooperate with one another? What causes individuals to lend a helping hand to a stranger, even if it comes at a major cost to their own well-being? Why do people severely punish those who violate social norms and undermine the collective interest? Edited by Paul A.M. Van Lange, Bettina Rockenbach, and Toshio Yamagishi, Trust in Social Dilemmas carefully considers the role of trust in establishing, promoting, and maintaining overall human cooperation.

By exploring the impact of trust and effective cooperation on relationships, organizations, and communities, Trust in Social Dilemmas draws inspiration from the fact that social dilemmas, defined in terms of conflicts between self-interest and the collective interest, are omnipresent in today’s society. In capturing the breadth and relevance of trust to social dilemmas and human cooperation more generally, this book is structured in three effective parts for readers: the biology and development of trust; the importance of trust for groups and organizations; and how trust factors across the overall health of today’s society.

As Van Lange, Rockenbach, Yamagishi, and their team of expert contributors all explore in this compelling new volume, there is little doubt that trust and cooperation are intimately related in most – if not all – of our social dilemmas….(More)”.

Policymakers around the world are embracing behavioural science


The Economist: “In 2013 thousands of school pupils in England received a letter from a student named Ben at the University of Bristol. The recipients had just gained good marks in their GCSEs, exams normally taken at age 16. But they attended schools where few pupils progressed to university at age 18, and those that did were likely to go to their nearest one. That suggested the schools were poor at nurturing aspiration. In his letter Ben explained that employers cared about the reputation of the university a job applicant has attended. He pointed out that top universities can be a cheaper option for poorer pupils, because they give more financial aid. He added that he had not known these facts at the recipient’s age.

The letters had the effect that was hoped for. A study published in March found that after leaving school, the students who received both Ben’s letter and another, similar one some months later were more likely to be at a prestigious university than those who received just one of the letters, and more likely again than those who received none. For each extra student in a better university, the initiative cost just £45 ($58), much less than universities’ own attempts to broaden their intake. And the approach was less heavy-handed than imposing quotas for poorer pupils, an option previous governments had considered. The education department is considering rolling out the scheme….

Some critics feared that nudges would do little good, and that their effects would fade over time. Others warned that governments were straying perilously close to mass manipulation. More recently, some of the findings on which the behavioural sciences rest have been questioned, as researchers in many fields have sought to replicate famous results, and failed.

By and large those doubts have been allayed. Even if specific results turn out to be mistaken, an experimental, iterative, data-driven approach to policymaking is gaining ground in many places, not just in dedicated units, but throughout government.

Nudging is hardly new. “In Genesis, Satan nudged, and Eve did too,” writes Cass Sunstein of Harvard University. From the middle of the 20th century psychologists such as Stanley Milgram and Philip Zimbardo showed how sensitive humans are to social pressure. Daniel Kahneman and Amos Tversky described the mental shortcuts and biases that influence decision-making. Dale Carnegie and Robert Cialdini wrote popular books on persuasion. Firms, especially in technology, retail and advertising, used behavioural science to shape brand perception and customer behaviour—and, ultimately, to sell more stuff.

But governments’ use of psychological insights to achieve policy goals was occasional and unsystematic. According to David Halpern, the boss of BIT, as far as policymakers were concerned, psychology was “the sickly sibling to economics”. That began to change after Mr Sunstein and Richard Thaler, an economist, published “Nudge”, in 2008. The book attacked the assumption of rational decision-making inherent in most economic models and showed how “choice architecture”, or context, could be changed to “nudge” people to make better choices…..

Now many governments are turning to nudges to save money and do better. In 2014 the White House opened the Social and Behavioural Sciences Team. A report that year by Mark Whitehead of Aberystwyth University counted 51 countries in which “centrally directed policy initiatives” were influenced by behavioural sciences. Non-profit organisations such as Ideas42, set up in 2008 at Harvard University, help run dozens of nudge-style trials and programmes around the world. In 2015 the World Bank set up a group that is now applying behavioural sciences in 52 poor countries. The UN is turning to nudging to help hit the “sustainable development goals”, a list of targets it has set for 2030….

