Set It and Forget It: How Default Settings Rule the World

Curated on Posted on by Stefaan Verhulst

Lena Groeger at ProPublica: “We’ve seen how design can keep us away from harm and save our lives. But there is a more subtle way that design influences our daily decisions and behavior – whether we know it or not. It’s not sexy or trendy or flashy in any way. I’m talking about defaults.

Defaults are the settings that come out of the box, the selections you make on your computer by hitting enter, the assumptions that people make unless you object, the options easily available to you because you haven’t changed them.

They might not seem like much, but defaults (and their designers) hold immense power – they make decisions for us that we’re not even aware of making. Consider the fact that most people never change the factory settings on their computer, the default ringtone on their phones, or the default temperature in their fridge. Someone, somewhere, decided what those defaults should be – and it probably wasn’t you.

Another example: In the U.S. when you register for your driver’s license, you’re asked whether or not you’d like to be an organ donor. We operate on an opt-in basis: that is, the default is that you are not an organ donor. If you want to donate your organs, you need to actively check a box on the DMV questionnaire. Only about 40 percent of the population is signed up to be an organ donor.

In other countries such as Spain, Portugal and Austria, the default is that you’re an organ donor unless you explicitly choose not to be. And in many of those countries over 99 percent of the population is registered. A recent study found that countries with opt-out or “presumed consent” policies don’t just have more people who sign up to be donors, they also have consistently higher numbers of transplants.

Of course, there are plenty of other factors that influence the success of organ donation systems, but the opt-in versus opt-out choice seems to have a real effect on our collective behavior. An effect that could potentially make the difference between someone getting a life-saving transplant or not.

Behavioral economist Richard Thaler and legal scholar Cass Sunstein pretty much wrote the book on the implications of defaults on human behavior. Nudge: Improving Decisions About Health, Wealth, and Happiness is full of ways in which default options can steer human choices, even if we have no idea it’s happening. Besides organ donation, the list of potential “nudges” include everything from changing the order of menu itemsto encourage people to pick certain dishes to changing the default temperature of office thermostats to save on energy.

But my favorite has to do with getting kids to eat their vegetables.

What if I told you there was one simple change you could make in a school cafeteria to get children to eat more salad? It doesn’t cost anything, force anyone to eat anything they don’t want, and it takes only a few minutes to fix. And it happened in real life: a middle school in New York moved their salad bar away from its default location against a walland put it smack in the middle of the room (and prominently in front of the two cash registers, as seen in the diagram below). Salad sales more than tripled….(More)”

Technology Is Monitoring the Urban Landscape

Curated on Posted on by Stefaan Verhulst

Quentin Hardy in the New York Times: “Big City is watching you.

It will do it with camera-equipped drones that inspect municipal powerlines and robotic cars that know where people go. Sensor-laden streetlights will change brightness based on danger levels. Technologists and urban planners are working on a major transformation of urban landscapes over the next few decades.

Much of it involves the close monitoring of things and people, thanks to digital technology. To the extent that this makes people’s lives easier, the planners say, they will probably like it. But troubling and knotty questions of privacy and control remain.

A White House report published in February identified advances in transportation, energy and manufacturing, among other developments, that will bring on what it termed “a new era of change.”

Much of the change will also come from the private sector, which is moving faster to reach city dwellers, and is more skilled in collecting and responding to data. That is leading cities everywhere to work more closely than ever with private companies, which may have different priorities than the government.

One of the biggest changes that will hit a digitally aware city, it is widely agreed, is the seemingly prosaic issue of parking. Space given to parking is expected to shrink by half or more, as self-driving cars and drone deliveries lead an overall shift in connected urban transport. That will change or eliminate acres of urban space occupied by raised and underground parking structures.

Shared vehicles are not parked as much, and with more automation, they will know where parking spaces are available, eliminating the need to drive in search of a space.

“Office complexes won’t need parking lots with twice the footprint of their buildings,” said Sebastian Thrun, who led Google’s self-driving car project in its early days and now runs Udacity, an online learning company. “Whenwe started on self-driving cars, we talked all the time about cutting the number of cars in a city by a factor of three,” or a two-thirds reduction.

In addition, police, fire, and even library services will seek greater responsiveness by tracking their own assets, and partly by looking at things like social media. Later, technologies like three-dimensional printing, new materials and robotic construction and demolition will be able to reshape skylines in a matter of weeks.

At least that is the plan. So much change afoot creates confusion….

The new techno-optimism is focused on big data and artificial intelligence.“Futurists used to think everyone would have their own plane,” said ErickGuerra, a professor of city and regional planning at the University ofPennsylvania. “We never have a good understanding of how things will actually turn out.”

He recently surveyed the 25 largest metropolitan planning organizations in the country and found that almost none have solid plans for modernizing their infrastructure. That may be the right way to approach the challenges of cities full of robots, but so far most clues are coming from companies that also sell the technology.

 “There’s a great deal of uncertainty, and a competition to show they’re low on regulation,” Mr. Guerra said. “There is too much potential money for new technology to be regulated out.”

The big tech companies say they are not interested in imposing the sweeping “smart city” projects they used to push, in part because things are changing too quickly. But they still want to build big, and they view digital surveillance as an essential component…(More)”

Solving All the Wrong Problems

Curated on Posted on by Stefaan Verhulst

Allison Arieff in the New York Times: “Every day, innovative companies promise to make the world a better place. Are they succeeding? Here is just a sampling of the products, apps and services that have come across my radar in the last few weeks:

A service that sends someone to fill your car with gas.

A service that sends a valet on a scooter to you, wherever you are, to park your car.

A service that will film anything you desire with a drone….

We are overloaded daily with new discoveries, patents and inventions all promising a better life, but that better life has not been forthcoming for most. In fact, the bulk of the above list targets a very specific (and tiny!) slice of the population. As one colleague in tech explained it to me recently, for most people working on such projects, the goal is basically to provide for themselves everything that their mothers no longer do….When everything is characterized as “world-changing,” is anything?

