4 reasons why Data Collaboratives are key to addressing migration

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Stefaan Verhulst and Andrew Young at the Migration Data Portal: “If every era poses its dilemmas, then our current decade will surely be defined by questions over the challenges and opportunities of a surge in migration. The issues in addressing migration safely, humanely, and for the benefit of communities of origin and destination are varied and complex, and today’s public policy practices and tools are not adequate. Increasingly, it is clear, we need not only new solutions but also new, more agile, methods for arriving at solutions.

Data are central to meeting these challenges and to enabling public policy innovation in a variety of ways. Yet, for all of data’s potential to address public challenges, the truth remains that most data generated today are in fact collected by the private sector. These data contains tremendous possible insights and avenues for innovation in how we solve public problems. But because of access restrictions, privacy concerns and often limited data science capacity, their vast potential often goes untapped.

Data Collaboratives offer a way around this limitation.

Data Collaboratives: A new form of Public-Private Partnership for a Data Age

Data Collaboratives are an emerging form of partnership, typically between the private and public sectors, but often also involving civil society groups and the education sector. Now in use across various countries and sectors, from health to agriculture to economic development, they allow for the opening and sharing of information held in the private sector, in the process freeing data silos up to serve public ends.

Although still fledgling, we have begun to see instances of Data Collaboratives implemented toward solving specific challenges within the broad and complex refugee and migrant space. As the examples we describe below suggest (which we examine in more detail Stanford Social Innovation Review), the use of such Collaboratives is geographically dispersed and diffuse; there is an urgent need to pull together a cohesive body of knowledge to more systematically analyze what works, and what doesn’t.

This is something we have started to do at the GovLab. We have analyzed a wide variety of Data Collaborative efforts, across geographies and sectors, with a goal of understanding when and how they are most effective.

The benefits of Data Collaboratives in the migration field

As part of our research, we have identified four main value propositions for the use of Data Collaboratives in addressing different elements of the multi-faceted migration issue. …(More)”,

Most Maps of the New Ebola Outbreak Are Wrong

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Ed Kong in The Atlantic: “Almost all the maps of the outbreak zone that have thus far been released contain mistakes of this kind. Different health organizations all seem to use their own maps, most of which contain significant discrepancies. Things are roughly in the right place, but their exact positions can be off by miles, as can the boundaries between different regions.

Sinai, a cartographer at UCLA, has been working with the Ministry of Health to improve the accuracy of the Congo’s maps, and flew over on Saturday at their request. For each health zone within the outbreak region, Sinai compiled a list of the constituent villages, plotted them using the most up-to-date sources of geographical data, and drew boundaries that include these places and no others. The maps at the top of this piece show the before (left) and after (right) images….

Consider Bikoro, the health zone where the outbreak may have originated, and where most cases are found. Sinai took a list of all Bikoro’s villages, plotted them using the most up-to-date sources of geographical data, and drew a boundary that includes these places and no others. This new shape is roughly similar to the one on current maps, but with critical differences. Notably, existing maps have the village of Ikoko Impenge—one of the epicenters of the outbreak—outside the Bikoro health zone, when it actually lies within the zone.

 “These visualizations are important for communicating the reality on the ground to all levels of the health hierarchy, and to international partners who don’t know the country,” says Mathias Mossoko, the head of disease surveillance data in DRC.

“It’s really important for the outbreak response to have real and accurate data,” adds Bernice Selo, who leads the cartographic work from the Ministry of Health’s command center in Kinshasa. “You need to know exactly where the villages are, where the health facilities are, where the transport routes and waterways are. All of this helps you understand where the outbreak is, where it’s moving, how it’s moving. You can see which villages have the highest risk.”

To be clear, there’s no evidence that these problems are hampering the response to the current outbreak. It’s not like doctors are showing up in the middle of the forest, wondering why they’re in the wrong place. “Everyone on the ground knows where the health zones start and end,” says Sinai. “I don’t think this will make or break the response. But you surely want the most accurate data.”