Among the most effective nudges are “social” ones: those that communicate norms or draw on people’s networks. A scheme tested in Guatemala with help from the World Bank and BIT tweaked the wording of letters sent to people and firms who had failed to submit tax returns the previous year. The letters that framed non-payment as an active choice, or noted that paying up is more common than evasion, cut the number of non-payers in the following year and increased the average sum paid. And a trial involving diabetes shows that it matters to nudge at the right moment. In 2014 Hamad Medical Corporation, a health-care provider in Qatar, raised take-up rates for diabetes screening by offering it during Ramadan. That meant most Qataris were fasting, so the need to do so before the test imposed no extra burden….(More)”.

What next for digital social innovation? Realising the potential of people and technology to tackle social challenges


Matt Stokes et al at nesta: “This report, and accompanying guide, produced as part of the DSI4EU project, maps the projects and organisations using technology to tackle social challenges across Europe, and explores the barriers to the growth of digital social innovation.

Key findings

  • There are almost 2,000 organisations and over 1,000 projects involved in digital social innovation (DSI) across Europe, with the highest concentration of activity in Western and Southern Europe.
  • Despite this activity, there are relatively few examples of DSI initiatives delivering impact at scale. The growth of DSI is being held back by barriers at the system level and at the level of individual projects.
  • Projects and organisations involved in DSI are still relatively poorly connected to each other. There is a pressing need to grow strong networks within and across countries and regions to boost collaboration and knowledge-sharing.
  • The growth of DSI is being held back by lack of funding and investment across the continent, especially outside Western Europe, and structural digital skills shortages.
  • Civil society organisations and the public sector have been slow to adopt DSI, despite the opportunity it offers them to deliver better services at a lower cost, although there are emerging examples of good practice from across Europe.
  • Practitioners struggle to engage citizens and users, understand and measure the impact of their digital social innovations, and plan for growth and sustainability.

Across Europe, thousands of people, projects and organisations are using digital technologies to tackle social challenges in fields like healthcare, education, employment, democratic participation, migration and the environment. We call this phenomenon digital social innovation.

Through crowdmapping DSI across Europe, we find that there are almost 2,000 organisations and over 1,000 projects using open and collaborative technologies to tackle social challenges. We complement this analysis by piloting experimental data methods such as Twitter analysis to understand in further depth the distribution of DSI across Europe. You can explore the data on projects and organisations on digitalsocial.eu.

However, despite widespread activity, few initiatives have grown to deliver impact at scale, to be institutionalised, or to become “the new normal”.

In this research, we find that weak networks between stakeholders, insufficient funding and investment, skills shortages, and slow adoption by public sector and established civil society organisations is holding back the growth of DSI…(More)”.

Internet of Things: Status and implications of an increasingly connected world


GAO Technology Assessment: “The Internet of Things (IoT) refers to the technologies and devices that sense information and communicate it to the Internet or other networks and, in some cases, act on that information. These “smart” devices are increasingly being used to communicate and process quantities and types of information that have never been captured before and respond automatically to improve industrial processes, public services, and the well-being of individual consumers. For example, a “connected” fitness tracker can monitor a user’s vital statistics, and store the information on a smartphone. A “smart” tractor can use GPS-based driving guidance to maximize crop planting or harvesting. Electronic processors and sensors have become smaller and less costly, which makes it easier to equip devices with IoT capabilities. This is fueling the global proliferation of connected devices, allowing new technologies to be embedded in millions of everyday products. The IoT’s rapid emergence brings the promise of important new benefits, but also presents potential challenges such as the following:

  • Information security. The IoT brings the risks inherent in potentially unsecured information technology systems into homes, factories, and communities. IoT devices, networks, or the cloud servers where they store data can be compromised in a cyberattack. For example, in 2016, hundreds of thousands of weakly-secured IoT devices were accessed and hacked, disrupting traffic on the Internet.
  • Privacy. Smart devices that monitor public spaces may collect information about individuals without their knowledge or consent. For example, fitness trackers link the data they collect to online user accounts, which generally include personally identifiable information, such as names, email addresses, and dates of birth. Such information could be used in ways that the consumer did not anticipate. For example, that data could be sold to companies to target consumers with advertising or to determine insurance rates.
  • Safety. Researchers have demonstrated that IoT devices such as connected automobiles and medical devices can be hacked, potentially endangering the health and safety of their owners. For example, in 2015, hackers gained remote access to a car through its connected entertainment system and were able to cut the brakes and disable the transmission.
  • Standards. IoT devices and systems must be able to communicate easily. Technical standards to enable this communication will need to be developed and implemented effectively.
  • Economic issues. While impacts such as positive growth for industries that can use the IoT to reduce costs and provide better services to customers are likely, economic disruptions are also possible, such as reducing the need for certain types of businesses and jobs that rely on individual interventions, including assembly line work or commercial vehicle deliveries…(More)”

