Stefaan Verhulst

Springwise: “Noticing that a global, collaborative effort is missing in the world of HIV vaccine research, scientists came together to make it a reality. Populated by research from the Collaboration for AIDS Vaccine Discovery — an international network of laboratories — DataSpace is a partnership between the Statistical Center for HIV/AIDS Research and Prevention, data management and software development company LabKey, and technology product development company Artefact.

Through pooled research results, scientists hope to make data more accessible and comparable. Two aspects make the platform particularly powerful. The Artefact team hand-coded a number of research points to allow results from multiple studies to be compared like-for-like. And rather than discard the findings of failed or inconclusive studies, DataSpace includes them in analysis, vastly increasing the volume of available information.

Material is added as study results become available, creating a constantly developing resource. Being able to quickly test ideas online helps researchers make serendipitous connections and avoid duplicating efforts….(More)”

Mark Moritz at the Conversation: “Big data offers us a window on the world. But large and easily available datasets may not show us the world we live in. For instance, epidemiological models of the recent Ebola epidemic in West Africa using big data consistently overestimated the risk of the disease’s spread and underestimated the local initiatives that played a critical role in controlling the outbreak.

Researchers are rightly excited about the possibilities offered by the availability of enormous amounts of computerized data. But there’s reason to stand back for a minute to consider what exactly this treasure trove of information really offers. Ethnographers like me use a cross-cultural approach when we collect our data because family, marriage and household mean different things in different contexts. This approach informs how I think about big data.

We’ve all heard the joke about the drunk who is asked why he is searching for his lost wallet under the streetlight, rather than where he thinks he dropped it. “Because the light is better here,” he said.

This “streetlight effect” is the tendency of researchers to study what is easy to study. I use this story in my course on Research Design and Ethnographic Methods to explain why so much research on disparities in educational outcomes is done in classrooms and not in students’ homes. Children are much easier to study at school than in their homes, even though many studies show that knowing what happens outside the classroom is important. Nevertheless, schools will continue to be the focus of most research because they generate big data and homes don’t.

The streetlight effect is one factor that prevents big data studies from being useful in the real world – especially studies analyzing easily available user-generated data from the Internet. Researchers assume that this data offers a window into reality. It doesn’t necessarily.

Looking at WEIRDOs

Based on the number of tweets following Hurricane Sandy, for example, it might seem as if the storm hit Manhattan the hardest, not the New Jersey shore. Another example: the since-retired Google Flu Trends, which in 2013 tracked online searches relating to flu symptoms to predict doctor visits, but gave estimates twice as high as reports from the Centers for Disease Control and Prevention. Without checking facts on the ground, researchers may fool themselves into thinking that their big data models accurately represent the world they aim to study.

The problem is similar to the “WEIRD” issue in many research studies. Harvard professor Joseph Henrich and colleagues have shown that findings based on research conducted with undergraduates at American universities – whom they describe as “some of the most psychologically unusual people on Earth” – apply only to that population and cannot be used to make any claims about other human populations, including other Americans. Unlike the typical research subject in psychology studies, they argue, most people in the world are not from Western, Educated, Industrialized, Rich and Democratic societies, i.e., WEIRD.

Twitter users are also atypical compared with the rest of humanity, giving rise to what our postdoctoral researcher Sarah Laborde has dubbed the “WEIRDO” problem of data analytics: most people are not Western, Educated, Industrialized, Rich, Democratic and Online.

Context is critical

Understanding the differences between the vast majority of humanity and that small subset of people whose activities are captured in big data sets is critical to correct analysis of the data. Considering the context and meaning of data – not just the data itself – is a key feature of ethnographic research, argues Michael Agar, who has written extensively about how ethnographers come to understand the world….(https://theconversation.com/big-datas-streetlight-effect-where-and-how-we-look-affects-what-we-see-58122More)”

Maroš Krivý at Planning Theory: “The smart city has become a hegemonic notion of urban governance, transforming and supplanting planning. The first part of this article reviews current critiques of this notion. Scholars present three main arguments against the smart city: that it is incompatible with an informal character of the city, that it subjects the city to corporate power and that it reproduces social and urban inequalities. It is argued that these critiques either misunderstand how power functions in the smart city or fail to address it as a specific modality of entrepreneurial urban governance. The second part advances an alternative critique, contending that the smart city should be understood as an urban embodiment of the society of control (Deleuze). The smart city is embedded in the intellectual framework of second order cybernetics and articulates urban subjectivity in terms of data flows. Planning as a political practice is superseded by an environmental-behavioural control, in which subjectivity is articulated supra-individually (permeating the city with sensing nodes) and infra-individually (making citizens into sensing nodes)….(More)”

Stephen Engelberg in ProPublica: “In 2013, ProPublica released Prescriber Checkup, a database that detailed the prescribing habits of hundreds of thousands of doctors across the country.