It feels unusual to not have this information readily at hand, especially in an era when digital maps are so omnipresent and so supposedly truthful. If you search for San Francisco on Google Maps, you can be pretty sure that what comes up is actually where San Francisco is. On Google Street View, you can even walk along a beach at the other end of the world….(More)”.

But the Congo is a massive country—a quarter the size of the United States with considerably fewer resources. Until very recently, they haven’t had the resources to get accurate geolocalized data. Instead, the boundaries of the health zones and their constituent “health areas,” as well as the position of specific villages, towns, rivers, hospitals, clinics, and other landmarks, are often based on local knowledge and hand-drawn maps. Here’s an example, which I saw when I visited the National Institute for Biomedical Research in March. It does the job, but it’s clearly not to scale.

The Challenge for Business and Society: From Risk to Reward

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Book by Stanley Litow that seeks to provide “A roadmap to improve corporate social responsibility”:  “The 2016 U.S. Presidential Campaign focused a good deal of attention on the role of corporations in society, from both sides of the aisle. In the lead up to the election, big companies were accused of profiteering, plundering the environment, and ignoring (even exacerbating) societal ills ranging from illiteracy and discrimination to obesity and opioid addiction. Income inequality was laid squarely at the feet of us companies. The Trump administration then moved swiftly to scrap fiscal, social, and environmental rules that purportedly hobble business, to redirect or shut down cabinet offices historically protecting the public good, and to roll back clean power, consumer protection, living wage, healthy eating initiatives and even basic public funding for public schools. To many eyes, and the lens of history, this may usher in a new era of cowboy capitalism with big companies, unfettered by regulation and encouraged by the presidential bully pulpit, free to go about the business of making money—no matter the consequences to consumers and the commonwealth. While this may please some companies in the short term, the long term consequences might result in just the opposite.

And while the new administration promises to reduce “foreign aid” and the social safety net, Stanley S. Litow believes big companies will be motivated to step up their efforts to create jobs, reduce poverty, improve education and health, and address climate change issues — both domestically and around the world. For some leaders in the private sector this is not a matter of public relations or charity. It is integral to their corporate strategy—resulting in creating new markets, reducing risks, attracting and retaining top talent, and generating growth and realizing opportunities. Through case studies (many of which the author spearheaded at IBM), The Challenge for Business and Society provides clear guidance for companies to build their own corporate sustainability and social responsibility plans positively effecting their bottom lines producing real return on their investments….(More).

Tech Platforms and the Knowledge Problem

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Frank Pasquale at American Affairs: “Friedrich von Hayek, the preeminent theorist of laissez-faire, called the “knowledge problem” an insuperable barrier to central planning. Knowledge about the price of supplies and labor, and consumers’ ability and willingness to pay, is so scattered and protean that even the wisest authorities cannot access all of it. No person knows everything about how goods and services in an economy should be priced. No central decision-maker can grasp the idiosyncratic preferences, values, and purchasing power of millions of individuals. That kind of knowledge, Hayek said, is distributed.

In an era of artificial intelligence and mass surveillance, however, the possibility of central planning has reemerged—this time in the form of massive firms. Having logged and analyzed billions of transactions, Amazon knows intimate details about all its customers and suppliers. It can carefully calibrate screen displays to herd buyers toward certain products or shopping practices, or to copy sellers with its own, cheaper, in-house offerings. Mark Zuckerberg aspires to omniscience of consumer desires, by profiling nearly everyone on Facebook, Instagram, and WhatsApp, and then leveraging that data trove to track users across the web and into the real world (via mobile usage and device fingerprinting). You don’t even have to use any of those apps to end up in Facebook/Instagram/WhatsApp files—profiles can be assigned to you. Google’s “database of intentions” is legendary, and antitrust authorities around the world have looked with increasing alarm at its ability to squeeze out rivals from search results once it gains an interest in their lines of business. Google knows not merely what consumers are searching for, but also what other businesses are searching, buying, emailing, planning—a truly unparalleled matching of data-processing capacity to raw communication flows.