Clay Tarver, a writer and producer for the painfully on-point HBO comedy “Silicon Valley,” said in a recent New Yorker article: “I’ve been told that, at some of the big companies, the P.R. departments have ordered their employees to stop saying ‘We’re making the world a better place,’ specifically because we have made fun of that phrase so mercilessly. So I guess, at the very least, we’re making the world a better place by making these people stop saying they’re making the world a better place.”

O.K., that’s a start. But the impulse to conflate toothbrush delivery with Nobel Prize-worthy good works is not just a bit cultish, it’s currently a wildfire burning through the so-called innovation sector. Products and services are designed to “disrupt” market sectors (a.k.a. bringing to market things no one really needs) more than to solve actual problems, especially those problems experienced by what the writer C. Z. Nnaemeka has described as “the unexotic underclass” — single mothers, the white rural poor, veterans, out-of-work Americans over 50 — who, she explains, have the “misfortune of being insufficiently interesting.”

If the most fundamental definition of design is to solve problems, why are so many people devoting so much energy to solving problems that don’t really exist? How can we get more people to look beyond their own lived experience?

In “Design: The Invention of Desire,” a thoughtful and necessary new book by the designer and theorist Jessica Helfand, the author brings to light an amazing kernel: “hack,” a term so beloved in Silicon Valley that it’s painted on the courtyard of the Facebook campus and is visible from planes flying overhead, is also prison slang for “horse’s ass carrying keys.”

To “hack” is to cut, to gash, to break. It proceeds from the belief that nothing is worth saving, that everything needs fixing. But is that really the case? Are we fixing the right things? Are we breaking the wrong ones? Is it necessary to start from scratch every time?…

Ms. Helfand calls for a deeper embrace of personal vigilance: “Design may provide the map,” she writes, “but the moral compass that guides our personal choices resides permanently within us all.”

Can we reset that moral compass? Maybe we can start by not being a bunch of hacks….(More)”

Building a Democracy Machine: Toward an Integrated and Empowered Form of Civic Engagement

Curated on Posted on by Stefaan Verhulst

Essay by John Gastil: “Dozens—and possibly hundreds—of online platforms have been built in the past decade to facilitate specific forms of civic engagement. Unconnected to each other, let alone an integrated system easy for citizens to use, these platforms cannot begin to realize their full potential. The author proposes a massive collaborative project to build an integrated platform called, tongue squarely in cheek, “The Democracy Machine.” The Machine draws on public energy and ideas, mixing those into concrete policy advice, influencing government decision making, and creating a feedback loop that helps officials and citizens track progress together as they continuously turn the policymaking crank. This online system could help to harmonize civic leaders, vocal and marginalized citizens, and government. Democracy’s need for ongoing public consultation would fuel the Machine, which would, in turn, generate the empowered deliberation and public legitimacy that government needs to make tough policy decisions….(More)”

Better research through video games

Curated on Posted on by Stefaan Verhulst

Simon Parkin at the New Yorker:”… it occurred to Szantner and Revaz that the tremendous amount of time and energy that people put into games could be co-opted in the name of human progress. That year, they founded Massively Multiplayer Online Science, a company that pairs game makers with scientists.

This past March, the first fruits of their conversation in Geneva appeared in EVE Online, a complex science-fiction game set in a galaxy composed of tens of thousands of stars and planets, and inhabited by half a million or so people from across the Internet, who explore and do battle daily. EVE was launched in 2003 by C.C.P., a studio based in Reykjavík, but players have only recently begun to contribute to scientific research. Their task is to assist with the Human Protein Atlas (H.P.A.), a Swedish-run effort to catalogue proteins and the genes that encode them, in both normal tissue and cancerous tumors. “Humans are, by evolution, very good at quickly recognizing patterns,” Emma Lundberg, the director of the H.P.A.’s Subcellular Atlas, a database of high-resolution images of fluorescently dyed cells, told me. “This is what we exploit in the game.”

The work, dubbed Project Discovery, fits snugly into EVE Online’s universe. At any point, players can take a break from their dogfighting, trading, and political machinations to play a simple game within the game, finding commonalities and differences between some thirteen million microscope images. In each one, the cell’s innards have been color-coded—blue for the nucleus (the cell’s brain), red for microtubules (the cell’s scaffolding), and green for anywhere that a protein has been detected. After completing a tutorial, players tag the image using a list of twenty-nine options, including “nucleus,” “cytoplasm,” and “mitochondria.” When enough players reach a consensus on a single image, it is marked as “solved” and handed off to the scientists at the H.P.A. “In terms of the pattern recognition and classification, it resembles what we are doing as researchers,” Lundberg said. “But the game interface is, of course, much cooler than our laboratory information-management system. I would love to work in-game only.”

Rather than presenting the project as a worthy extracurricular activity, EVE Online’s designers have cast it as an extension of the game’s broader fiction. Players work for the Sisters of EVE, a religious humanitarian-aid organization, which rewards their efforts with virtual currency. This can be used to purchase items in the game, including a unique set of armor designed by one of the C.C.P.’s artists, Andrei Cristea. (The armor is available only to players who participate in Project Discovery, and therefore, like a rare Coco Chanel frock, is desirable as much for its scarcity as for its design.) Insuring that the mini-game be thought of as more than a short-term novelty or diversion was an issue that Linzi Campbell, Project Discovery’s lead designer, considered carefully. “The hardest challenge has been turning the image-analysis process into a game that is strong enough to motivate the player to continue playing,” Campbell told me. “The fun comes from the feeling of mastery.”

Evidently, her efforts were successful. On the game’s first day of release, there were four hundred thousand submissions from players. According to C.C.P., some people have been so caught up in the task that they have played for fifteen hours without interruption. “EVE players turned out to be a perfect crowd for this type of citizen science,” Lundberg said. She anticipates that the first phase of the project will be completed this summer. If the work meets this target, players will be presented with more advanced images and tasks, such as the classification of protein patterns in complex tumor-tissue samples. Eventually, their efforts could aid in the development of new cancer drugs….(More)”

Civic Data Initiatives

Curated on Posted on by Stefaan Verhulst

Burak Arikan at Medium: “Big data is the term used to define the perpetual and massive data gathered by corporations and governments on consumers and citizens. When the subject of data is not necessarily individuals but governments and companies themselves, we can call it civic data, and when systematically generated in large amounts, civic big data. Increasingly, a new generation of initiatives are generating and organizing structured data on particular societal issues from human rights violations, to auditing government budgets, from labor crimes to climate justice.

