Casualties of a Pandemic: Truth, Trust and Transparency


Essay by Frank D. LoMonte at the Journal of Civic Information: “In an April 1 interview with NPR’s “Morning Edition,” retired U.S. Army Gen. Stanley A. McChrystal, former commander of U.S. forces in Iraq, explained that, in a crisis situation, accurate information from government authorities can be crucial in reassuring the public – and in the absence of accurate information, speculation and rumor will proliferate. Joni Mitchell, who’s probably never before appeared in the same paragraph with Stanley McChrystal, might have put it a touch more poetically: “Don’t it always seem to go; That you don’t know what you’ve got ’til it’s gone.”

The outbreak of the coronavirus strain COVID-19, which prompted the U.S. Department of Health and Human Services to declare a public health emergency on Jan. 31, 2020,3 is introducing Americans to a newfound world of austerity and loss. Professional haircuts, sit-down restaurant meals and recreational plane flights increasingly seem like memories from a bygone golden age (small inconveniences, to be sure, alongside the suffering of thousands who’ve died and the families they’ve left behind).

Access to information from government agencies, too, is adapting to a mail-order, drive-through society. As public-health authorities reached consensus that the spread of COVID-19 could be contained only by eliminating non-essential travel and group gatherings, strict adherence to open-meeting and public-records laws became a casualty alongside salad bars and theme-park rides. Governors and legislatures relaxed, or entirely waived, compliance with statutes that require agencies to open their meetings to in-person public attendance and promptly fulfill requests for documents.

As with all other areas of public life, some sacrifices in open-government formalities are unavoidable. With agencies down to a sustenance-level crew of essential workers, it’s unrealistic to expect that decades-old paper documents will be speedily located and produced. And it’s unsafe to invite people to congregate at public hearings to address their elected officials. But the public shouldn’t be alone in the sacrifice….(More)”.

How Facebook and Google are helping the CDC forecast coronavirus


Karen Hao at MIT Technology Review: “When it comes to predicting the spread of an infectious disease, it’s crucial to understand what Ryan Tibshirani, an associate professor at Carnegie Mellon University, calls the “the pyramid of severity.” The bottom of the pyramid is asymptomatic carriers (those who have the infection but feel fine); the next level is symptomatic carriers (those who are feeling ill); then come hospitalizations, critical hospitalizations, and finally deaths.

Every level of the pyramid has a clear relationship to the next: “For example, sadly, it’s pretty predictable how many people will die once you know how many people are under critical care,” says Tibshirani, who is part of CMU’s Delphi research group, one of the best flu-forecasting teams in the US. The goal, therefore, is to have a clear measure of the lower levels of the pyramid, as the foundation for forecasting the higher ones.

But in the US, building such a model is a Herculean task. A lack of testing makes it impossible to assess the number of asymptomatic carriers. The results also don’t accurately reflect how many symptomatic carriers there are. Different counties have different testing requirements—some choosing only to test patients who require hospitalization. Test results also often take upwards of a week to return.

The remaining option is to measure symptomatic carriers through a large-scale, self-reported survey. But such an initiative won’t work unless it covers a big enough cross section of the entire population. Now the Delphi group, which has been working with the Centers for Disease Control and Prevention to help it coordinate the national pandemic response, has turned to the largest platforms in the US: Facebook and Google.

Facebook will help CMU Delphi research group gather data about Covid symptoms

In a new partnership with Delphi, both tech giants have agreed to help gather data from those who voluntarily choose to report whether they’re experiencing covid-like symptoms. Facebook will target a fraction of their US users with a CMU-run survey, while Google has thus far been using its Opinion Rewards app, which lets users respond to questions for app store credit. The hope is this new information will allow the lab to produce county-by-county projections that will help policymakers allocate resources more effectively.

Neither company will ever actually see the survey results; they’re merely pointing users to the questions administered and processed by the lab. The lab will also never share any of the raw data back to either company. Still, the agreements represent a major deviation from typical data-sharing practices, which could raise privacy concerns. “If this wasn’t a pandemic, I don’t know that companies would want to take the risk of being associated with or asking directly for such a personal piece of information as health,” Tibshirani says.

Without such cooperation, the researchers would’ve been hard pressed to find the data anywhere else. Several other apps allow users to self-report symptoms, including a popular one in the UK known as the Covid Symptom Tracker that has been downloaded over 1.5 million times. But none of them offer the same systematic and expansive coverage as a Facebook or Google-administered survey, says Tibshirani. He hopes the project will collect millions of responses each week….(More)”.

