Democracy in a Pandemic: Participation in response to Covid

Curated on Posted on by Stefaan Verhulst

Open Access Book by Involve: “Covid-19 has highlighted limitations in our democratic politics – but also lessons for how to deepen our democracy and more effectively respond to future crises.

In the face of an emergency, the working assumption all too often is that only a centralised, top-down response is possible. This book exposes the weakness of this assumption, making the case for deeper participation and deliberation in times of crises. During the pandemic, mutual aid and self-help groups have realised unmet needs. And forward-thinking organisations have shown that listening to and working with diverse social groups leads to more inclusive outcomes.

Participation and deliberation are not just possible in an emergency. They are valuable, perhaps even indispensable. 

This book draws together a diverse range of voices of activists, practitioners, policy makers, researchers and writers. Together they make visible the critical role played by participation and deliberation during the pandemic and make the case for enhanced engagement during and beyond emergency contexts.

Another, more democratic world can be realised in the face of a crisis. The contributors to this book offer us meaningful insights into what this could look like….(More)”.

The Predictive Power of Patents

Curated on Posted on by Stefaan Verhulst

Paper by Sabrina Safrin: “This article explains that domestic patenting activity may foreshadow a country’s level of regulation of path-breaking technologies. The article considers whether different governments will act with a light or a heavy regulatory hand when encountering a new disruptive technology. The article hypothesizes that part of the answer to this important regulatory, economic, and geopolitical question may lie in an unexpected place: the world’s patent offices. Countries with early and significant patent activity in an emerging technology are more likely to view themselves as having a stake in the technology and therefore will be less inclined to subject the technology to extensive health, safety and environmental regulation that would constrain it. The article introduces the term “patent footprint” to describe a country’s degree of patenting activity in a new technology, and the article posits that a country’s patent footprint may provide an early clue to its willingness or reluctance to strenuously regulate the new technology. Even more so, lack of geographic diversity in patent footprints may help predict whether an emerging technology will face extensive international regulation. Patent footprints provide a useful tool to policymakers, businesses, investors, and NGOs considering the health, safety, and environmental regulation of a disruptive technology. The predictive power of patent footprints adds to the literature on the broader function of patents in society….(More)”.

On the forecastability of food insecurity

Curated on Posted on by Stefaan Verhulst

Paper by Pietro Foini,  Michele Tizzoni, Daniela Paolotti, and Elisa Omodei: “Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk.

Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, the proposed forecasting model makes it possible to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level…(More)”.

The State of Global Emotions

Curated on Posted on by Stefaan Verhulst

Gallup: “Nobody was alone in feeling more sad, angry, worried or stressed last year. Gallup’s latest Negative Experience Index, which annually tracks these experiences worldwide in more than 100 countries and areas, shows that collectively, the world was feeling the worst it had in 15 years. The index score reached a new high of 32 in 2020.

Line graph. The Negative Experience Index, an annual composite index of stress, anger, worry, sadness and physical pain, continued to rise in 2020, hitting a new record of 32.

Gallup asked adults in 115 countries and areas if they had five specific negative experiences on the day preceding the survey. Four in 10 adults said they had experienced worry (40%) or stress (40%), and just under three in 10 had experienced physical pain (29%) during a lot of the previous day. About one in four or more experienced sadness (27%) or anger (24%).

Already at or near record highs in 2019, experiences of worry, stress, sadness and anger continued to gain steam and set new records in 2020. Worry and sadness each rose one percentage point, anger rose two, and stress rocketed up five. The percentage of adults worldwide who experienced pain was the only index item that declined — dropping two points after holding steady for several years at 31%.

But 2020 officially became the most stressful year in recent history. The five-point jump from 35% in 2019 to 40% in 2020 represents nearly 190 million more people globally who experienced stress during a lot of the previous day.

Line graph. Reported stress worldwide soared to a record 40% in 2020 amid the COVID-19 pandemic.

Worldwide, not everyone was feeling this stress to the same degree. Reported stress ranged from a high of 66% in Peru — which represents a new high for the country — to a low of 13% in Kyrgyzstan, where stress levels have historically been low and stayed low in 2020….(More)”

What Is Behavioral Data Science and How to Get into It?