These civic data initiatives diverge from the traditional civil society organizations in their outcomes,that they don’t just publish their research as reports, but also open it to the public as a database.Civic data initiatives are quite different in their data work than international non-governmental organizations such as UN, OECD, World Bank and other similar bodies. Such organizations track social, economical, political conditions of countries and concentrate upon producing general statistical data, whereas civic data initiatives aim to produce actionable data on issues that impact individuals directly. The change in the GDP value of a country is useless for people struggling for free transportation in their city. Incarceration rate of a country does not help the struggle of the imprisoned journalists. Corruption indicators may serve as a parameter in a country’s credit score, but does not help to resolve monopolization created with public procurement. Carbon emission statistics do not prevent the energy deals between corrupt governments that destroy the nature in their region.

Partnership / financing network of oil refinery and thermal power plants around SOCAR (The State Oil Company of Azerbaijan). Data mapping by Network of Dispossession.

Needless to say, civic data initiatives also differ from governmental institutions, which are reluctant to share any more that they are legally obligated to. Many governments in the world simply dump scanned hardcopies of documents on official websites instead of releasing machine-readable data, which prevents systematic auditing of government activities.Civic data initiatives, on the other hand, make it a priority to structure and release their data in formats that are both accessible and queryable.

Civic data initiatives also deviate from general purpose information commons such as Wikipedia. Because they consistently engage with problems, closely watch a particular societal issue, make frequent updates,even record from the field to generate and organize highly granular data about the matter….

Several civic data initiatives generate data on variety of issues at different geographies, scopes, and scales. The non-exhaustive list below have information on founders, data sources, and financial support. It is sorted according to each initiative’s founding year. Please send your suggestions to contact at graphcommons.com. See more detailed information and updates on the spreadsheet of civic data initiatives.

Open Secrets tracks data about the money flow in the US government, so it becomes more accessible for journalists, researchers, and advocates.Founded as a non-profit in 1983 by Center for Responsive Politics, gets support from variety of institutions.

PolitiFact is a fact-checking website that rates the accuracy of claims by elected officials and others who speak up in American politics. Uses on-the-record interviews as its data source. Founded in 2007 as a non-profit organization by Tampa Bay Times. Supported by Democracy Fund, Bill &Melinda Gates Foundation, John S. and James L. Knight Foundation, FordFoundation, Knight Foundation, Craigslist Charitable Fund, and the CollinsCenter for Public Policy…..

La Fabrique de La loi (The Law Factory) maps issues of local-regional socio-economic development, public investments, and ecology in France.Started in 2014, the project builds a database by tracking bills from government sources, provides a search engine as well as an API. The partners of the project are CEE Sciences Po, médialab Sciences Po, RegardsCitoyens, and Density Design.

Mapping Media Freedom identifies threats, violations and limitations faced by members of the press throughout European Union member states,candidates for entry and neighbouring countries. Initiated by Index onCensorship and European Commission in 2004, the project…(More)”

City of Copenhagen launches data marketplace

Curated on Posted on by Stefaan Verhulst

Sarah Wray at TMForum: “The City of Copenhagen has launched its City Data Exchange to make public and private data accessible to power innovation.

The City Data Exchange is a new service to create a ‘marketplace for data’ from public and private data providers and allow monetization. The platform has been developed by Hitachi Insight Group.

“Data is the fuel powering our digital world, but in most cities it is unused,” said Hans Lindeman, Senior Vice President, Hitachi Insight Group, EMEA. “Even where data sits in public, freely accessible databases, the cost of extracting and processing it can easily outweigh the benefits.”

The City of Copenhagen is using guidelines for a data format that is safe, secure, ensures privacy and makes data easy to use. The City Data Exchange will only accept data that has been fully anonymized by the data supplier, for example.

According to Hitachi Insight Group, “All of this spares organizations the trouble and cost of extracting and processing data from multiple sources. At the same time, proprietary data can now become a business resource that can be monetized outside an organization.”

As a way to demonstrate how data from the City Data Exchange could be used in applications, Hitachi Insight Group is developing two applications:

  • Journey Insight, which helps citizens in the region to track their transportation usage over time and understand the carbon footprint of their travel
  • Energy Insight, which allows both households and businesses to see how much energy they use.

Both are set for public launch later this year.

Another example of how data marketplaces can enable innovation is the Mind My Business mobile app, developed by Vizalytics. It brings together all the data that can affect a retailer — from real-time information on how construction or traffic issues can hurt the footfall of a business, to timely reminders about taxes to pay or new regulations to meet. The “survival app for shopkeepers” makes full use of all the relevant data sources brought together by the City Data Exchange.

The platform will offer data in different categories such as: city life, infrastructure, climate and environment, business data and economy, demographics, housing and buildings, and utilities usage. It aims to meet the needs of local government, city planners, architects, retailers, telecoms networks, utilities, and all other companies and organizations who want to understand what makes Copenhagen, its businesses and its citizens tick.

“Smart cities need smart insights, and that’s only possible if everybody has all the facts at their disposal. The City Data Exchange makes that possible; it’s the solution that will help us all to create better public spaces and — for companies in Copenhagen — to offer better services and create jobs,” said Frank Jensen, the Lord Mayor of Copenhagen.

The City Data Exchange is currently offering raw data to its customers, and later this year will add analytical tools. The cost of gathering and processing the data will be recovered through subscription and service fees, which are expected to be much lower than the cost any company or city would face in performing the work of extracting, collecting and integrating the data by themselves….(More)”

Selected Readings on Data Collaboratives

Curated on Posted on by Andrew Young

By Neil Britto, David Sangokoya, Iryna Susha, Stefaan Verhulst and Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of data collaboratives was originally published in 2017.