We Have the Power to Destroy Ourselves Without the Wisdom to Ensure That We Don’t


EdgeCast by Toby Ord: “Lately, I’ve been asking myself questions about the future of humanity, not just about the next five years or even the next hundred years, but about everything humanity might be able to achieve in the time to come.

The past of humanity is about 200,000 years. That’s how long Homo sapiens have been around according to our current best guess (it might be a little bit longer). Maybe we should even include some of our other hominid ancestors and think about humanity somewhat more broadly. If we play our cards right, we could live hundreds of thousands of years more. In fact, there’s not much stopping us living millions of years. The typical species lives about a million years. Our 200,000 years so far would put us about in our adolescence, just old enough to be getting ourselves in trouble, but not wise enough to have thought through how we should act.

But a million years isn’t an upper bound for how long we could live. The horseshoe crab, for example, has lived for 450 million years so far. The Earth should remain habitable for at least that long. So, if we can survive as long as the horseshoe crab, we could have a future stretching millions of centuries from now. That’s millions of centuries of human progress, human achievement, and human flourishing. And if we could learn over that time how to reach out a little bit further into the cosmos to get to the planets around other stars, then we could have longer yet. If we went seven light-years at a time just making jumps of that distance, we could reach almost every star in the galaxy by continually spreading out from the new location. There are already plans in progress to send spacecraft these types of distances. If we could do that, the whole galaxy would open up to us….

Humanity is not a typical species. One of the things that most worries me is the way in which our technology might put us at risk. If we look back at the history of humanity these 2000 centuries, we see this initially gradual accumulation of knowledge and power. If you think back to the earliest humans, they weren’t that remarkable compared to the other species around them. An individual human is not that remarkable on the Savanna compared to a cheetah, or lion, or gazelle, but what set us apart was our ability to work together, to cooperate with other humans to form something greater than ourselves. It was teamwork, the ability to work together with those of us in the same tribe that let us expand to dozens of humans working together in cooperation. But much more important than that was our ability to cooperate across time, across the generations. By making small innovations and passing them on to our children, we were able to set a chain in motion wherein generations of people worked across time, slowly building up these innovations and technologies and accumulating power….(More)”.

Tracking coronavirus: big data and the challenge to privacy


Nic Fildes and Javier Espinoza at the Financial Times: “When the World Health Organization launched a 2007 initiative to eliminate malaria on Zanzibar, it turned to an unusual source to track the spread of the disease between the island and mainland Africa: mobile phones sold by Tanzania’s telecoms groups including Vodafone, the UK mobile operator.

Working together with researchers at Southampton university, Vodafone began compiling sets of location data from mobile phones in the areas where cases of the disease had been recorded. 

Mapping how populations move between locations has proved invaluable in tracking and responding to epidemics. The Zanzibar project has been replicated by academics across the continent to monitor other deadly diseases, including Ebola in west Africa….

With much of Europe at a standstill as a result of the coronavirus pandemic, politicians want the telecoms operators to provide similar data from smartphones. Thierry Breton, the former chief executive of France Telecom who is now the European commissioner for the internal market, has called on operators to hand over aggregated location data to track how the virus is spreading and to identify spots where help is most needed.

Both politicians and the industry insist that the data sets will be “anonymised”, meaning that customers’ individual identities will be scrubbed out. Mr Breton told the Financial Times: “In no way are we going to track individuals. That’s absolutely not the case. We are talking about fully anonymised, aggregated data to anticipate the development of the pandemic.”

But the use of such data to track the virus has triggered fears of growing surveillance, including questions about how the data might be used once the crisis is over and whether such data sets are ever truly anonymous….(More)”.

From insight network to open policy practice: practical experiences


Paper by Jouni T. Tuomisto, Mikko V. Pohjola & Teemu J. Rintala: “Evidence-informed decision-making and better use of scientific information in societal decisions has been an area of development for decades but is still topical. Decision support work can be viewed from the perspective of information collection, synthesis and flow between decision-makers, experts and stakeholders. Open policy practice is a coherent set of methods for such work. It has been developed and utilised mostly in Finnish and European contexts.

The evaluation revealed that methods and online tools work as expected, as demonstrated by the assessments and policy support processes conducted. The approach improves the availability of information and especially of relevant details. Experts are ambivalent about the acceptability of openness – it is an important scientific principle, but it goes against many current research and decision-making practices. However, co-creation and openness are megatrends that are changing science, decision-making and the society at large. Against many experts’ fears, open participation has not caused problems in performing high-quality assessments. On the contrary, a key challenge is to motivate and help more experts, decision-makers and citizens to participate and share their views. Many methods within open policy practice have also been widely used in other contexts.

