Stefaan Verhulst
Heidi Ledford at Nature: “Elizaveta Sivak spent nearly a decade training as a sociologist. Then, in the middle of a research project, she realized that she needed to head back to school.
Sivak studies families and childhood at the National Research University Higher School of Economics in Moscow. In 2015, she studied the movements of adolescents by asking them in a series of interviews to recount ten places that they had visited in the past five days. A year later, she had analysed the data and was feeling frustrated by the narrowness of relying on individual interviews, when a colleague pointed her to a paper analysing data from the Copenhagen Networks Study, a ground-breaking project that tracked the social-media contacts, demographics and location of about 1,000 students, with five-minute resolution, over five months1. She knew then that her field was about to change. “I realized that these new kinds of data will revolutionize social science forever,” she says. “And I thought that it’s really cool.”
With that, Sivak decided to learn how to program, and join the revolution. Now, she and other computational social scientists are exploring massive and unruly data sets, extracting meaning from society’s digital imprint. They are tracking people’s online activities; exploring digitized books and historical documents; interpreting data from wearable sensors that record a person’s every step and contact; conducting online surveys and experiments that collect millions of data points; and probing databases that are so large that they will yield secrets about society only with the help of sophisticated data analysis.
Over the past decade, researchers have used such techniques to pick apart topics that social scientists have chased for more than a century: from the psychological underpinnings of human morality, to the influence of misinformation, to the factors that make some artists more successful than others. One study uncovered widespread racism in algorithms that inform health-care decisions2; another used mobile-phone data to map impoverished regions in Rwanda3.
“The biggest achievement is a shift in thinking about digital behavioural data as an interesting and useful source”, says Markus Strohmaier, a computational social scientist at the GESIS Leibniz Institute for the Social Sciences in Cologne, Germany.
Not everyone has embraced that shift. Some social scientists are concerned that the computer scientists flooding into the field with ambitions as big as their data sets are not sufficiently familiar with previous research. Another complaint is that some computational researchers look only at patterns and do not consider the causes, or that they draw weighty conclusions from incomplete and messy data — often gained from social-media platforms and other sources that are lacking in data hygiene.
The barbs fly both ways. Some computational social scientists who hail from fields such as physics and engineering argue that many social-science theories are too nebulous or poorly defined to be tested.
This all amounts to “a power struggle within the social-science camp”, says Marc Keuschnigg, an analytical sociologist at Linköping University in Norrköping, Sweden. “Who in the end succeeds will claim the label of the social sciences.”
But the two camps are starting to merge. “The intersection of computational social science with traditional social science is growing,” says Keuschnigg, pointing to the boom in shared journals, conferences and study programmes. “The mutual respect is growing, also.”…(More)”.
Discussion Paper by Fabio Ricciato, Albrecht Wirthmann and Martina Hahn: “In this discussion paper, we outline the motivations and the main principles of the Trusted Smart Statistics (TSS) concept that is under development in the European Statistical System. TSS represents the evolution of official statistics in response to the challenges posed by the new datafied society. Taking stock from the availability of new digital data sources, new technologies, and new behaviors, statistical offices are called nowadays to rethink the way they operate in order to reassert their role in modern democratic society. The issue at stake is considerably broader and deeper than merely adapting existing processes to embrace so-called Big Data. In several aspects, such evolution entails a fundamental paradigm shift with respect to the legacy model of official statistics production based on traditional data sources, for example, in the relation between data and computation, between data collection and analysis, between methodological development and statistical production, and of course in the roles of the various stakeholders and their mutual relationships. Such complex evolution must be guided by a comprehensive system-level view based on clearly spelled design principles. In this paper, we aim at providing a general account of the TSS concept reflecting the current state of the discussion within the European Statistical System….(More)”
Paper by Laetitia Gauvin, Michele Tizzoni, Simone Piaggesi, Andrew Young, Natalia Adler, Stefaan Verhulst, Leo Ferres & Ciro Cattuto in Humanities and Social Sciences Communications: “Mobile phone data have been extensively used to study urban mobility. However, studies based on gender-disaggregated large-scale data are still lacking, limiting our understanding of gendered aspects of urban mobility and our ability to design policies for gender equality. Here we study urban mobility from a gendered perspective, combining commercial and open datasets for the city of Santiago, Chile.