The term data collaborative refers to a new form of collaboration, beyond the public-private partnership model, in which participants from different sectors (including private companies, research institutions, and government agencies ) can exchange data to help solve public problems. Several of society’s greatest challenges — from addressing climate change to public health to job creation to improving the lives of children — require greater access to data, more collaboration between public – and private-sector entities, and an increased ability to analyze datasets. In the coming months and years, data collaboratives will be essential vehicles for harnessing the vast stores of privately held data toward the public good.

Selected Reading List (in alphabetical order)

Annotated Selected Readings List (in alphabetical order)

Agaba, G., Akindès, F., Bengtsson, L., Cowls, J., Ganesh, M., Hoffman, N., . . . Meissner, F. “Big Data and Positive Social Change in the Developing World: A White Paper for Practitioners and Researchers.” 2014. http://bit.ly/25RRC6N.

  • This white paper, produced by “a group of activists, researchers and data experts” explores the potential of big data to improve development outcomes and spur positive social change in low- and middle-income countries. Using examples, the authors discuss four areas in which the use of big data can impact development efforts:
    • Advocating and facilitating by “opening[ing] up new public spaces for discussion and awareness building;
    • Describing and predicting through the detection of “new correlations and the surfac[ing] of new questions;
    • Facilitating information exchange through “multiple feedback loops which feed into both research and action,” and
    • Promoting accountability and transparency, especially as a byproduct of crowdsourcing efforts aimed at “aggregat[ing] and analyz[ing] information in real time.
  • The authors argue that in order to maximize the potential of big data’s use in development, “there is a case to be made for building a data commons for private/public data, and for setting up new and more appropriate ethical guidelines.”
  • They also identify a number of challenges, especially when leveraging data made accessible from a number of sources, including private sector entities, such as:
    • Lack of general data literacy;
    • Lack of open learning environments and repositories;
    • Lack of resources, capacity and access;
    • Challenges of sensitivity and risk perception with regard to using data;
    • Storage and computing capacity; and
    • Externally validating data sources for comparison and verification.

Ansell, C. and Gash, A. “Collaborative Governance in Theory and Practice.” Journal of Public Administration Research and  Theory 18 (4), 2008. http://bit.ly/1RZgsI5.

  • This article describes collaborative arrangements that include public and private organizations working together and proposes a model for understanding an emergent form of public-private interaction informed by 137 diverse cases of collaborative governance.
  • The article suggests factors significant to successful partnering processes and outcomes include:
    • Shared understanding of challenges,
    • Trust building processes,
    • The importance of recognizing seemingly modest progress, and
    • Strong indicators of commitment to the partnership’s aspirations and process.
  • The authors provide a ‘’contingency theory model’’ that specifies relationships between different variables that influence outcomes of collaborative governance initiatives. Three “core contingencies’’ for successful collaborative governance initiatives identified by the authors are:
    • Time (e.g., decision making time afforded to the collaboration);
    • Interdependence (e.g., a high degree of interdependence can mitigate negative effects of low trust); and
    • Trust (e.g. a higher level of trust indicates a higher probability of success).

Ballivian A, Hoffman W. “Public-Private Partnerships for Data: Issues Paper for Data Revolution Consultation.” World Bank, 2015. Available from: http://bit.ly/1ENvmRJ

  • This World Bank report provides a background document on forming public-prviate partnerships for data with the private sector in order to inform the UN’s Independent Expert Advisory Group (IEAG) on sustaining a “data revolution” in sustainable development.
  • The report highlights the critical position of private companies within the data value chain and reflects on key elements of a sustainable data PPP: “common objectives across all impacted stakeholders, alignment of incentives, and sharing of risks.” In addition, the report describes the risks and incentives of public and private actors, and the principles needed to “build[ing] the legal, cultural, technological and economic infrastructures to enable the balancing of competing interests.” These principles include understanding; experimentation; adaptability; balance; persuasion and compulsion; risk management; and governance.
  • Examples of data collaboratives cited in the report include HP Earth Insights, Orange Data for Development Challenges, Amazon Web Services, IBM Smart Cities Initiative, and the Governance Lab’s Open Data 500.

Brack, Matthew, and Tito Castillo. “Data Sharing for Public Health: Key Lessons from Other Sectors.” Chatham House, Centre on Global Health Security. April 2015. Available from: http://bit.ly/1DHFGVl

  • The Chatham House report provides an overview on public health surveillance data sharing, highlighting the benefits and challenges of shared health data and the complexity in adapting technical solutions from other sectors for public health.
  • The report describes data sharing processes from several perspectives, including in-depth case studies of actual data sharing in practice at the individual, organizational and sector levels. Among the key lessons for public health data sharing, the report strongly highlights the need to harness momentum for action and maintain collaborative engagement: “Successful data sharing communities are highly collaborative. Collaboration holds the key to producing and abiding by community standards, and building and maintaining productive networks, and is by definition the essence of data sharing itself. Time should be invested in establishing and sustaining collaboration with all stakeholders concerned with public health surveillance data sharing.”
  • Examples of data collaboratives include H3Africa (a collaboration between NIH and Wellcome Trust) and NHS England’s care.data programme.

de Montjoye, Yves-Alexandre, Jake Kendall, and Cameron F. Kerry. “Enabling Humanitarian Use of Mobile Phone Data.” The Brookings Institution, Issues in Technology Innovation. November 2014. Available from: http://brook.gs/1JxVpxp

  • Using Ebola as a case study, the authors describe the value of using private telecom data for uncovering “valuable insights into understanding the spread of infectious diseases as well as strategies into micro-target outreach and driving update of health-seeking behavior.”
  • The authors highlight the absence of a common legal and standards framework for “sharing mobile phone data in privacy-conscientious ways” and recommend “engaging companies, NGOs, researchers, privacy experts, and governments to agree on a set of best practices for new privacy-conscientious metadata sharing models.”

Eckartz, Silja M., Hofman, Wout J., Van Veenstra, Anne Fleur. “A decision model for data sharing.” Vol. 8653 LNCS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. http://bit.ly/21cGWfw.