Open policy practice proved to be a useful and coherent set of methods. It guided policy processes toward a more collaborative approach, whose purpose was wider understanding rather than winning a debate. There is potential for merging open policy practice with other open science and open decision process tools. Active facilitation, community building and improving the user-friendliness of the tools were identified as key solutions for improving the usability of the method in the future….(More)”.

The significance of algorithmic selection for everyday life: The Case of Switzerland


University of Zurich: “This project empirically investigates the significance of automated algorithmic selection (AS) applications on the Internet for everyday life in Switzerland. It is the first countrywide, representative empirical study in the emerging interdisciplinary field of critical algorithm studies which assesses growing social, economic and political implications of algorithms in various life domains. The project is based on an innovative mix of methods comprising qualitative interviews and a representative Swiss online survey, combined with a passive metering (tracking) of Internet use.

  • Latzer, Michael / Festic, Noemi / Kappeler, Kiran (2020): Use and Assigned Relevance of Algorithmic-Selection Applications in Switzerland. Report 1 from the Project: The Significance of Algorithmic Selection for Everyday Life: The Case of Switzerland. Zurich: University of Zurich. http://mediachange.ch/research/algosig [forthcoming]
  • Latzer, Michael / Festic, Noemi / Kappeler, Kiran (2020): Awareness of Algorithmic Selection and Attitudes in Switzerland. Report 2 from the Project: The Significance of Algorithmic Selection for Everyday Life: The Case of Switzerland. Zurich: University of Zurich. http://mediachange.ch/research/algosig [forthcoming]
  • Latzer, Michael / Festic, Noemi / Kappeler, Kiran (2020): Awareness of Risks Related to Algorithmic Selection in Switzerland. Report 3 from the Project: The Significance of Algorithmic Selection for Everyday Life: The Case of Switzerland. Zurich: University of Zurich. http://mediachange.ch/research/algosig [forthcoming]
  • Latzer, Michael / Festic, Noemi / Kappeler, Kiran (2020): Coping Practices Related to Algorithmic Selection in Switzerland. Report 4 from the Project: The Significance of Algorithmic Selection for Everyday Life: The Case of Switzerland. Zurich: University of Zurich. http://mediachange.ch/research/algosig [forthcoming]…(More)”.

Online collective intelligence course aims to improve responses to COVID-19 and other crises


PressRelease: “Working with 11 partner institutions around the world,  The Governance Lab (The GovLab) at the New York University Tandon School of Engineering today launches a massive open online course (MOOC) on “Collective Crisis Intelligence.” The course is free, open to anyone, and designed to help institutions improve disaster response through the use of data and volunteer participation. 

Thirteen modules have been created by leading global experts in major disasters such as the post-election violence in Kenya in 2008, the Fukushima nuclear plant disaster in 2011, the Ebola crisis in 2014, the Zika outbreak in 2016, and the current coronavirus. The course is designed to help those responding to coronavirus make use of volunteerism. 

As the COVID-19 pandemic reaches unprecedented proportions and spreads to more than 150 countries on six continents, policymakers are struggling to answer questions such as “How do we predict how the virus will spread?,” “How do we help the elderly and the homebound?,” “How do we provide economic assistance to those affected by business closures?,” and more. 

In each mini-lecture, those who have learned how to mobilize groups of people online to manage in a crisis present the basic concepts and tools to learn, analyze, and implement a crowdsourced public response. Lectures include

  • Introduction: Why Collective Intelligence Matters in a Crisis
  • Defining Actionable Problems (led by Matt Andrews, Harvard Kennedy School)
  • Three Day Evidence Review (led by Peter Bragge, Monash University, Australia)
  • Priorities for Collective Intelligence (led by Geoff Mulgan, University College London
  • Smarter Crowdsourcing (led by Beth Simone Noveck, The GovLab)
  • Crowdfunding (led by Peter Baeck, Nesta, United Kingdom)
  • Secondary Fall Out (led by Azby Brown, Safecast, Japan)
  • Crowdsourcing Surveillance (led by Tolbert Nyenswah, Johns Hopkins Bloomberg School of Public Health, United States/Liberia)
  • Crowdsourcing Data (led by Angela Oduor Lungati and Juliana Rotich, Ushahidi, Kenya)
  • Mobilizing a Network (led by Sean Bonner, Safecast, Japan)
  • Crowdsourcing Scientific Expertise (led by Ali Nouri, Federation of American Scientists)
  • Chatbots and Social Media Strategies for Crisis (led by Nashin Mahtani, PetaBencana.id, Indonesia)
  • Conclusion: Lessons Learned

The course explores such innovative uses of crowdsourcing as Safecast’s implementation of citizen science to gather information about environmental conditions after the meltdown of the Fukushima nuclear plant; Ushahidi, an online platform in Kenya for crowdsourcing data for crisis relief, human rights advocacy, transparency, and accountability campaigns; and “Ask a Scientist,” an interactive tool developed by The GovLab with the Federation of American Scientists and the New Jersey Office of Innovation, in which a network of scientists answer citizens’ questions about COVID-19.