Nor is this logic limited to the online context. Concentration is paying dividends for the largest banks (widely assumed to be too big to fail), and major health insurers (now squeezing and expanding the medical supply chain like an accordion). Like the digital giants, these finance and insurance firms not only act as middlemen, taking a cut of transactions, but also aspire to capitalize on the knowledge they have gained from monitoring customers and providers in order to supplant them and directly provide services and investment. If it succeeds, the CVS-Aetna merger betokens intense corporate consolidations that will see more vertical integration of insurers, providers, and a baroque series of middlemen (from pharmaceutical benefit managers to group purchasing organizations) into gargantuan health providers. A CVS doctor may eventually refer a patient to a CVS hospital for a CVS surgery, to be followed up by home health care workers employed by CVS who bring CVS pharmaceuticals—allcovered by a CVS/Aetna insurance plan, which might penalize the patient for using any providers outside the CVS network. While such a panoptic firm may sound dystopian, it is a logical outgrowth of health services researchers’ enthusiasm for “integrated delivery systems,” which are supposed to provide “care coordination” and “wraparound services” more efficiently than America’s current, fragmented health care system.

The rise of powerful intermediaries like search engines and insurers may seem like the next logical step in the development of capitalism. But a growing chorus of critics questions the size and scope of leading firms in these fields. The Institute for Local Self-Reliance highlights Amazon’s manipulation of both law and contracts to accumulate unfair advantages. International antitrust authorities have taken Google down a peg, questioning the company’s aggressive use of its search engine and Android operating system to promote its own services (and demote rivals). They also question why Google and Facebook have for years been acquiring companies at a pace of more than two per month. Consumer advocates complain about manipulative advertising. Finance scholars lambaste megabanks for taking advantage of the implicit subsidies that too-big-to-fail status confers….(More)”.

Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

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Paper by Martin Mueller and Marcel Salath: “In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams.

At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community.

Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases…(More)”.

Data Violence and How Bad Engineering Choices Can Damage Society

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Blog by Anna Lauren Hoffmann: “…In 2015, a black developer in New York discovered that Google’s algorithmic photo recognition software had tagged pictures of him and his friends as gorillas.

The same year, Facebook auto-suspended Native Americans for using their real names, and in 2016, facial recognition was found to struggle to read black faces.

Software in airport body scanners has flagged transgender bodies as threatsfor years. In 2017, Google Translate took gender-neutral pronouns in Turkish and converted them to gendered pronouns in English — with startlingly biased results.

“Violence” might seem like a dramatic way to talk about these accidents of engineering and the processes of gathering data and using algorithms to interpret it. Yet just like physical violence in the real world, this kind of “data violence” (a term inspired by Dean Spade’s concept of administrative violence) occurs as the result of choices that implicitly and explicitly lead to harmful or even fatal outcomes.

Those choices are built on assumptions and prejudices about people, intimately weaving them into processes and results that reinforce biases and, worse, make them seem natural or given.

Take the experience of being a woman and having to constantly push back against rigid stereotypes and aggressive objectification.

Writer and novelist Kate Zambreno describes these biases as “ghosts,” a violent haunting of our true reality. “A return to these old roles that we play, that we didn’t even originate. All the ghosts of the past. Ghosts that aren’t even our ghosts.”

Structural bias is reinforced by the stereotypes fed to us in novels, films, and a pervasive cultural narrative that shapes the lives of real women every day, Zambreno describes. This extends to data and automated systems that now mediate our lives as well. Our viewing and shopping habits, our health and fitness tracking, our financial information all conspire to create a “data double” of ourselves, produced about us by third parties and standing in for us on data-driven systems and platforms.

These fabrications don’t emerge de novo, disconnected from history or social context. Rather, they often pick up and unwittingly spit out a tangled mess of historical conditions and current realities.