We analyze call detail records for a large cohort of anonymized mobile phone users and reveal a gender gap in mobility: women visit fewer unique locations than men, and distribute their time less equally among such locations. Mapping this mobility gap over administrative divisions, we observe that a wider gap is associated with lower income and lack of public and private transportation options. Our results uncover a complex interplay between gendered mobility patterns, socio-economic factors and urban affordances, calling for further research and providing insights for policymakers and urban planners….(More)”.
Blog by Sally Kerr: “The COVID emergency has brought many challenges that were unimaginable a few months ago. The first priorities were safety and health, but when lockdown started one of the early issues was accessing and sharing local data to help everyone deal with and live through the emergency. Communities grappled with the scarcity of local data, finding it difficult to source for some services, food deliveries and goods. This was not a new issue, but the pandemic brought it into sharp relief.
Local data use covers a broad spectrum. People moving to a new area want information about the environment — schools, amenities, transport, crime rates and local health. For residents, continuing knowledge of business opening hours, events, local issues, council plans and roadworks remains important, not only for everyday living but to help understand issues and future plans that will change their environment. Really local data (hyperlocal data) is either fragmented or unavailable, making it difficult for local people to stay informed, whilst larger data sets about an area (e.g. population, school performance) are not always easy to understand or use. They sit in silos owned by different sectors, on disparate websites, usually collated for professional or research use.
Third sector organisations in a community will gather data relevant to their work such as contacts and event numbers but may not source wider data sets about the area, such as demographics, to improve their work. Using this data could strengthen future grant applications by validating their work. For Government or Health bodies carrying out place making community projects, there is a reliance on their own or national data sources supplemented with qualitative data snapshots. Their dependence on tried and tested sources is due to time and resource pressures but means there is no time to gather that rich seam of local data that profiles individual needs.
Imagine a future community where local data is collected and managed together for both official organisations and the community itself. Where there are shared aims and varied use. Current and relevant data would be accessible and easy to understand, provided in formats that suit the user — from data scientist to school child. A curated data hub would help citizens learn data skills and carry out collaborative projects on anything from air quality to local biodiversity, managing the data and offering increased insight and useful validation for wider decision making. Costs would be reduced with duplication and effort reduced….(More)”.
Article by Lawsuit.org: “In the 2002 dystopian sci-fi film “Minority Report,” law enforcement can manage crime by “predicting” illegal behavior before it happens. While fiction, the plot is intriguing and contributes to the conversation on advanced crime-fighting technology. However, today’s world may not be far off.
Data’s role in our lives and more accessibility to artificial intelligence is changing the way we approach topics such as research, real estate, and law enforcement. In fact, recent investigative reporting has shown that “dozens of [American] cities” are now experimenting with predictive policing technology.
Despite the current controversy surrounding predictive policing, it seems to be a growing trend that has been met with little real resistance. We may be closer to policing that mirrors the frightening depictions in “Minority Report” than we ever thought possible.
Fighting Fire With Fire
In its current state, predictive policing is defined as:
“The usage of mathematical, predictive analytics, and other analytical techniques in law enforcement to identify potential criminal activity. Predictive policing methods fall into four general categories: methods for predicting crimes, methods for predicting offenders, methods for predicting perpetrators’ identities, and methods for predicting victims of crime.”
While it might not be possible to prevent predictive policing from being employed by the criminal justice system, perhaps there are ways we can create a more level playing field: One where the powers of big data analysis aren’t just used to predict crime, but also are used to police law enforcement themselves.
Below, we’ve provided a detailed breakdown of what this potential reality could look like when applied to one South Florida county’s public databases, along with information on how citizens and communities can use public data to better understand the behaviors of local law enforcement and even individual police officers….(More)”.