  • This paper proposes a decision model for data sharing of public and private data based on literature review and three case studies in the logistics sector.
  • The authors identify five categories of the barriers to data sharing and offer a decision model for identifying potential interventions to overcome each barrier:
    • Ownership. Possible interventions likely require improving trust among those who own the data through, for example, involvement and support from higher management
    • Privacy. Interventions include “anonymization by filtering of sensitive information and aggregation of data,” and access control mechanisms built around identity management and regulated access.  
    • Economic. Interventions include a model where data is shared only with a few trusted organizations, and yield management mechanisms to ensure negative financial consequences are avoided.
    • Data quality. Interventions include identifying additional data sources that could improve the completeness of datasets, and efforts to improve metadata.
    • Technical. Interventions include making data available in structured formats and publishing data according to widely agreed upon data standards.

Hoffman, Sharona and Podgurski, Andy. “The Use and Misuse of Biomedical Data: Is Bigger Really Better?” American Journal of Law & Medicine 497, 2013. http://bit.ly/1syMS7J.

  • This journal articles explores the benefits and, in particular, the risks related to large-scale biomedical databases bringing together health information from a diversity of sources across sectors. Some data collaboratives examined in the piece include:
    • MedMining – a company that extracts EHR data, de-identifies it, and offers it to researchers. The data sets that MedMining delivers to its customers include ‘lab results, vital signs, medications, procedures, diagnoses, lifestyle data, and detailed costs’ from inpatient and outpatient facilities.
    • Explorys has formed a large healthcare database derived from financial, administrative, and medical records. It has partnered with major healthcare organizations such as the Cleveland Clinic Foundation and Summa Health System to aggregate and standardize health information from ten million patients and over thirty billion clinical events.
  • Hoffman and Podgurski note that biomedical databases populated have many potential uses, with those likely to benefit including: “researchers, regulators, public health officials, commercial entities, lawyers,” as well as “healthcare providers who conduct quality assessment and improvement activities,” regulatory monitoring entities like the FDA, and “litigants in tort cases to develop evidence concerning causation and harm.”
  • They argue, however, that risks arise based on:
    • The data contained in biomedical databases is surprisingly likely to be incorrect or incomplete;
    • Systemic biases, arising from both the nature of the data and the preconceptions of investigators are serious threats the validity of research results, especially in answering causal questions;
  • Data mining of biomedical databases makes it easier for individuals with political, social, or economic agendas to generate ostensibly scientific but misleading research findings for the purpose of manipulating public opinion and swaying policymakers.

Krumholz, Harlan M., et al. “Sea Change in Open Science and Data Sharing Leadership by Industry.” Circulation: Cardiovascular Quality and Outcomes 7.4. 2014. 499-504. http://1.usa.gov/1J6q7KJ

  • This article provides a comprehensive overview of industry-led efforts and cross-sector collaborations in data sharing by pharmaceutical companies to inform clinical practice.
  • The article details the types of data being shared and the early activities of GlaxoSmithKline (“in coordination with other companies such as Roche and ViiV”); Medtronic and the Yale University Open Data Access Project; and Janssen Pharmaceuticals (Johnson & Johnson). The article also describes the range of involvement in data sharing among pharmaceutical companies including Pfizer, Novartis, Bayer, AbbVie, Eli Llly, AstraZeneca, and Bristol-Myers Squibb.

Mann, Gideon. “Private Data and the Public Good.” Medium. May 17, 2016. http://bit.ly/1OgOY68.

    • This Medium post from Gideon Mann, the Head of Data Science at Bloomberg, shares his prepared remarks given at a lecture at the City College of New York. Mann argues for the potential benefits of increasing access to private sector data, both to improve research and academic inquiry and also to help solve practical, real-world problems. He also describes a number of initiatives underway at Bloomberg along these lines.    
  • Mann argues that data generated at private companies “could enable amazing discoveries and research,” but is often inaccessible to those who could put it to those uses. Beyond research, he notes that corporate data could, for instance, benefit:
      • Public health – including suicide prevention, addiction counseling and mental health monitoring.
    • Legal and ethical questions – especially as they relate to “the role algorithms have in decisions about our lives,” such as credit checks and resume screening.
  • Mann recognizes the privacy challenges inherent in private sector data sharing, but argues that it is a common misconception that the only two choices are “complete privacy or complete disclosure.” He believes that flexible frameworks for differential privacy could open up new opportunities for responsibly leveraging data collaboratives.

Pastor Escuredo, D., Morales-Guzmán, A. et al, “Flooding through the Lens of Mobile Phone Activity.” IEEE Global Humanitarian Technology Conference, GHTC 2014. Available from: http://bit.ly/1OzK2bK

  • This report describes the impact of using mobile data in order to understand the impact of disasters and improve disaster management. The report was conducted in the Mexican state of Tabasco in 2009 as a multidisciplinary, multi-stakeholder consortium involving the UN World Food Programme (WFP), Telefonica Research, Technical University of Madrid (UPM), Digital Strategy Coordination Office of the President of Mexico, and UN Global Pulse.
  • Telefonica Research, a division of the major Latin American telecommunications company, provided call detail records covering flood-affected areas for nine months. This data was combined with “remote sensing data (satellite images), rainfall data, census and civil protection data.” The results of the data demonstrated that “analysing mobile activity during floods could be used to potentially locate damaged areas, efficiently assess needs and allocate resources (for example, sending supplies to affected areas).”
  • In addition to the results, the study highlighted “the value of a public-private partnership on using mobile data to accurately indicate flooding impacts in Tabasco, thus improving early warning and crisis management.”

* Perkmann, M. and Schildt, H. “Open data partnerships between firms and universities: The role of boundary organizations.” Research Policy, 44(5), 2015. http://bit.ly/25RRJ2c. 

  • This paper discusses the concept of a “boundary organization” in relation to industry-academic partnerships driven by data. Boundary organizations perform mediated revealing, allowing firms to disclose their research problems to a broad audience of innovators and simultaneously minimize the risk that this information would be adversely used by competitors.
  • The authors identify two especially important challenges for private firms to enter open data or participate in data collaboratives with the academic research community that could be addressed through more involvement from boundary organizations:
    • First is a challenge of maintaining competitive advantage. The authors note that, “the more a firm attempts to align the efforts in an open data research programme with its R&D priorities, the more it will have to reveal about the problems it is addressing within its proprietary R&D.”
    • Second, involves the misalignment of incentives between the private and academic field. Perkmann and Schildt argue that, a firm seeking to build collaborations around its opened data “will have to provide suitable incentives that are aligned with academic scientists’ desire to be rewarded for their work within their respective communities.”

