Engaging citizens in determining the appropriate conditions and purposes for re-using Health Data

Curated on Posted on by Stefaan Verhulst

Beth Noveck at The GovLab: “…The term, big health data, refers to the ability to gather and analyze vast quantities of online information about health, wellness and lifestyle. It includes not only our medical records but data from apps that track what we buy, how often we exercise and how well we sleep, among many other things. It provides an ocean of information about how healthy or ill we are, and unsurprisingly, doctors, medical researchers, healthcare organizations, insurance companies and governments are keen to get access to it. Should they be allowed to?

It’s a huge question, and AARP is partnering with GovLab to learn what older Americans think about it. AARP is a non-profit organization — the largest in the nation and the world — dedicated to empowering Americans to choose how they live as they age. In 2018 it had more than 38 million members. It is a key voice in policymaking in the United States, because it represents the views of people aged over 50 in this country.

From today, AARP and the GovLab are using the Internet to capture what AARP members feel are the most urgent issues confronting them to try to discover what worries people most: the use of big health data or the failure to use it.

The answers are not simple. On the one hand, increasing the use and sharing of data could enable doctors to make better diagnoses and interventions to prevent disease and make us healthier. It could lead medical researchers to find cures faster, while the creation of health data businesses could strengthen the economy.

On the other hand, the collection, sharing, and use of big health data could reveal sensitive personal information over which we have little control. This data could be sold without our consent, and be used by entities for surveillance or discrimination, rather than to promote well-being….(More)”.

What are hidden data treasuries and how can they help development outcomes?

Curated on Posted on by Stefaan Verhulst

Blogpost by Damien Jacques et al: “Cashew nuts in Burkina Faso can be seen growing from space. Such is the power of satellite technology, it’s now possible to observe the changing colors of fields as crops slowly ripen.

This matters because it can be used as an early warning of crop failure and food crisis – giving governments and aid agencies more time to organize a response.

Our team built an exhaustive crop type and yield estimation map in Burkina Faso, using artificial intelligence and satellite images from the European Space Agency. 

But building the map would not have been possible without a data set that GIZ, the German government’s international development agency, had collected for one purpose on the ground some years before – and never looked at again.

At Dalberg, we call this a “hidden data treasury” and it has huge potential to be used for good. 

Unlocking data potential

In the records of the GIZ Data Lab, the GPS coordinates and crop yield measurements of just a few hundred cashew fields were sitting dormant.

They’d been collected in 2015 to assess the impact of a program to train farmers. But through the power of machine learning, that data set has been given a new purpose.

Using Dalberg Data Insights’ AIDA platform, our team trained algorithms to analyze satellite images for cashew crops, track the crops’ color as they ripen, and from there, estimate yields for the area covered by the data.

From this, it’s now possible to predict crop failures for thousands of fields.

We believe this “recycling” of old data, when paired with artificial intelligence, can help to bridge the data gaps in low-income countries and meet the UN’s Sustainable Development Goals….(More)”.

How randomised trials became big in development economics

Curated on Posted on by Stefaan Verhulst

Seán Mfundza Muller, Grieve Chelwa, and Nimi Hoffmann at the Conversation: “…One view of the challenge of development is that it is fundamentally about answering causal questions. If a country adopts a particular policy, will that cause an increase in economic growth, a reduction in poverty or some other improvement in the well-being of citizens?

In recent decades economists have been concerned about the reliability of previously used methods for identifying causal relationships. In addition to those methodological concerns, some have argued that “grand theories of development” are either incorrect or at least have failed to yield meaningful improvements in many developing countries.

Two notable examples are the idea that developing countries may be caught in a poverty trap that requires a “big push” to escape and the view that institutions are key for growth and development.

These concerns about methods and policies provided a fertile ground for randomised experiments in development economics. The surge of interest in experimental approaches in economics began in the early 1990s. Researchers began to use “natural experiments”, where for example random variation was part of a policy rather than decided by a researcher, to look at causation.

But it really gathered momentum in the 2000s, with researchers such as the Nobel awardees designing and implementing experiments to study a wide range of microeconomic questions.