Search engines are a prime example of how data and algorithms can conspire to amplify racist and sexist biases. The academic Safiya Umoja Noble threw these messy entanglements into sharp relief in her book Algorithms of Oppression. Google Search, she explains, has a history of offering up pages of porn for women from particular racial or ethnic groups, and especially black women. Google have also served up ads for criminal background checksalongside search results for African American–sounding names, as former Federal Trade Commission CTO Latanya Sweeney discovered.

“These search engine results for women whose identities are already maligned in the media, such as Black women and girls, only further debase and erode efforts for social, political, and economic recognition and justice,” Noble says.

These kinds of cultural harms go well beyond search results. Sociologist Rena Bivens has shown how the gender categories employed by platforms like Facebook can inflict symbolic violences against transgender and nonbinary users in ways that may never be made obvious to users….(More)”.

Using Blockchain Technology to Create Positive Social Impact

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Randall Minas in Healthcare Informatics: “…Healthcare is yet another area where blockchain can make a substantial impact. Blockchain technology could be used to enable the WHO and CDC to better monitor disease outbreaks over time by creating distributed “ledgers” that are both secure and updated hundreds of times per day. Issued in near real-time, these updates would alert healthcare professionals to spikes in local cases almost immediately. Additionally, using blockchain would allow accurate diagnosis and streamline the isolation of clusters of cases as quickly as possible. Providing blocks of real-time disease information—especially in urban areas—would be invaluable.

In the United States, disease updates are provided in a Morbidity and Mortality Weekly Report (MMWR) from the CDC. This weekly report provides tables of current disease trends for hospitals and public health officials. Another disease reporting mechanism is the National Outbreak Reporting System (NORS), launched in 2009. NORS’ web-based tool provides outbreak data through 2016 and is accessible to the general public. There are two current weaknesses in the NORS reporting system and both can be addressed by blockchain technology.

The first issue lies in the number of steps required to accurately report each outbreak. A health department reports an outbreak to the NORS system, the CDC checks it for accuracy, analyzes the data, then provides a summary via the MMRW. Instantiating blockchain as the technology through which the NORS data is reported, every health department in the country could have preliminary data on disease trends at their fingertips without having to wait for the next MMRW publication.

The second issue is the inherent cybersecurity vulnerabilities using a web-based platform to monitor disease reporting. As we have seen with cyberattacks both domestic and abroad, cybersecurity vulnerabilities underlie most of our modern-day computing infrastructure. Blockchain was designed to be secure because it is decentralized across many computer networks and, since it was designed as a digital ledger, the previous data (or “blocks”) in the blockchain are difficult to alter.

While the NORS platform could be hacked with malware to gain access to our electricity and water infrastructure, instituting blockchain technology would limit the potential damage of the malware based on the inherent security of the technology. If this does not sound important, imagine the damage and ensuing panic that could be caused by a compromised NORS reporting a widespread Ebola outbreak.

The use of blockchain in monitoring epidemic outbreaks might not only apply to fast-spreading outbreaks like the flu, but also to epidemics that have lasted for decades. Since blockchain allows an unchangeable snapshot of data over time and can be anonymous, partner organizations could provide HIV test results to an individual’s “digital ledger” with a date of the test and the results.

Individuals could then exchange their HIV status securely, in an application, before engaging in high-risk behaviors. Since many municipalities provide free or low-cost, anonymous HIV testing, the use of blockchain would allow disease monitoring and exchange of status in a secure and trusted manner. The LGBTQ community and other high-risk communities could use an application to securely exchange HIV status with potential partners. With widespread adoption of this status-exchange system, an individual’s high-risk exposure could be limited, further reducing the spread of the epidemic.

While much of the creative application around blockchain has focused on supply chain-like models, including distribution of renewable energy and local sourcing of goods, it is important also to think innovatively about how blockchain can be used outside of supply chain and accounting.

In healthcare, blockchain has been discussed frequently in relation to electronic health records (EHRs), yet even that could be underappreciating the technology’s potential. Leaders in the blockchain arena should invest in application development for epidemic monitoring and disease control using blockchain technology. …(More)”.

