Why the Global South should nationalise its data

Curated on Posted on by Stefaan Verhulst

Ulises Ali Mejias at AlJazeera: “The recent coup in Bolivia reminds us that poor countries rich in resources continue to be plagued by the legacy of colonialism. Anything that stands in the way of a foreign corporation’s ability to extract cheap resources must be removed.

Today, apart from minerals and fossil fuels, corporations are after another precious resource: Personal data. As with natural resources, data too has become the target of extractive corporate practices.

As sociologist Nick Couldry and I argue in our book, The Costs of Connection: How Data is Colonizing Human Life and Appropriating It for Capitalism, there is a new form of colonialism emerging in the world: data colonialism. By this, we mean a new resource-grab whereby human life itself has become a direct input into economic production in the form of extracted data.

We acknowledge that this term is controversial, given the extreme physical violence and structures of racism that historical colonialism employed. However, our point is not to say that data colonialism is the same as historical colonialism, but rather to suggest that it shares the same core function: extraction, exploitation, and dispossession.

Like classical colonialism, data colonialism violently reconfigures human relations to economic production. Things like land, water, and other natural resources were valued by native people in the precolonial era, but not in the same way that colonisers (and later, capitalists) came to value them: as private property. Likewise, we are experiencing a situation in which things that were once primarily outside the economic realm – things like our most intimate social interactions with friends and family, or our medical records – have now been commodified and made part of an economic cycle of data extraction that benefits a few corporations.

So what could countries in the Global South do to avoid the dangers of data colonialism?…(More)”.

New Orleans has declared a state of emergency after a cyberattack

Curated on Posted on by Stefaan Verhulst

MIT Technology Review: “The city told its employees to shut down their computers as a precaution this weekend after an attempted cyberattack on Friday.

The news: New Orleans spotted suspicious activity in its networks at around 5 a.m. on Friday, with a spike in the attempted attacks at 8 a.m. It detected phishing attempts and ransomware, Kim LaGrue, the city’s head of IT, later told reporters. Once they were confident the city was under attack, the team shut down its servers and computers. City authorities then filed a declaration of a state of emergency with the Civil District Court, and pulled local, state, and federal authorities into a (still pending) investigation of the incident. The city is still working to recover data from the attack but will be open as usual from this morning, Mayor LaToya Cantrell said on Twitter.

Was it ransomware? The nature of the attack is still something of a mystery. Cantrell confirmed that ransomware had been detected, but the city hasn’t received any demands for ransom money.

The positives: New Orleans was at least fairly well prepared for this attack, thanks to training for this scenario and its ability to operate many of its services without internet access, officials told reporters.

A familiar story: New Orleans is just the latest government to face ransomware attacks, after nearly two dozen cities in Texas were targeted in August, plus Louisiana in November (causing the governor to declare a state of emergency). The phenomenon goes beyond the US, too: in October Johannesburg became the biggest city yet to face a ransomware attack.…(More)”.

Imagery: A better “picture” of the city

Curated on Posted on by Stefaan Verhulst

Daniel Arribas-Bel at Catapult: ‘When trying to understand something as complex as the city, every bit of data helps create a better picture. Researchers, practitioners and policymakers gather as much information as they can to represent every aspect of their city – from noise levels captured by open-source sensors and the study of social isolation using tweets to where the latest hipster coffee shop has opened – exploration and creativity seem to have no limits.

But what about imagery?

You might well ask, what type of images? How do you analyse them? What’s the point anyway?

Let’s start with the why. Images contain visual cues that encode a host of socio-economic information. Imagine a picture of a street with potholes outside a derelict house next to a burnt out car. It may be easy to make some fairly sweeping assumptions about the average income of its resident population. Or the image of a street with a trendy barber-shop next door to a coffee-shop with bare concrete feature walls on one side, and an independent record shop on the other. Again, it may be possible to describe the character of this area.

These are just some of the many kinds of signals embedded in image data. In fact, there is entire literature in geography and sociology that document these associations (see, for example, Cityscapes by Daniel Aaron Silver and Terry Nichols Clark for a sociology approach and The Predictive Postcode by Richard Webber and Roger Burrows for a geography perspective). Imagine if we could figure out ways to condense such information into formal descriptors of cities that help us measure aspects that traditional datasets can’t, or to update them more frequently than standard sources currently allow…(More)”.

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)”.