Curated on Posted on by Stefaan Verhulst

Blogpost by Ganna Pogrebna: “Behavioral Data Science is a new, emerging, interdisciplinary field, which combines techniques from the behavioral sciences, such as psychology, economics, sociology, and business, with computational approaches from computer science, statistics, data-centric engineering, information systems research and mathematics, all in order to better model, understand and predict behavior.

Behavioral Data Science lies at the interface of all these disciplines (and a growing list of others) — all interested in combining deep knowledge about the questions underlying human, algorithmic, and systems behavior with increasing quantities of data. The kinds of questions this field engages are not only exciting and challenging, but also timely, such as:

Behavioral Data Science is capable of addressing all these issues (and many more) partly because of the availability of new data sources and partly due to the emergence of new (hybrid) models, which merge behavioral science and data science models. The main advantage of these models is that they expand machine learning techniques, operating, essentially, as black boxes, to fully tractable, and explainable upgrades. Specifically, while a deep learning model can generate accurate prediction of why people select one product or brand over the other, it will not tell you what exactly drives people’s preferences; whereas hybrid models, such as anthropomorphic learning, will be able to provide this insight….(More)”

Political Science Has Its Own Lab Leaks

Curated on Posted on by Stefaan Verhulst

Paul Musgrave at Foreign Policy: “The idea of a lab leak has gone, well, viral. As a political scientist, I cannot assess whether the evidence shows that COVID-19 emerged naturally or from laboratory procedures (although many experts strenuously disagree). Yet as a political scientist, I do think that my discipline can learn something from thinking seriously about our own “lab leaks” and the damage they could cause.

A political science lab leak might seem as much of a punchline as the concept of a mad social scientist. Nevertheless, the notion that scholarly ideas and findings can escape the nuanced, cautious world of the academic seminar and transform into new forms, even becoming threats, becomes more of a compelling metaphor if you think of academics as professional crafters of ideas intended to survive in a hostile environment. Given the importance of what we study, from nuclear war to international economics to democratization and genocide, the escape of a faulty idea could have—and has had—dangerous consequences for the world.

Academic settings provide an evolutionarily challenging environment in which ideas adapt to survive. The process of developing and testing academic theories provides metaphorical gain-of-function accelerations of these dynamics. To survive peer review, an idea has to be extremely lucky or, more likely, crafted to evade the antibodies of academia (reviewers’ objections). By that point, an idea is either so clunky it cannot survive on its own—or it is optimized to thrive in a less hostile environment.

Think tanks and magazines like the Atlantic (or Foreign Policy) serve as metaphorical wet markets where wild ideas are introduced into new and vulnerable populations. Although some authors lament a putative decline of social science’s influence, the spread of formerly academic ideas like intersectionality and the use of quantitative social science to reshape electioneering suggest that ideas not only move from the academy but can flourish once transplanted. This is hardly new: Terms from disciplines including psychoanalysis (“ego”), evolution (“survival of the fittest”), and economics (the “free market” and Marxism both) have escaped from the confines of academic work before…(More)”.

How data governance technologies can democratize data sharing for community well-being

Curated on Posted on by Stefaan Verhulst

Paper by Dan Wu, Stefaan Verhulst, Alex Pentland, Thiago Avila, Kelsey Finch, and Abhishek Gupta in Data & Policy (Cambridge University Press) focusing on “Data sharing efforts to allow underserved groups and organizations to overcome the concentration of power in our data landscape…

A few special organizations, due to their data monopolies and resources, are able to decide which problems to solve and how to solve them. But even though data sharing creates a counterbalancing democratizing force, it must nevertheless be approached cautiously. Underserved organizations and groups must navigate difficult barriers related to technological complexity and legal risk.

To examine what those common barriers are, one type of data sharing effort—data trusts—are examined, specifically the reports commenting on that effort. To address these practical issues, data governance technologies have a large role to play in democratizing data trusts safely and in a trustworthy manner. Yet technology is far from a silver bullet. It is dangerous to rely upon it. But technology that is no-code, flexible, and secure can help more responsibly operate data trusts. This type of technology helps innovators put relationships at the center of their efforts….(More)”.