Robin, N., Klein, T., & Jütting, J. “Public-Private Partnerships for Statistics: Lessons Learned, Future Steps.” OECD. 2016. http://bit.ly/24FLYlD.

  • This working paper acknowledges the growing body of work on how different types of data (e.g, telecom data, social media, sensors and geospatial data, etc.) can address data gaps relevant to National Statistical Offices (NSOs).
  • Four models of public-private interaction for statistics are describe: in-house production of statistics by a data-provider for a national statistics office (NSO), transfer of data-sets to NSOs from private entities, transfer of data to a third party provider to manage the NSO and private entity data, and the outsourcing of NSO functions.
  • The paper highlights challenges to public-private partnerships involving data (e.g., technical challenges, data confidentiality, risks, limited incentives for participation), suggests deliberate and highly structured approaches to public-private partnerships involving data require enforceable contracts, emphasizes the trade-off between data specificity and accessibility of such data, and the importance of pricing mechanisms that reflect the capacity and capability of national statistic offices.
  • Case studies referenced in the paper include:
    • A mobile network operator’s (MNO Telefonica) in house analysis of call detail records;
    • A third-party data provider and steward of travel statistics (Positium);
    • The Data for Development (D4D) challenge organized by MNO Orange; and
    • Statistics Netherlands use of social media to predict consumer confidence.

Stuart, Elizabeth, Samman, Emma, Avis, William, Berliner, Tom. “The data revolution: finding the missing millions.” Overseas Development Institute, 2015. Available from: http://bit.ly/1bPKOjw

  • The authors of this report highlight the need for good quality, relevant, accessible and timely data for governments to extend services into underrepresented communities and implement policies towards a sustainable “data revolution.”
  • The solutions focused on this recent report from the Overseas Development Institute focus on capacity-building activities of national statistical offices (NSOs), alternative sources of data (including shared corporate data) to address gaps, and building strong data management systems.

Taylor, L., & Schroeder, R. “Is bigger better? The emergence of big data as a tool for international development policy.” GeoJournal, 80(4). 2015. 503-518. http://bit.ly/1RZgSy4.

  • This journal article describes how privately held data – namely “digital traces” of consumer activity – “are becoming seen by policymakers and researchers as a potential solution to the lack of reliable statistical data on lower-income countries.
  • They focus especially on three categories of data collaborative use cases:
    • Mobile data as a predictive tool for issues such as human mobility and economic activity;
    • Use of mobile data to inform humanitarian response to crises; and
    • Use of born-digital web data as a tool for predicting economic trends, and the implications these have for LMICs.
  • They note, however, that a number of challenges and drawbacks exist for these types of use cases, including:
    • Access to private data sources often must be negotiated or bought, “which potentially means substituting negotiations with corporations for those with national statistical offices;”
    • The meaning of such data is not always simple or stable, and local knowledge is needed to understand how people are using the technologies in question
    • Bias in proprietary data can be hard to understand and quantify;
    • Lack of privacy frameworks; and
    • Power asymmetries, wherein “LMIC citizens are unwittingly placed in a panopticon staffed by international researchers, with no way out and no legal recourse.”

van Panhuis, Willem G., Proma Paul, Claudia Emerson, John Grefenstette, Richard Wilder, Abraham J. Herbst, David Heymann, and Donald S. Burke. “A systematic review of barriers to data sharing in public health.” BMC public health 14, no. 1 (2014): 1144. Available from: http://bit.ly/1JOBruO

  • The authors of this report provide a “systematic literature of potential barriers to public health data sharing.” These twenty potential barriers are classified in six categories: “technical, motivational, economic, political, legal and ethical.” In this taxonomy, “the first three categories are deeply rooted in well-known challenges of health information systems for which structural solutions have yet to be found; the last three have solutions that lie in an international dialogue aimed at generating consensus on policies and instruments for data sharing.”
  • The authors suggest the need for a “systematic framework of barriers to data sharing in public health” in order to accelerate access and use of data for public good.

Verhulst, Stefaan and Sangokoya, David. “Mapping the Next Frontier of Open Data: Corporate Data Sharing.” In: Gasser, Urs and Zittrain, Jonathan and Faris, Robert and Heacock Jones, Rebekah, “Internet Monitor 2014: Reflections on the Digital World: Platforms, Policy, Privacy, and Public Discourse (December 15, 2014).” Berkman Center Research Publication No. 2014-17. http://bit.ly/1GC12a2

  • This essay describe a taxonomy of current corporate data sharing practices for public good: research partnerships; prizes and challenges; trusted intermediaries; application programming interfaces (APIs); intelligence products; and corporate data cooperatives or pooling.
  • Examples of data collaboratives include: Yelp Dataset Challenge, the Digital Ecologies Research Partnerhsip, BBVA Innova Challenge, Telecom Italia’s Big Data Challenge, NIH’s Accelerating Medicines Partnership and the White House’s Climate Data Partnerships.
  • The authors highlight important questions to consider towards a more comprehensive mapping of these activities.

Verhulst, Stefaan and Sangokoya, David, 2015. “Data Collaboratives: Exchanging Data to Improve People’s Lives.” Medium. Available from: http://bit.ly/1JOBDdy

  • The essay refers to data collaboratives as a new form of collaboration involving participants from different sectors exchanging data to help solve public problems. These forms of collaborations can improve people’s lives through data-driven decision-making; information exchange and coordination; and shared standards and frameworks for multi-actor, multi-sector participation.
  • The essay cites four activities that are critical to accelerating data collaboratives: documenting value and measuring impact; matching public demand and corporate supply of data in a trusted way; training and convening data providers and users; experimenting and scaling existing initiatives.
  • Examples of data collaboratives include NIH’s Precision Medicine Initiative; the Mobile Data, Environmental Extremes and Population (MDEEP) Project; and Twitter-MIT’s Laboratory for Social Machines.