Randomised trials

Proponents of these methods argued that a focus on “small” problems was more likely to succeed. They also argued that randomised experiments would bring credibility to economic analysis by providing a simple solution to causal questions.

These experiments randomly allocate a treatment to some members of a group and compare the outcomes against the other members who did not receive treatment. For example, to test whether providing credit helps to grow small firms or increase their likelihood of success, a researcher might partner with a financial institution and randomly allocate credit to applicants that meet certain basic requirements. Then a year later the researcher would compare changes in sales or employment in small firms that received the credit to those that did not.

Randomised trials are not a new research method. They are best known for their use in testing new medicines. The first medical experiment to use controlled randomisation occurred in the aftermath of the second world war. The British government used it to assess the effectiveness of a drug for tuberculosis treatment.

In the early 20th century and mid-20th century American researchers had used experiments like this to examine the effects of various social policies. Examples included income protection and social housing.

The introduction of these methods into development economics also followed an increase in their use in other areas of economics. One example was the study of labour markets.

Randomised control trials in economics are now mostly used to evaluate the impact of social policy interventions in poor and middle-income countries. Work by the 2019 Nobel awardees – Michael Kremer, Abhijit Banerjee and Esther Duflo – includes experiments in Kenya and India on teacher attendance, textbook provision, monitoring of nurse attendance and the provision of microcredit.

The popularity, among academics and policymakers, of the approach is not only due to its seeming ability to solve methodological and policy concerns. It is also due to very deliberate, well-funded advocacy by its proponents….(More)”.

Quadratic Payments: A Primer

Curated on Posted on by Stefaan Verhulst

Blogpost by Vitalik Buterin: “If you follow applied mechanism design or decentralized governance at all, you may have recently heard one of a few buzzwords: quadratic voting, quadratic funding and quadratic attention purchase. These ideas have been gaining popularity rapidly over the last few years, and small-scale tests have already been deployed: the Taiwanese presidential hackathon used quadratic voting to vote on winning projects, Gitcoin Grants used quadratic funding to fund public goods in the Ethereum ecosystem, and the Colorado Democratic party also experimented with quadratic voting to determine their party platform.

To the proponents of these voting schemes, this is not just another slight improvement to what exists. Rather, it’s an initial foray into a fundamentally new class of social technology which, has the potential to overturn how we make many public decisions, large and small. The ultimate effect of these schemes rolled out in their full form could be as deeply transformative as the industrial-era advent of mostly-free markets and constitutional democracy. But now, you may be thinking: “These are large promises. What do these new governance technologies have that justifies such claims?”…(More)”.

Public Entrepreneurship and Policy Engineering

Curated on Posted on by Stefaan Verhulst

Essay by Beth Noveck at Communications of the ACM: “Science and technology have progressed exponentially, making it possible for humans to live longer, healthier, more creative lives. The explosion of Internet and mobile phone technologies have increased trade, literacy, and mobility. At the same time, life expectancy for the poor has not increased and is declining.

As science fiction writer William Gibson famously quipped, the future is here, but unevenly distributed. With urgent problems from inequality to climate change, we must train more passionate and innovative people—what I call public entrepreneurs—to learn how to leverage new technology to tackle public problems. Public problems are those compelling and important challenges where neither the problem is well understood nor the solution agreed upon, yet we must devise and implement approaches, often from different disciplines, in an effort to improve people’s lives….(More)”.

Rosie the Robot: Social accountability one tweet at a time

Curated on Posted on by Stefaan Verhulst

Blogpost by Yasodara Cordova and Eduardo Vicente Goncalvese: “Every month in Brazil, the government team in charge of processing reimbursement expenses incurred by congresspeople receives more than 20,000 claims. This is a manually intensive process that is prone to error and susceptible to corruption. Under Brazilian law, this information is available to the public, making it possible to check the accuracy of this data with further scrutiny. But it’s hard to sift through so many transactions. Fortunately, Rosie, a robot built to analyze the expenses of the country’s congress members, is helping out.