More information on the courses is available at https://covidcourse.thegovlab.org

The Responsible Data for Children (RD4C) Case Studies


Andrew Young at Datastewards.net: “This week, as part of the Responsible Data for Children initiative (RD4C), the GovLab and UNICEF launched a new case study series to provide insights on promising practice as well as barriers to realizing responsible data for children.

Drawing upon field-based research and established good practice, RD4C aims to highlight and support responsible handling of data for and about children; identify challenges and develop practical tools to assist practitioners in evaluating and addressing them; and encourage a broader discussion on actionable principles, insights, and approaches for responsible data management.

RD4C launched in October 2019 with the release of the RD4C Synthesis ReportSelected Readings, and the RD4C Principles: Purpose-Driven, People-Centric, Participatory, Protective of Children’s Rights, Proportional, Professionally Accountable, and Prevention of Harms Across the Data Lifecycle.

The RD4C Case Studies analyze data systems deployed in diverse country environments, with a focus on their alignment with the RD4C Principles. This week’s release includes case studies arising from field missions to Romania, Kenya, and Afghanistan in 2019. The data systems examined are:

A guide to healthy skepticism of artificial intelligence and coronavirus


Alex Engler at Brookings: “The COVID-19 outbreak has spurred considerable news coverage about the ways artificial intelligence (AI) can combat the pandemic’s spread. Unfortunately, much of it has failed to be appropriately skeptical about the claims of AI’s value. Like many tools, AI has a role to play, but its effect on the outbreak is probably small. While this may change in the future, technologies like data reporting, telemedicine, and conventional diagnostic tools are currently far more impactful than AI.

Still, various news articles have dramatized the role AI is playing in the pandemic by overstating what tasks it can perform, inflating its effectiveness and scale, neglecting the level of human involvement, and being careless in consideration of related risks. In fact, the COVID-19 AI-hype has been diverse enough to cover the greatest hits of exaggerated claims around AI. And so, framed around examples from the COVID-19 outbreak, here are eight considerations for a skeptic’s approach to AI claims….(More)”.

The 9/11 Playbook for Protecting Privacy


Adam Klein and Edward Felten at Politico: “Geolocation data—precise GPS coordinates or records of proximity to other devices, often collected by smartphone apps—is emerging as a critical tool for tracking potential spread. But other, more novel types of surveillance are already being contemplated for this first pandemic of the digital age. Body temperature readings from internet-connected thermometers are already being used at scale, but there are more exotic possibilities. Could smart-home devices be used to identify coughs of a timbre associated with Covid-19? Can facial recognition and remote temperature sensing be harnessed to identify likely carriers at a distance?

Weigh the benefits of each collection and use of data against the risks.

Each scenario will present a different level of privacy sensitivity, different collection mechanisms, different technical options affecting privacy, and varying potential value to health professionals, meaning there is no substitute for case-by-case judgment about whether the benefits of a particular use of data outweighs the risks.

The various ways to use location data, for example, present vastly different levels of concern for privacy. Aggregated location data, which combines many individualized location trails to show broader trends, is possible with few privacy risks, using methods that ensure no individual’s location trail is reconstructable from released data. For that reason, governments should not seek individualized location trails for any application where aggregated data would suffice—for example, analyzing travel trends to predict future epidemic hotspots.

If authorities need to trace the movements of identifiable people, their location trails should be obtained on the basis of an individualized showing. Gathering from companies the location trails for all users—as the Israeli government does, according to news reports—would raise far greater privacy concerns.

Establish clear rules for how data can be used, retained, and shared.

Once data is collected, the focus shifts to what the government can do with it. In counterterrorism programs, detailed rules seek to reduce the effect on individual privacy by limiting how different types of data can be used, stored, and shared.

The most basic safeguard is deleting data when it is no longer needed. Keeping data longer than needed unnecessarily exposes it to data breaches, leaks, and other potential privacy harms. Any individualized location tracking should cease, and the data should be deleted, once the individual no longer presents a danger to public health.

Poland’s new tracking app for those exposed to the coronavirus illustrates why reasonable limits are essential. The Polish government plans to retain location data collected by the app for six years. It is hard to see a public-health justification for keeping the data that long. But the story also illustrates well how a failure to consider users’ privacy can undermine a program’s efficacy: the app’s onerous terms led at least one Polish citizen to refuse to download it….(More)”.