Charting the ‘Data for Good’ Landscape

Curated on Posted on by Stefaan Verhulst

Report by Jake Porway at Data.org: “There is huge potential for data science and AI to play a productive role in advancing social impact. However, the field of “data for good” is not only overshadowed by the public conversations about the risks rampant data misuse can pose to civil society, it is also a fractured and disconnected space. There are a myriad of different interpretations of what it means to “use data for good” or “use AI for good”, which creates duplicate efforts, nonstrategic initiatives, and confusion about what a successfully data-driven social sector could look like. To add to that, funding is scarce for a field that requires expensive tools and skills to do well. These enduring challenges result in work being done at an activity and project level, but do not create a coherent set of building blocks to constitute a strong and healthy field that is capable of solving a new class of systems-level problems.

We are taking one tiny step forward in trying to make a more coherent Data for Good space with a landscape that makes clear what various Data for Good initiatives (and AI for Good initiatives) are trying to achieve, how they do it, and what makes them similar or different from one another. One of the major confusion points in talking about “Data for Good” is that it treats all efforts as similar by the mere fact that they use “data” and seek to do something “good”. This term is so broad as to be practically meaningless; as unhelpful as saying “Wood for Good”. We would laugh at a term as vague as “Wood for Good”, which would lump together activities as different as building houses to burning wood in cook stoves to making paper, combining architecture with carpentry, forestry with fuel. However, we are content to say “Data for Good”, and its related phrases “we need to use our data better” or “we need to be data-driven”, when data is arguably even more general than something like wood.

We are trying to bring clarity to the conversation by going beyond mapping organizations into arbitrary groups, to define the dimensions of what it means to do data for good. By creating an ontology for what Data for Good initiatives seek to achieve, in which sector, and by what means, we can gain a better understanding of the underlying fundamentals of using data for good, as well as creating a landscape of what initiatives are doing.

We hope that this landscape of initiatives will help to bring some more nuance and clarity to the field, as well as identify which initiatives are out there and what purpose they serve. Specifically, we hope this landscape will help:

  • Data for Good field practitioners align on a shared language for the outcomes, activities, and aims of the field.
  • Purpose-driven organizations who are interested in applying data and computing to their missions better understand what they might need and who they might go to to get it.
  • Funders make more strategic decisions about funding in the data/AI space based on activities that align with their interests and the amount of funding already devoted to that area.
  • Organizations with Data for Good initiatives can find one another and collaborate based on similarity of mission and activities.

Below you will find a very preliminary landscape map, along with a description of the different kinds of groups in the Data for Good ecosystem and why you might need to engage with them….(More)”.

Understanding crowdsourcing projects: A review on the key design elements of a crowdsourcing initiative

Curated on Posted on by Stefaan Verhulst

Paper by Rea Karachiwalla and Felix Pinkow: “Crowdsourcing has gained considerable traction over the past decade and has emerged as a powerful tool in the innovation process of organizations. Given its growing significance in practice, a profound understanding of the concept is crucial. The goal of this study is to develop a comprehensive understanding of designing crowdsourcing projects for innovation by identifying and analyzing critical design elements of crowdsourcing contests. Through synthesizing the principles of the social exchange theory and absorptive capacity, this study provides a novel conceptual configuration that accounts for both the attraction of solvers and the ability of the crowdsourcer to capture value from crowdsourcing contests. Therefore, this paper adopts a morphological approach to structure the four dimensions, namely, (i) task, (ii) crowd, (iii) platform and (iv) crowdsourcer, into a conceptual framework to present an integrated overview of the various crowdsourcing design options. The morphological analysis allows the possibility of identifying relevant interdependencies between design elements, based on the goals of the problem to be crowdsourced. In doing so, the paper aims to enrich the extant literature by providing a comprehensive overview of crowdsourcing and to serve as a blueprint for practitioners to make more informed decisions when designing and executing crowdsourcing projects….(More)”.

Could Trade Agreements Help Address the Wicked Problem of Cross-Border Disinformation?

Curated on Posted on by Stefaan Verhulst

Essay by Susan Ariel Aaronson: “Whether produced domestically or internationally, disinformation is a “wicked” problem that has global impacts. Although trade agreements contain measures that address cross-border disinformation, domestically created disinformation remains out of their reach. This paper looks at how policy makers can use trade agreements to mitigate disinformation and spam while implementing financial and trade sanctions against entities and countries that engage in disseminating cross-border disinformation. Developed and developing countries will need to work together to solve this global problem….(More)”.