Rosie was born from Operação Serenata de Amor, a flagship project we helped create with other civic hackers. We suspected that data provided by members of Congress, especially regarding work-related reimbursements, might not always be accurate. There were clear, straightforward reimbursement regulations, but we wondered how easily individuals could maneuver around them. 

Furthermore, we believed that transparency portals and the public data weren’t realizing their full potential for accountability. Citizens struggled to understand public sector jargon and make sense of the extensive volume of data. We thought data science could help make better sense of the open data  provided by the Brazilian government.

Using agile methods, specifically Domain Driven Design, a flexible and adaptive process framework for solving complex problems, our group started studying the regulations, and converting them into  software code. We did this by reverse-engineering the legal documents–understanding the reimbursement rules and brainstorming ways to circumvent them. Next, we thought about the traces this circumvention would leave in the databases and developed a way to identify these traces using the existing data. The public expenses database included the images of the receipts used to claim reimbursements and we could see evidence of expenses, such as alcohol, which weren’t allowed to be paid with public money. We named our creation, Rosie.

This method of researching the regulations to then translate them into software in an agile way is called Domain-Driven Design. Used for complex systems, this useful approach analyzes the data and the sector as an ecosystem, and then uses observations and rapid prototyping to generate and test an evolving model. This is how Rosie works. Rosie sifts through the reported data and flags specific expenses made by representatives as “suspicious.” An example could be purchases that indicate the Congress member was in two locations on the same day and time.

After finding a suspicious transaction, Rosie then automatically tweets the results to both citizens and congress members.  She invites citizens to corroborate or dismiss the suspicions, while also inviting congress members to justify themselves.

Rosie isn’t working alone. Beyond translating the law into computer code, the group also created new interfaces to help citizens check up on Rosie’s suspicions. The same information that was spread in different places in official government websites was put together in a more intuitive, indexed and machine-readable platform. This platform is called Jarbas – its name was inspired by the AI system that controls Tony Stark’s mansion in Iron Man, J.A.R.V.I.S. (which has origins in the human “Jarbas”) – and it is a website and API (application programming interface) that helps citizens more easily navigate and browse data from different sources. Together, Rosie and Jarbas helps citizens use and interpret the data to decide whether there was a misuse of public funds. So far, Rosie has tweeted 967 times. She is particularly good at detecting overpriced meals. According to an open research, made by the group, since her introduction, members of Congress have reduced spending on meals by about ten percent….(More)”.

Technology & the Law of Corporate Responsibility – The Impact of Blockchain

Curated on Posted on by Stefaan Verhulst

Blogpost by Elizabeth Boomer: “Blockchain, a technology regularly associated with digital currency, is increasingly being utilized as a corporate social responsibility tool in major international corporations. This intersection of law, technology, and corporate responsibility was addressed earlier this month at the World Bank Law, Justice, and Development Week 2019, where the theme was Rights, Technology and Development. The law related to corporate responsibility for sustainable development is increasingly visible due in part to several lawsuits against large international corporations, alleging the use of child and forced labor. In addition, the United Nations has been working for some time on a treaty on business and human rights to encourage corporations to avoid “causing or contributing to adverse human rights impacts through their own activities and [to] address such impacts when they occur.”

DeBeers, Volvo, and Coca-Cola, among other industry leaders, are using blockchain, a technology that allows digital information to be distributed and analyzed, but not copied or manipulated, to trace the source of materials and better manage their supply chains. These initiatives have come as welcome news in industries where child or forced labor in the supply chain can be hard to detect, e.g. conflict minerals, sugar, tobacco, and cacao. The issue is especially difficult when trying to trace the mining of cobalt for lithium ion batteries, increasingly used in electric cars, because the final product is not directly traceable to a single source.

While non governmental organizations (NGOs) have been advocating for improved corporate performance in supply chains regarding labor and environmental standards for years, blockchain may be a technological tool that could reliably trace information regarding various products – from food to minerals – that go through several layers of suppliers before being certified as slave- or child labor- free.