ProPublica reporters used the data — which reflected prescriptions covered by Medicare’s massive drug program, known as part D — to uncover several important findings. The data showed doctors often prescribed narcotic painkillers and antipsychotic drugs in quantities that could be dangerous for their patients, many of whom were elderly. The reporters also found evidence that some doctors wrote far, far more prescriptions than their peers for expensive brand-name drugs for which there were cheaper generic alternatives. And we found instances of probable fraud that had gone undetected by the government.

The data proved equally useful for others: Doctors themselves turned to Prescriber Checkup to assess how they compared to their peers. Medical plan administrators and hospitals checked it to see whether their doctors were following best practices in treating patients. Law enforcement officials searched the database for leads on fraud and illicit trafficking in pain medications. Patients turned to the data to vet their doctors’ drug choices and compare them with others in their specialties.

Recently, though, we picked up clear signs that some readers are using the data for another purpose: To search for doctors likely to prescribe them some widely abused drugs, many of them opioids.

Like nearly everyone on the web, we use Google Analytics to collect data on our site. So far this year, it appears that perhaps as many as 25 percent of Prescriber Checkup’s page views involve narcotic painkillers, anti-anxiety medications, and amphetamines….(More)”

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

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

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

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

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

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

When nudges are enough

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

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

Sometimes a nudge isn’t enough

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

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

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

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

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

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

Paper by Jacob Metcalf and Kate Crawford: “There are growing discontinuities between the research practices of data science and established tools of research ethics regulation. Some of the core commitments of existing research ethics regulations, such as the distinction between research and practice, cannot be cleanly exported from biomedical research to data science research. These discontinuities have led some data science practitioners and researchers to move toward rejecting ethics regulations outright. These shifts occur at the same time as a proposal for major revisions to the Common Rule — the primary regulation governing human-subjects research in the U.S. — is under consideration for the first time in decades. We contextualize these revisions in long-running complaints about regulation of social science research, and argue data science should be understood as continuous with social sciences in this regard. The proposed regulations are more flexible and scalable to the methods of non-biomedical research, but they problematically exclude many data science methods from human-subjects regulation, particularly uses of public datasets. The ethical frameworks for big data research are highly contested and in flux, and the potential harms of data science research are unpredictable. We examine several contentious cases of research harms in data science, including the 2014 Facebook emotional contagion study and the 2016 use of geographical data techniques to identify the pseudonymous artist Banksy. To address disputes about human-subjects research ethics in data science,critical data studies should offer a historically nuanced theory of “data subjectivity” responsive to the epistemic methods, harms and benefits of data science and commerce….(More)”

Medha Basu at GovInsider: “Jakarta is predicting floods and traffic using complaints data, and plans to do so for dengue as well.

Its Smart City Unit has partnered with startup Qlue to build a dashboard, analysing data from online complaints, sensors and traffic apps. “Our algorithms can predict several things related to our reports such as flood, traffic, and others”, Qlue co-founder and CEO Rama Raditya told GovInsider.

Take floods, for instance. Using trends in complaints from citizens, water level history from sensors and weather data, it can predict the intensity of floods in specific locations next year. “They can predict what will happen when they compare the weather with the flood conditions from last year”, he said.

The city will start to predict dengue hotspots from next year, Rama said. The dashboard was not originally looking at dengue, but after receiving “thousands of complaints on dengue locations”, the government is now looking into this data. “Next year our algorithm will allow the government to know before it happens so they can prepare the amount of medication and so on within each district,” he said.

The dashboard is paired with an app. The app started with collecting citizens’ complaints and has been expanding with new features. It now has a virtual reality section to explore tourist sites in the city. Next week it is launching an augmented reality feature giving directions to nearby ATMs, restaurants,mosques and parks, Rama said.

Qlue has become a strategic part of the Jakarta administration, with the Governor himself using it to decide who to fire and promote. Following its rise in the capital city, it is now being used by 12 other cities across Indonesia: Bandung, Makassar, Bali, Manado, Surabaya, Bogor, Depok, Palembang, Bekasi,Yogyakarta, Riau and Semarang….(More)