Using Facebook Ads Audiences for Global Lifestyle Disease Surveillance: Promises and Limitations


Paper by Matheus Araujo et al at ArXiv: “Every day, millions of users reveal their interests on Facebook, which are then monetized via targeted advertisement marketing campaigns. In this paper, we explore the use of demographically rich Facebook Ads audience estimates for tracking non-communicable diseases around the world. Across 47 countries, we compute the audiences of marker interests, and evaluate their potential in tracking health conditions associated with tobacco use, obesity, and diabetes, compared to the performance of placebo interests. Despite its huge potential, we €find that, for modeling prevalence of health conditions across countries, di‚fferences in these interest audiences are only weakly indicative of the corresponding prevalence rates. Within the countries, however, our approach provides interesting insights on trends of health awareness across demographic groups. Finally, we provide a temporal error analysis to expose the potential pitfalls of using Facebook’s Marketing API as a black box…(More)”.

Decision Making in a World of Comparative Effectiveness Research


Book by Howard G. Birnbaum and Paul E. Greenberg: “In the past decade there has been a worldwide evolution in evidence-based medicine that focuses on real-world Comparative Effectiveness Research (CER) to compare the effects of one medical treatment versus another in real world settings. While most of this burgeoning literature has focused on research findings, data and methods, Howard Birnbaum and Paul Greenberg (both of Analysis Group) have edited a book that provides a practical guide to decision making using the results of analysis and interpretation of CER. Decision Making in a World of Comparative Effectiveness contains chapters by senior industry executives, key opinion leaders, accomplished researchers, and leading attorneys involved in resolving disputes in the life sciences industry. The book is aimed at ‘users’ and ‘decision makers’ involved in the life sciences industry rather than those doing the actual research. This book appeals to those who commission CER within the life sciences industry (pharmaceutical, biologic, and device manufacturers), government (both public and private payers), as well as decision makers of all levels, both in the US and globally…(More)”.

Why big-data analysis of police activity is inherently biased


 and  in The Conversation: “In early 2017, Chicago Mayor Rahm Emanuel announced a new initiative in the city’s ongoing battle with violent crime. The most common solutions to this sort of problem involve hiring more police officers or working more closely with community members. But Emanuel declared that the Chicago Police Department would expand its use of software, enabling what is called “predictive policing,” particularly in neighborhoods on the city’s south side.

The Chicago police will use data and computer analysis to identify neighborhoods that are more likely to experience violent crime, assigning additional police patrols in those areas. In addition, the software will identify individual people who are expected to become – but have yet to be – victims or perpetrators of violent crimes. Officers may even be assigned to visit those people to warn them against committing a violent crime.

Any attempt to curb the alarming rate of homicides in Chicago is laudable. But the city’s new effort seems to ignore evidence, including recent research from members of our policing study team at the Human Rights Data Analysis Group, that predictive policing tools reinforce, rather than reimagine, existing police practices. Their expanded use could lead to further targeting of communities or people of color.

Working with available data

At its core, any predictive model or algorithm is a combination of data and a statistical process that seeks to identify patterns in the numbers. This can include looking at police data in hopes of learning about crime trends or recidivism. But a useful outcome depends not only on good mathematical analysis: It also needs good data. That’s where predictive policing often falls short.

Machine-learning algorithms learn to make predictions by analyzing patterns in an initial training data set and then look for similar patterns in new data as they come in. If they learn the wrong signals from the data, the subsequent analysis will be lacking.