Child labor and forced labor are still common in some countries. The majority of countries worldwide have ratified International Labour Organization (ILO) Convention No. 182, prohibiting the worst forms of child labor (186 ratifications), as well as the ILO Convention prohibiting forced labor (No. 29, with 178 ratifications), and the abolition of forced labor (Convention No. 105, with 175 ratifications). However, the ILO estimates that approximately 40 million men and women are engaged in modern day slavery and 152 million children are subject to child labor, 38% of whom are working in hazardous conditions. The enduring existence of forced labor and child labor raises difficult ethical questions, because in many contexts, the victim does not have a viable alternative livelihood….(More)”.

Seeing Like a Finite State Machine

Curated on Posted on by Stefaan Verhulst

Henry Farrell at the Crooked Timber: “…So what might a similar analysis say about the marriage of authoritarianism and machine learning? Something like the following, I think. There are two notable problems with machine learning. One – that while it can do many extraordinary things, it is not nearly as universally effective as the mythology suggests. The other is that it can serve as a magnifier for already existing biases in the data. The patterns that it identifies may be the product of the problematic data that goes in, which is (to the extent that it is accurate) often the product of biased social processes. When this data is then used to make decisions that may plausibly reinforce those processes (by singling e.g. particular groups that are regarded as problematic out for particular police attention, leading them to be more liable to be arrested and so on), the bias may feed upon itself.

This is a substantial problem in democratic societies, but it is a problem where there are at least some counteracting tendencies. The great advantage of democracy is its openness to contrary opinions and divergent perspectives. This opens up democracy to a specific set of destabilizing attacks but it also means that there are countervailing tendencies to self-reinforcing biases. When there are groups that are victimized by such biases, they may mobilize against it (although they will find it harder to mobilize against algorithms than overt discrimination). When there are obvious inefficiencies or social, political or economic problems that result from biases, then there will be ways for people to point out these inefficiencies or problems.

These correction tendencies will be weaker in authoritarian societies; in extreme versions of authoritarianism, they may barely even exist. Groups that are discriminated against will have no obvious recourse. Major mistakes may go uncorrected: they may be nearly invisible to a state whose data is polluted both by the means employed to observe and classify it, and the policies implemented on the basis of this data. A plausible feedback loop would see bias leading to error leading to further bias, and no ready ways to correct it. This of course, will be likely to be reinforced by the ordinary politics of authoritarianism, and the typical reluctance to correct leaders, even when their policies are leading to disaster. The flawed ideology of the leader (We must all study Comrade Xi thought to discover the truth!) and of the algorithm (machine learning is magic!) may reinforce each other in highly unfortunate ways.

In short, there is a very plausible set of mechanisms under which machine learning and related techniques may turn out to be a disaster for authoritarianism, reinforcing its weaknesses rather than its strengths, by increasing its tendency to bad decision making, and reducing further the possibility of negative feedback that could help correct against errors. This disaster would unfold in two ways. The first will involve enormous human costs: self-reinforcing bias will likely increase discrimination against out-groups, of the sort that we are seeing against the Uighur today. The second will involve more ordinary self-ramifying errors, that may lead to widespread planning disasters, which will differ from those described in Scott’s account of High Modernism in that they are not as immediately visible, but that may also be more pernicious, and more damaging to the political health and viability of the regime for just that reason….(More)”

The Right to Be Seen

Curated on Posted on by Stefaan Verhulst

Anne-Marie Slaughter and Yuliya Panfil at Project Syndicate: “While much of the developed world is properly worried about myriad privacy outrages at the hands of Big Tech and demanding – and securing – for individuals a “right to be forgotten,” many around the world are posing a very different question: What about the right to be seen?

Just ask the billion people who are locked out of services we take for granted – things like a bank account, a deed to a house, or even a mobile phone account – because they lack identity documents and thus can’t prove who they are. They are effectively invisible as a result of poor data.

The ability to exercise many of our most basic rights and privileges – such as the right to vote, drive, own property, and travel internationally – is determined by large administrative agencies that rely on standardized information to determine who is eligible for what. For example, to obtain a passport it is typically necessary to present a birth certificate. But what if you do not have a birth certificate? To open a bank account requires proof of address. But what if your house doesn’t have an address?