Article by By Sara J. Singer, Stephen Downs, Grace Ann Joseph, Neha Chaudhary, Christopher Gardner, Nina Hersher, Kelsey P. Mellard, Norma Padrón & Yennie Solheim: “….Aligning the technology sector with a societal goal of greater health and well-being entails a number of shifts in thinking. The most fundamental is understanding health not as a vertical market segment, but as a horizontal value: In addition to developing a line of health products or services, health should be expressed across a company’s full portfolio of products and services. Rather than pushing behaviors on people through information and feedback, technology companies should also pull behaviors from people by changing the environment and products they are offered; in addition to developing technology to help people overcome the challenge of being healthy, we need to envision technology that helps to reduce the challenges to being healthy. And in addition to holding individuals responsible for choices that they make, we also need to recognize the collective responsibility that society bears for the choices it makes available.
How to catalyze these shifts?
To find out, we convened a “tech-enabled health,” in which 50 entrepreneurs, leaders from large technology companies, investors, policymakers, clinicians, and public health experts designed a hands-on, interactive, and substantively focused agenda. Participants brainstormed ways that consumer-facing technologies could help people move more, eat better, sleep well, stay socially connected, and reduce stress. In groups and collectively, participants also considered ways in which ideas related and might be synergistic, potential barriers and contextual conditions that might impede or support transformation, and strategies for catalyzing the desired shift. Participants were mixed in terms of sector, discipline, and gender (though the attendees were not as diverse in terms of race/ethnicity or economic strata as the users we potentially wanted to impact—a limitation noted by participants). We intentionally maintained a positive tone, emphasizing potential benefits of shifting toward a health-positive approach, rather than bemoaning the negative role that technology can play….(More)”.
Jack Dunn at IAPP: “…It is revealing that our relationship with privacy is amorphous and requires additional context in light of transformative technologies, new economic realities and public health emergencies. How can we reasonably evaluate the costs and benefits of Google or Facebook sharing location data with the federal government when it has been perfectly legal for Walgreen’s to share access to customer data with pharmaceutical advertisers? How does aggregating and anonymizing data safeguard privacy when a user’s personal data can be revealed through other data points?
The pandemic is only revealing that we’ve yet to reach a consensus on privacy norms that will come to define the digital age.
This isn’t the first time that technology confounded notions of privacy and consumer protection. In fact, the constitutional right to privacy was born out of another public health crisis. Before 1965, 32 women per 100,000 live births died while giving birth. Similarly, 25 infants died per 100,000 live births. As a result, medical professionals and women’s rights advocates began arguing for greater access to birth control. When state legislatures sought to minimize access, birth control advocates filed lawsuits that eventually lead to the Supreme Court’s seminal case regarding the right to privacy, Griswold v. Connecticut.…
Today, there is growing public concern over the way in which consumer data is used to consolidate economic gain among the few while steering public perception among the many — particularly at a time when privacy seems to be the price for ending public health emergencies.
But the COVID-19 outbreak is also highlighting how user data has the capacity to improve consumer well being and public health. While strict adherence to traditional notions of privacy may be ineffectual in a time of exponential technological growth, the history of our relationship to privacy and technology suggests regulatory policies can strike a balance between otherwise competing interests….(More)“.
Philipp Grüll at Euractiv: “When Germany takes over the European Council Presidency on 1 July, Berlin will have plenty to do. The draft programme seen by EURACTIV Germany focuses on the major challenges of our time: climate change, digitisation, and the coronavirus.
Berlin wants to establish ‘European Digital Diplomacy’ by creating a ‘Digital Diplomacy Network’ to exist alongside the ‘Technospheres USA and China’.
This should not only be about keeping European industries competitive. After all, the term “digital diplomacy” is not new.
Ilan Manor, a researcher at Oxford University and author of numerous papers on digital diplomacy, defines it as “the use of digital tools to achieve foreign policy goals.”
This definition is intentionally broad, Manor told EURACTIV Germany, because technology can be used in so many areas of international relations….