This happened with a Google initiative called “Flu Trends,” which was launched in 2008 in hopes of using information about people’s online searches to spot disease outbreaks. Google’s systems would monitor users’ searches and identify locations where many people were researching various flu symptoms. In those places, the program would alert public health authorities that more people were about to come down with the flu.

But the project failed to account for the potential for periodic changes in Google’s own search algorithm. In an early 2012 update, Google modified its search tool to suggest a diagnosis when users searched for terms like “cough” or “fever.” On its own, this change increased the number of searches for flu-related terms. But Google Flu Trends interpreted the data as predicting a flu outbreak twice as big as federal public health officials expected and far larger than what actually happened.

Criminal justice data are biased

The failure of the Google Flu Trends system was a result of one kind of flawed data – information biased by factors other than what was being measured. It’s much harder to identify bias in criminal justice prediction models. In part, this is because police data aren’t collected uniformly, and in part it’s because what data police track reflect longstanding institutional biases along income, race and gender lines….(More)”.

Scientists crowdsource autism data to learn where resource gaps exist


SCOPE: “How common is autism? Since 2000, the U.S. Centers for Disease Control and Prevention has revised its estimate several times, with the numbers ticking steadily upward. But the most recent figure of 1 in 68 kids affected is based on data from only 11 states. It gives no indication of where people with autism live around the country nor whether their communities have the resources to treat them.
That’s a knowledge gap Stanford biomedical data scientist Dennis Wall, PhD, wants to fill — not just in the United States but also around the world. A new paper, published online in JMIR Public Health & Surveillance, explains how Wall and his team created GapMap, an interactive website designed to crowdsource the missing autism data. They’re now inviting people and families affected by autism to contribute to the database….
The pilot phase of the research, which is described in the new paper, estimated that the average distance from an individual in the U.S. to the nearest autism diagnostic center is 50 miles, while those with an autism diagnosis live an average of 20 miles from the nearest diagnostic center. The researchers think this may reflect lower rates of diagnosis among people in rural areas….Data submitted to GapMap will be stored in a secure, HIPAA-compliant database. In addition to showing where more autism treatment resources are needed, the researchers hope the project will help build communities of families affected by autism and will inform them of treatment options nearby. Families will also have the option of participating in future autism research, and the scientists plan to add more features, including the locations of environmental factors such as local pollution, to understand if they contribute to autism…(More)”

NYC’s New Tech to Track Every Homeless Person in the City


Wired: “New York is facing a crisis. The city that never sleeps has become the city with the most people who have no home to sleep in. As rising rents outpace income growth across the five boroughs, some 62,000 people, nearly 40 percent of them children, live in homeless shelters—rates the city hasn’t seen since the Great Depression.

As New York City Mayor Bill de Blasio faces reelection in November, his reputation and electoral prospects depend in part on his ability to reverse this troubling trend. In the mayor’s estimation, combatting homelessness effectively will require opening 90 new shelters across the city and expanding the number of outreach workers who canvass the streets every day offering aid and housing. The effort will also require having the technology in place to ensure that work happens as efficiently as possible. To that end, the city is rolling out a new tool, StreetSmart, aims to give city agencies and non-profit groups a comprehensive view of all of the data being collected on New York’s homeless on a daily basis.

Think of StreetSmart as a customer relationship management system for the homeless. Every day in New York, some 400 outreach workers walk the streets checking in on homeless people and collecting information about their health, income, demographics, and history in the shelter system, among other data points. The workers get to know this vulnerable population and build trust in the hope of one day placing them in some type of housing.

StreetSmart-Dashboard.jpg

Traditionally, outreach workers have entered information about every encounter into a database, keeping running case files. But those databases never talked to each other. One outreach worker in the Bronx might never know she was talking to the same person who’d checked into a Brooklyn shelter a week prior. More importantly, the worker might never know why that person left. What’s more, systems used by city agencies and non-profits seldom overlapped, complicating efforts to keep track of individuals….

The big promise of StreetSmart extends beyond its ability to help outreach workers in the moment. The aggregation of all this information could also help the city proactively design fixes to problems it wouldn’t have otherwise seen. The tool has a map feature that shows where encampments are popping up and where outreach workers are having the most interactions. It can also be used to assess how effective different housing facilities are at keeping people off the streets….(More)”.