Why Policymakers Should Care About “Big Data” in Healthcare

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David W.Bates et al at Health Policy and Technology: “The term “big data” has gotten increasing popular attention, and there is growing focus on how such data can be used to measure and improve health and healthcare. Analytic techniques for extracting information from these data have grown vastly more powerful, and they are now broadly available. But for these approaches to be most useful, large amounts of data must be available, and barriers to use should be low. We discuss how “smart cities” are beginning to invest in this area to improve the health of their populations; provide examples around model approaches for making large quantities of data available to researchers and clinicians among other stakeholders; discuss the current state of big data approaches to improve clinical care including specific examples, and then discuss some of the policy issues around and examples of successful regulatory approaches, including deidentification and privacy protection….(More)”.

Citizenship and democratic production

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Article by Mara Balestrini and Valeria Right in Open Democracy: “In the last decades we have seen how the concept of innovation has changed, as not only the ecosystem of innovation-producing agents, but also the ways in which innovation is produced have expanded. The concept of producer-innovation, for example, where companies innovate on the basis of self-generated ideas, has been superseded by the concept of user-innovation, where innovation originates from the observation of the consumers’ needs, and then by the concept of consumer-innovation, where consumers enhanced by the new technologies are themselves able to create their own products. Innovation-related business models have changed too. We now talk about not only patent-protected innovation, but also open innovation and even free innovation, where open knowledge sharing plays a key role.

A similar evolution has taken place in the field of the smart city. While the first smart city models prioritized technology left in the hands of experts as a key factor for solving urban problems, more recent initiatives such as Sharing City (Seoul), Co-city (Bologna), or Fab City (Barcelona) focus on citizen participation, open data economics and collaborative-distributed processes as catalysts for innovative solutions to urban challenges. These initiatives could prompt a new wave in the design of more inclusive and sustainable cities by challenging existing power structures, amplifying the range of solutions to urban problems and, possibly, creating value on a larger scale.

In a context of economic austerity and massive urbanization, public administrations are acknowledging the need to seek innovative alternatives to increasing urban demands. Meanwhile, citizens, harnessing the potential of technologies – many of them accessible through open licenses – are putting their creative capacity into practice and contributing to a wave of innovation that could reinvent even the most established sectors.

Contributive production

The virtuous combination of citizen participation and abilities, digital technologies, and open and collaborative strategies is catalyzing innovation in all areas. Citizen innovation encompasses everything, from work and housing to food and health. The scope of work, for example, is potentially affected by the new processes of manufacturing and production on an individual scale: citizens can now produce small and large objects (new capacity), thanks to easy access to new technologies such as 3D printers (new element); they can also take advantage of new intellectual property licenses by adapting innovations from others and freely sharing their own (new rule) in response to a wide range of needs.

Along these lines, between 2015 and 2016, the city of Bristol launched a citizen innovation program aimed at solving problems related to the state of rented homes, which produced solutions through citizen participation and the use of sensors and open data. Citizens designed and produced themselves temperature and humidity sensors – using open hardware (Raspberry Pi), 3D printers and laser cutters – to combat problems related to home damp. These sensors, placed in the homes, allowed to map the scale of the problem, to differentiate between condensation and humidity, and thus to understand if the problem was due to structural failures of the buildings or to bad habits of the tenants. Through the inclusion of affected citizens, the community felt empowered to contribute ideas towards solutions to its problems, together with the landlords and the City Council.

A similar process is currently being undertaken in Amsterdam, Barcelona and Pristina under the umbrella of the Making Sense Project. In this case, citizens affected by environmental issues are producing their own sensors and urban devices to collect open data about the city and organizing collective action and awareness interventions….

Digital social innovation is disrupting the field of health too. There are different manifestations of these processes. First, platforms such as DataDonors or PatientsLikeMe show that there is an increasing citizen participation in biomedical research through the donation of their own health data…. projects such as OpenCare in Milan and mobile applications like Good Sam show how citizens can organize themselves to provide medical services that otherwise would be very costly or at a scale and granularity that the public sector could hardly afford….