Manor divides the development of this digital public diplomacy into two phases.
In the first one, from 2008 to 2015, governments took the first cautious steps. They experimented and launched random and often directionless online activities. Foreign ministries and embassies set up social media accounts. Sweden opened a virtual embassy in the online video game “Second Life.”
It was only in the second phase, from 2015 to the present, that foreign ministries began to act more strategically. They used “Big Data” to record public opinion in other countries, and also to track down online propaganda against their own country.
As an example, Manor cites the Russian embassy in the United Kingdom, which is said to have deliberately disseminated anti-EU narratives prior to the Brexit referendum, packaged in funny and seemingly innocent Internet memes that spread rapidly….(More)”.
Frank Swain at the BBC: “For almost half a century, Benedictine monks in Herefordshire dutifully logged the readings of a rain gauge on the grounds of Belmont Abbey, recording the quantity of rain that had fallen each month without fail. That is, until 1948, when measurements were suspended while the abbot waited for someone to repair a bullet hole in the gauge funnel.
How the bullet hole came to be there is still a mystery, but it’s just one of the stories uncovered by a team of 16,000 volunteers who have been taking part in Rainfall Rescue, a project to digitise hand-written records of British weather. The documents, held by the Met Office, contain 3.5 million datapoints and stretch as far back as 1820.
Ed Hawkins, a climate scientist at the University of Reading, leads the project. “It launched at the end of March, we realised people would have a lot of spare time on their hands,” he explains. “It was completed in 16 days. I was expecting 16 weeks, not 16 days… the volunteers absolutely blitzed it.” He says the data will be used to improve future weather predictions and climate modelling.
With millions of people trapped at home during the pandemic, citizen science projects are seeing a boom in engagement. Rainfall Rescue uses a platform called Zooniverse, which hosts dozens of projects covering everything from artworks to zebra. While the projects generally have scientific aims, many allow people to also contribute some good to the world.
Volunteers can scour satellite images for rural houses across Africa so they can be connected to the electricity grid, for example. Another – led by researchers at the University of Nottingham in the UK – is hunting for signs of modern slavery in the shape of brick kilns in South Asia (although the project has faced some criticism for being an over-simplified way of looking at modern slavery).
Others are trying to track the spread of invasive species in the ocean from underwater photographs, or identify earthquakes and tremors by speeding up the seismic signals so they become audible and can be classified by sharp-eared volunteers. “You could type in data on old documents, count penguins, go to the Serengeti and look at track camera images – it’s an incredible array,” says Hawkins. “Whatever you’re interested in there’s something for you.”…(More)”.
Paper by Hayden Dahmm: “In the midst of the COVID-19 pandemic, data has never been more salient. COVID has generated new data demands and increased cross-sector data collaboration. Yet, these data collaborations require careful planning and evaluation of risks and opportunities, especially when sharing sensitive data. Data sharing agreements (DSAs) are written agreements that establish the terms for how data are shared between parties and are important for establishing accountability and trust.
However, negotiating DSAs is often time consuming, and collaborators lacking legal or financial capacity are disadvantaged. Contracts for Data Collaboration (C4DC) is a joint initiative between SDSN TReNDS, NYU’s GovLab, the World Economic Forum, and the University of Washington, working to strengthen trust and transparency of data collaboratives. The partners have created an online library of DSAs which represents a selection of data applications and contexts.
This report introduces C4DC and its DSA library. We demonstrate how the library can support the data community to strengthen future data collaborations by showcasing various DSA applications and key considerations. First, we explain our method of analyzing the agreements and consider how six major issues are addressed by different agreements in the library. Key issues discussed include data use, access, breaches, proprietary issues, publicization of the analysis, and deletion of data upon termination of the agreement. For each of these issues, we describe approaches illustrated with examples from the library. While our analysis suggests some pertinent issues are regularly not addressed in DSAs, we have identified common areas of practice that may be helpful for entities negotiating partnership agreements to consider in the future….(More)”.