Verhulst, Stefaan, Susha, Iryna, Kostura, Alexander. “Data Collaboratives: matching Supply of (Corporate) Data to Solve Public Problems.” Medium. February 24, 2016. http://bit.ly/1ZEp2Sr.

  • This piece articulates a set of key lessons learned during a session at the International Data Responsibility Conference focused on identifying emerging practices, opportunities and challenges confronting data collaboratives.
  • The authors list a number of privately held data sources that could create positive public impacts if made more accessible in a collaborative manner, including:
    • Data for early warning systems to help mitigate the effects of natural disasters;
    • Data to help understand human behavior as it relates to nutrition and livelihoods in developing countries;
    • Data to monitor compliance with weapons treaties;
    • Data to more accurately measure progress related to the UN Sustainable Development Goals.
  • To the end of identifying and expanding on emerging practice in the space, the authors describe a number of current data collaborative experiments, including:
    • Trusted Intermediaries: Statistics Netherlands partnered with Vodafone to analyze mobile call data records in order to better understand mobility patterns and inform urban planning.
    • Prizes and Challenges: Orange Telecom, which has been a leader in this type of Data Collaboration, provided several examples of the company’s initiatives, such as the use of call data records to track the spread of malaria as well as their experience with Challenge 4 Development.
    • Research partnerships: The Data for Climate Action project is an ongoing large-scale initiative incentivizing companies to share their data to help researchers answer particular scientific questions related to climate change and adaptation.
    • Sharing intelligence products: JPMorgan Chase shares macro economic insights they gained leveraging their data through the newly established JPMorgan Chase Institute.
  • In order to capitalize on the opportunities provided by data collaboratives, a number of needs were identified:
    • A responsible data framework;
    • Increased insight into different business models that may facilitate the sharing of data;
    • Capacity to tap into the potential value of data;
    • Transparent stock of available data supply; and
    • Mapping emerging practices and models of sharing.

Vogel, N., Theisen, C., Leidig, J. P., Scripps, J., Graham, D. H., & Wolffe, G. “Mining mobile datasets to enable the fine-grained stochastic simulation of Ebola diffusion.” Paper presented at the Procedia Computer Science. 2015. http://bit.ly/1TZDroF.

  • The paper presents a research study conducted on the basis of the mobile calls records shared with researchers in the framework of the Data for Development Challenge by the mobile operator Orange.
  • The study discusses the data analysis approach in relation to developing a situation of Ebola diffusion built around “the interactions of multi-scale models, including viral loads (at the cellular level), disease progression (at the individual person level), disease propagation (at the workplace and family level), societal changes in migration and travel movements (at the population level), and mitigating interventions (at the abstract government policy level).”
  • The authors argue that the use of their population, mobility, and simulation models provide more accurate simulation details in comparison to high-level analytical predictions and that the D4D mobile datasets provide high-resolution information useful for modeling developing regions and hard to reach locations.

Welle Donker, F., van Loenen, B., & Bregt, A. K. “Open Data and Beyond.” ISPRS International Journal of Geo-Information, 5(4). 2016. http://bit.ly/22YtugY.

  • This research has developed a monitoring framework to assess the effects of open (private) data using a case study of a Dutch energy network administrator Liander.
  • Focusing on the potential impacts of open private energy data – beyond ‘smart disclosure’ where citizens are given information only about their own energy usage – the authors identify three attainable strategic goals:
    • Continuously optimize performance on services, security of supply, and costs;
    • Improve management of energy flows and insight into energy consumption;
    • Help customers save energy and switch over to renewable energy sources.
  • The authors propose a seven-step framework for assessing the impacts of Liander data, in particular, and open private data more generally:
    • Develop a performance framework to describe what the program is about, description of the organization’s mission and strategic goals;
    • Identify the most important elements, or key performance areas which are most critical to understanding and assessing your program’s success;
    • Select the most appropriate performance measures;
    • Determine the gaps between what information you need and what is available;
    • Develop and implement a measurement strategy to address the gaps;
    • Develop a performance report which highlights what you have accomplished and what you have learned;
    • Learn from your experiences and refine your approach as required.
  • While the authors note that the true impacts of this open private data will likely not come into view in the short term, they argue that, “Liander has successfully demonstrated that private energy companies can release open data, and has successfully championed the other Dutch network administrators to follow suit.”

World Economic Forum, 2015. “Data-driven development: pathways for progress.” Geneva: World Economic Forum. http://bit.ly/1JOBS8u

  • This report captures an overview of the existing data deficit and the value and impact of big data for sustainable development.
  • The authors of the report focus on four main priorities towards a sustainable data revolution: commercial incentives and trusted agreements with public- and private-sector actors; the development of shared policy frameworks, legal protections and impact assessments; capacity building activities at the institutional, community, local and individual level; and lastly, recognizing individuals as both produces and consumers of data.

Society’s biggest problems need more than a nudge

Curated on Posted on by Stefaan Verhulst

 at the Conversation: “So-called “nudge units” are popping up in governments all around the world.

The best-known examples include the U.K.’s Behavioural Insights Team, created in 2010, and the White House-based Social and Behavioral Sciences Team, introduced by the Obama administration in 2014. Their mission is to leverage findings from behavioral science so that people’s decisions can be nudged in the direction of their best intentions without curtailing their ability to make choices that don’t align with their priorities.

Overall, these – and other – governments have made important strides when it comes to using behavioral science to nudge their constituents into better choices.

Yet, the same governments have done little to improve their own decision-making processes. Consider big missteps like the Flint water crisis. How could officials in Michigan decide to place an essential service – safe water – and almost 100,000 people at risk in order to save US$100 per day for three months? No defensible decision-making process should have allowed this call to be made.

When it comes to many of the big decisions faced by governments – and the private sector – behavioral science has more to offer than simple nudges.

Behavioral scientists who study decision-making processes could also help policy-makers understand why things went wrong in Flint, and how to get their arms around a wide array of society’s biggest problems – from energy transitions to how to best approach the refugee crisis in Syria.

When nudges are enough

The idea of nudging people in the direction of decisions that are in their own best interest has been around for a while. But it was popularized in 2008 with the publication of the bestseller “Nudge“ by Richard Thaler of the University of Chicago and Cass Sunstein of Harvard.

