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


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)”.

Civic tech, Data and Demos: exploring the interaction between democracy and technology


IAPP: “The French data protection authority, the CNIL, has published a report that explores emerging issues of data protection and freedoms in democracy, technology and citizen participation. The report discusses the emergence of civic technologies and data protection issues associated. The CNIL also uses the report to propose its recommendations for creating “an environment of trust” with civic tech “that allows everyone to exercise their citizenship while respecting their rights and freedoms.” Those recommendations include more implementation of EU General Data Protection Regulation principles and considerations for improved digital communication and participation….(More)”.

Sifting for Deeper Insights from Public Opinion: Towards Crowdsourcing and Big Data for Project Improvement


Paper by Jean Marie Tshimula et al: “Over the years, there seems to be a unidirectional top-down approach to decision-making in providing social services to the masses. This has often led to poor uninformed decisions being made with outcomes which do not necessarily match needs. Similarly from the grassroots level, it has been challenging to give opinions that reach the governing authorities (decision-making organs). The government consequently sets targets geared towards addressing societal concerns, but which do not often achieve desired results where such government endeavors are not in harmony with societal needs.

With public opinions being heard and given consideration, societal needs can be better known and priorities set to address these concerns. This paper therefore presents a priority-based voting model for governments to collect public opinion data that bring suggestions to boost their endeavors in the right direction using crowdsourcing and big data analytics….(More)”.

Data Protection in the Humanitarian Sector – A Blockchain Approach


Report by Andrej Verity and Irene Solaiman: “Data collection and storage are becoming increasingly digital. In the humanitarian sector, data motivates action, informing organizations who then determine priorities and resource allocation in crises.

“Humanitarians are dependent on technology and on the Internet. When life-saving aid isn’t delivered on time and to the right beneficiaries, people can die.” -Brookings

In the age of information and cyber warfare, humanitarian organizations must take measures to protect civilians, especially those in critical and vulnerable positions.

“Data privacy and ensuring protection from harm, including the provision of data security, are therefore fundamentally linked—and neither can be realized without the other.” -The Signal Code

Information in the wrong hands can risk lives or even force aid organizations to shut down. For example, in 2009, Sudan expelled over a dozen international nongovernmental organizations (NGOs) that were deemed key to maintaining a lifeline to 4.7 million people in western Darfur. The expulsion occurred after the Sudanese Government collected Internet-accessible information that made leadership fear international criminal charges. Responsible data protection is a crucial component of cybersecurity. As technology develops, so do threats and data vulnerabilities. Emerging technologies such as blockchain provide further security to sensitive information and overall data storage. Still, with new technologies come considerations for implementation…(More)”.

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


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)”.

The Politics of Open Government Data: Understanding Organizational Responses to Pressure for More Transparency


Paper by Erna Ruijer et al: “This article contributes to the growing body of literature within public management on open government data by taking
a political perspective. We argue that open government data are a strategic resource of organizations and therefore organizations are not likely to share it. We develop an analytical framework for studying the politics of open government data, based on theories of strategic responses to institutional processes, government transparency, and open government data. The framework shows that there can be different organizational strategic responses to open data—varying from conformity to active resistance—and that different institutional antecedents influence these responses. The value of the framework is explored in two cases: a province in the Netherlands and a municipality in France. The cases provide insights into why governments might release datasets in certain policy domains but not in others thereby producing “strategically opaque transparency.” The article concludes that the politics of open government data framework helps us understand open data practices in relation to broader institutional pressures that influence government transparency….(More)”.

The Pledging Puzzle: How Can Revocable Promises Increase Charitable Giving


Paper by James Andreoni and Marta Serra-Garcia: “What is the value of pledges if they are often reneged upon? In this paper we show – both theoretically and experimentally – that pledges can be used to screen donors and to better understand their motives for giving. In return, nonprofit managers can use the information they glean from pledges to better target future charitable giving appeals and interventions to donors, such as expressions of gratitude. In an experiment, we find that offering the option to pledge gifts induces self-selection. If expressions of gratitude are then targeted to individuals who select into pledges, reneging can be significantly reduced. Our findings provide an explanation for the potential usefulness of pledges….(More)”.

How randomised trials became big in development economics


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 attendancetextbook provisionmonitoring 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)”.

Statistical comfort distorts our politics


Wolfgang Münchau at the Financial Times: “…So how should we deal with data and statistics in areas where we are not experts?

My most important advice is to treat statistics as tools to help you ask questions, not to answer them. If you have to seek answers from data, make sure that you understand the issues and that the data are independently verified by people with no skin in the game.

What I am saying here is issuing a plea for perspective, not a rant against statistics. On the contrary. I am in awe of mathematical statistics and its theoretical foundations.

Modern statistics has a profound impact on our daily lives. I rely on Google’s statistical translation technology to obtain information from Danish newspapers, for example.  Statistical advances allow our smartphone cameras to see in the dark, or a medical imaging device to detect a disease. But political data are of a much more uncertain quality. In political discussions, especially on social networks, statistics are used almost entirely to confirm political biases or as weapons in an argument. To the extent that this is so, you are better off without them….(More)”.

Responsible Operations: Data Science, Machine Learning, and AI in Libraries


OCLC Research Position Paper by Thomas Padilla: “Despite greater awareness, significant gaps persist between concept and operationalization in libraries at the level of workflows (managing bias in probabilistic description), policies (community engagement vis-à-vis the development of machine-actionable collections), positions (developing staff who can utilize, develop, critique, and/or promote services influenced by data science, machine learning, and AI), collections (development of “gold standard” training data), and infrastructure (development of systems that make use of these technologies and methods). Shifting from awareness to operationalization will require holistic organizational commitment to responsible operations. The viability of responsible operations depends on organizational incentives and protections that promote constructive dissent…(More)”.