The inability to provide such basic information is a barrier to stability, prosperity, and opportunity. Invisible people are locked out of the formal economy, unable to vote, travel, or access medical and education benefits. It’s not that they are undeserving or unqualified, it’s that they are data poor.

In this context, the rich digital record provided by our smartphones and other sensors could become a powerful tool for good, so long as the risks are acknowledged. These gadgets, which have become central to our social and economic lives, leave a data trail that for many of us is the raw material that fuels what Harvard’s Shoshana Zuboff calls “surveillance capitalism.” Our Google location history shows exactly where we live and work. Our email activity reveals our social networks. Even the way we hold our smartphone can give away early signs of Parkinson’s.

But what if citizens could harness the power of these data for themselves, to become visible to administrative gatekeepers and access the rights and privileges to which they are entitled? Their virtual trail could then be converted into proof of physical facts.

That is beginning to happen. In India, slum dwellers are using smartphone location data to put themselves on city maps for the first time and register for addresses that they can then use to receive mail and register for government IDs. In Tanzania, citizens are using their mobile payment histories to build their credit scores and access more traditional financial services. And in Europe and the United States, Uber drivers are fighting for their rideshare data to advocate for employment benefits….(More)”.

Why Data Is Not the New Oil

Curated on Posted on by Stefaan Verhulst

Blogpost by Alec Stapp: “Data is the new oil,” said Jaron Lanier in a recent op-ed for The New York Times. Lanier’s use of this metaphor is only the latest instance of what has become the dumbest meme in tech policy. As the digital economy becomes more prominent in our lives, it is not unreasonable to seek to understand one of its most important inputs. But this analogy to the physical economy is fundamentally flawed. Worse, introducing regulations premised upon faulty assumptions like this will likely do far more harm than good. Here are seven reasons why “data is the new oil” misses the mark:

1. Oil is rivalrous; data is non-rivalrous

If someone uses a barrel of oil, it can’t be consumed again. But, as Alan McQuinn, a senior policy analyst at the Information Technology and Innovation Foundation, noted, “when consumers ‘pay with data’ to access a website, they still have the same amount of data after the transaction as before. As a result, users have an infinite resource available to them to access free online services.” Imposing restrictions on data collection makes this infinite resource finite. 

2. Oil is excludable; data is non-excludable

Oil is highly excludable because, as a physical commodity, it can be stored in ways that prevent use by non-authorized parties. However, as my colleagues pointed out in a recent comment to the FTC: “While databases may be proprietary, the underlying data usually is not.” They go on to argue that this can lead to under-investment in data collection:

[C]ompanies that have acquired a valuable piece of data will struggle both to prevent their rivals from obtaining the same data as well as to derive competitive advantage from the data. For these reasons, it also  means that firms may well be more reluctant to invest in data generation than is socially optimal. In fact, to the extent this is true there is arguably more risk of companies under-investing in data  generation than of firms over-investing in order to create data troves with which to monopolize a market. This contrasts with oil, where complete excludability is the norm.

3. Oil is fungible; data is non-fungible

Oil is a commodity, so, by definition, one barrel of oil of a given grade is equivalent to any other barrel of that grade. Data, on the other hand, is heterogeneous. Each person’s data is unique and may consist of a practically unlimited number of different attributes that can be collected into a profile. This means that oil will follow the law of one price, while a dataset’s value will be highly contingent on its particular properties and commercialization potential.

4. Oil has positive marginal costs; data has zero marginal costs

There is a significant expense to producing and distributing an additional barrel of oil (as low as $5.49 per barrel in Saudi Arabia; as high as $21.66 in the U.K.). Data is merely encoded information (bits of 1s and 0s), so gathering, storing, and transferring it is nearly costless (though, to be clear, setting up systems for collecting and processing can be a large fixed cost). Under perfect competition, the market clearing price is equal to the marginal cost of production (hence why data is traded for free services and oil still requires cold, hard cash)….(More)”.