A common nudge goes something like this: if we want to eat better but are having a hard time doing it, choice architects can reengineer the environment in which we make our food choices so that healthier options are intuitively easier to select, without making it unrealistically difficult to eat junk food if that’s what we’d rather do. So, for example, we can shelve healthy foods at eye level in supermarkets, with less-healthy options relegated to the shelves nearer to the floor….

Sometimes a nudge isn’t enough

Nudges work for a wide array of choices, from ones we face every day to those that we face infrequently. Likewise, nudges are particularly well-suited to decisions that are complex with lots of different alternatives to choose from. And, they are advocated in situations where the outcomes of our decisions are delayed far enough into the future that they feel uncertain or abstract. This describes many of the big decisions policy-makers face, so it makes sense to think the solution must be more nudge units.

But herein lies the rub. For every context where a nudge seems like a realistic option, there’s at least another context where the application of passive decision support would be either be impossible – or, worse, a mistake.

Take, for example, the question of energy transitions. These transitions are often characterized by the move from infrastructure based on fossil fuels to renewables to address all manner of risks, including those from climate change. These are decisions that society makes infrequently. They are complex. And, the outcomes – which are based on our ability to meet conflicting economic, social and environmental objectives – will be delayed.

But, absent regulation that would place severe restrictions on the kinds of options we could choose from – and which, incidentally, would violate the freedom-of-choice tenet of choice architecture – there’s no way to put renewable infrastructure options at proverbial eye level for state or federal decision-makers, or their stakeholders.

Simply put, a nudge for a decision like this would be impossible. In these cases, decisions have to be made the old-fashioned way: with a heavy lift instead of a nudge.

Complex policy decisions like this require what we call active decision support….(More)”

Insights On Collective Problem-Solving: Complexity, Categorization And Lessons From Academia

Curated on Posted on by Stefaan Verhulst

Part 3 of an interview series by Henry Farrell for the MacArthur Research Network on Opening Governance: “…Complexity theorists have devoted enormous energy and attention to thinking about how complex problems, in which different factors interact in ways that are hard to predict, can best be solved. One key challenge is categorizing problems, so as to understand which approaches are best suited to addressing them.

Scott Page is the Leonid Hurwicz Collegiate Professor of Complex Systems at the University of Michigan, Ann Arbor, and one of the world’s foremost experts on diversity and problem-solving. I asked him a series of questions about how we might use insights from academic research to think better about how problem solving works.

Henry: One of the key issues of collective problem-solving is what you call the ‘problem of problems’ – the question of identifying which problems we need to solve. This is often politically controversial – e.g., it may be hard to get agreement that global warming, or inequality, or long prison sentences are a problem. How do we best go about identifying problems, given that people may disagree?

Scott: In a recent big think paper on the potential of diversity for collective problem solving in Scientific American, Katherine Phillips writes that group members must feel validated, that they must share a commitment to the group, and they must have a common goal if they are going to contribute. This implies that you won’t succeed in getting people to collaborate by setting an agenda from on high and then seeking to attract diverse people to further that agenda.

One way of starting to tackle the problem of problems is to steal a rule of thumb from Getting to Yes, by getting to think people about their broad interests rather than the position that they’re starting from. People often agree on their fundamental desires but disagree on how they can be achieved. For example, nearly everyone wants less crime, but they may disagree over whether they think the solution to crime involves tackling poverty or imposing longer prison sentences. If you can get them to focus on their common interest in solving crime rather than their disagreements, you’re more likely to get them to collaborate usefully.

Segregation amplifies the problem of problems. We live in towns and neighborhoods segregated by race, income, ideology, and human capital. Democrats live near Democrats and Republicans near Republicans. Consensus requires integration. We must work across ideologies. Relatedly, opportunity requires more than access. Many people grow up not knowing any engineers, dentists, doctors, lawyers, and statisticians. This isolation narrows the set of careers they consider and it reduces the diversity of many professions. We cannot imagine lives we do not know.

Henry: Once you get past the problem of problems, you still need to identify which kind of problem you are dealing with. You identify three standard types of problems: solution problems, selection problems and optimization problems. What – very briefly – are the key differences between these kinds of problems?

Scott: I’m constantly pondering the potential set of categories in which collective intelligence can emerge. I’m teaching a course on collective intelligence this semester and the undergraduates and I developed an acronym SCARCE PIGS to describe the different types of domains. Here’s the brief summary:

  • Predict: when individuals combine information, models, or measurements to estimate a future event, guess an answer, or classify an event. Examples might involve betting markets, or combined efforts to guess a quantity, such as Francis Galton’s example of people at a fair trying to guess the weight of a steer.
  • Identify: when individuals have local, partial, or possibly erroneous knowledge and collectively can find an object. Here, an example is DARPA’s Red Balloon project.
  • Solve: when individuals apply and possibly combine higher order cognitive processes and analytic tools for the purpose of finding or improving a solution to a task. Innocentive and similar organizations provide examples of this.
  • Generate: when individuals apply diverse representations, heuristics, and knowledge to produce something new. An everyday example is creating a new building.
  • Coordinate: when individuals adopt similar actions, behaviors, beliefs, or mental frameworks by learning through local interactions. Ordinary social conventions such as people greeting each other are good examples.
  • Cooperate: when individuals take actions, not necessarily in their self interest, that collectively produce a desirable outcome. Here, think of managing common pool resources (e.g. fishing boats not overfishing an area that they collectively control).
  • Arrange: when individuals manipulate items in a physical or virtual environment for their own purposes resulting in an organization of that environment. As an example, imagine a student co-op which keeps twenty types of hot sauce in its pantry. If each student puts whichever hot sauce she uses in the front of the pantry, then on average, the hot sauces will be arranged according to popularity, with the most favored hot sauces in the front and the least favored lost in the back.
  • Respond: when individuals react to external or internal stimuli creating collective responses that maintains system level functioning. For example, when yellow jackets attack a predator to maintain the colony, they are displaying this kind of problem solving.
  • Emerge: when individual parts create a whole that has categorically distinct and new functionalities. The most obvious example of this is the human brain….(More)”