The production processes of these products and services force us to think about their political implications and the role of public institutions, as they question the cities’ existing participation and contribution rules. In times of sociopolitical turbulence and austerity plans such as these, there is a need to design and test new approaches to civic participation, production and management which can strengthen democracy, add value and take into account the aspirations, emotional intelligence and agency of both individuals and communities.

In order for the new wave of citizen production to generate social capital, inclusive innovation and well-being, it is necessary to ensure that all citizens, particularly those from less-represented communities, are empowered to contribute and participate in the design of cities-for-all. It is therefore essential to develop programs to increase citizen access to the new technologies and the acquisition of the knowhow and skills needed to use and transform them….(More)

This piece is an excerpt from an original article published as part of the eBook El ecosistema de la Democracia Abierta.

Health Citizenship: A New Social Contract To Improve The Clinical Trial Process

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Essay by Cynthia Grossman  and Tanisha Carino: “…We call this new social contract health citizenship, which includes a set of implied rights and responsibilities for all parties.

Three fundamental truths underpin our efforts:

  1. The path to better health and the advancement of science begin and end with engaged patients.
  2. The biomedical research enterprise lives all around us — in clinical trials, the data in our wearables, electronic health records, and data used for payment.
  3. The stakeholders that fuel advancement — clinicians, academia, government, the private sector, and investors — must create a system focused on speeding medical research and ensuring that patients have appropriate access to treatments.

To find tomorrow’s cures, treatments, and prevention measures, every aspect of society needs to get involved. Health citizenship recognizes that the future of innovative research and development depends on both patients and the formal healthcare system stepping up to the plate.

Moving Toward A Culture Of Transparency  

Increasing clinical trials registration and posting of research results are steps in the direction of transparency. Access to information about clinical trials — enrollment criteria, endpoints, locations, and results — is critical to empowering patients, their families, and primary care physicians. Also, transparency has a cascading impact on the cost and speed of scientific discovery, through ensuring validation and reproducibility of results…..

Encouraging Data Sharing

Data is the currency of biomedical research, and now patients are poised to contribute more of it than ever. In fact, many patients who participate in clinical research expect that their data will be shared and want to be partners, not just participants, in how data is used to advance the science and clinical practice that impact their disease or condition.

Engaging more patients in data sharing is only one part of what is needed to advance a data-sharing ecosystem. The National Academies of Science, Engineering, and Medicine (formerly the Institute of Medicine) conducted a consensus study that details the challenges to clinical trial data sharing. Out of that study spun a new data-sharing platform, Vivli, which will publicly launch this year. The New England Journal of Medicine took an important step toward demonstrating the value of sharing clinical trial data through its SPRINT Data Challenge, where it opened up a data set and supported projects that sought to derive new insights from the existing data. Examples like these will go a long way toward demonstrating the value of data sharing to advancing science, academic careers, and, most importantly, patient health.

As the technology to share clinical trial data improves, it will become less of an impediment than aligning incentives. The academic environment incentivizes researchers through first author and top-tier journal publications, which contribute to investigators holding on to clinical trial data. A recent publication suggests a way to ensure academic credit, through publication credit, for sharing data sets and allows investigators to tag data sets with unique IDs.

While this effort could assist in incentivizing data sharing, we see the value of tagging data sets as a way to rapidly gather examples of the value of data sharing, including what types of data sets are taken up for analysis and what types of analyses or actions are most valuable. This type of information is currently missing, and, without the value proposition, it is difficult to encourage data sharing behavior.

The value of clinical trial data will need to be collectively reexamined through embracing the sharing of data both across clinical trials and combined with other types of data. Similar to the airline and car manufacturing industries sharing data in support of public safety,7 as more evidence is gathered to support the impact of clinical trial data sharing and as the technology is developed to do this safely and securely, the incentives, resources, and equity issues will need to be addressed collectively…(More)”.