Stefaan Verhulst
Richard Gibson at the Hedgehog Review: “American society is prone, political theorist Langdon Winner wrote in 2005, to “technological euphoria,” each bout of which is inevitably followed by a period of letdown and reassessment. Perhaps in part for this reason, reviewing the history of digital democracy feels like watching the same movie over and over again. Even Winner’s point has that quality: He first made it in the mid-eighties and has repeated it in every decade since. In the same vein, Warren Yoder, longtime director of the Public Policy Center of Mississippi, responded to the Pew survey by arguing that we have reached the inevitable “low point” with digital technology—as “has happened many times in the past with pamphleteers, muckraking newspapers, radio, deregulated television.” (“Things will get better,” Yoder cheekily adds, “just in time for a new generational crisis beginning soon after 2030.”)
So one threat the present techlash poses is to obscure the ways that digital technology in fact serves many of the functions the visionaries imagined. We now take for granted the vast array of “Gov Tech”—meaning internal government digital upgrades—that makes our democracy go. We have become accustomed to the numerous government services that citizens can avail themselves of with a few clicks, a process spearheaded by the Clinton-Gore administration. We forget how revolutionary the “Internet campaign” of Howard Dean was at the 2004 Democratic primaries, establishing the Internet-based model of campaigning that all presidential candidates use to coordinate volunteer efforts and conduct fundraising, in both cases pulling new participants into the democratic process.
An honest assessment of the current state of digital democracy would acknowledge that the good jostles with the bad and the ugly. Social media has become the new hotspot for Rheingold’s “disinformocracy.” The president’s toxic tweeting continues, though Twitter has attempted recently to provide more oversight. At the same time, digital media have played a conspicuous role in the protests following George Floyd’s death, from the phone used to record his murder to the apps and Google docs used by the organizers of protests. The protests, too, have sparked fresh debate about facial recognition software (rightly one of the major concerns in the Pew report), leading Amazon to announce in June that it was “pausing” police use of its facial recognition software for one year. The city of Boston has made a similar move. Senator Sherrod Brown’s Data Accountability and Transparency Act of 2020, now circulating in draft form, would also limit the federal government’s use of “facial surveillance technology.”
We thus need to avoid summary judgments at this still-early date in the ongoing history of digital democracy. In a superb research paper on “The Internet and Engaged Citizenship” commissioned by the American Academy of Arts and Sciences last year, the political scientist David Karpf wisely concludes that the incredible velocity of “Internet Time” befuddles our attempts to state flatly what has or hasn’t happened to democratic practices and participation in our times. The 2016 election has rightly put many observers on guard. Yet there is a danger in living headline-by-headline. We must not forget how volatile the tech scene remains. That fact leads to Karpf’s hopeful conclusion: “The Internet of 2019 is not a finished product. The choices made by technologists, investors, policy-makers, lawyers, and engaged citizens will all shape what the medium becomes next.” The same can be said about digital technology in 2020: The landscape is still evolving….(More)“.
Nicole Wetsman at The Verge: “From the early days of the COVID-19 pandemic, epidemiologist Melissa Haendel knew that the United States was going to have a data problem. There didn’t seem to be a national strategy to control the virus, and cases were springing up in sporadic hotspots around the country. With such a patchwork response, nationwide information about the people who got sick would probably be hard to come by.
Other researchers around the country were pinpointing similar problems. In Seattle, Adam Wilcox, the chief analytics officer at UW Medicine, was reaching out to colleagues. The city was the first US COVID-19 hotspot. “We had 10 times the data, in terms of just raw testing, than other areas,” he says. He wanted to share that data with other hospitals, so they would have that information on hand before COVID-19 cases started to climb in their area. Everyone wanted to get as much data as possible in the hands of as many people as possible, so they could start to understand the virus.
Haendel was in a good position to help make that happen. She’s the chair of the National Center for Data to Health (CD2H), a National Institutes of Health program that works to improve collaboration and data sharing within the medical research community. So one week in March, just after she’d started working from home and pulled her 10th grader out of school, she started trying to figure out how to use existing data-sharing projects to help fight this new disease.
The solution Haendel and CD2H landed on sounds simple: a centralized, anonymous database of health records from people who tested positive for COVID-19. Researchers could use the data to figure out why some people get very sick and others don’t, how conditions like cancer and asthma interact with the disease, and which treatments end up being effective.
But in the United States, building that type of resource isn’t easy. “The US healthcare system is very fragmented,” Haendel says. “And because we have no centralized healthcare, that makes it also the case that we have no centralized healthcare data.” Hospitals, citing privacy concerns, don’t like to give out their patients’ health data. Even if hospitals agree to share, they all use different ways of storing information. At one institution, the classification “female” could go into a record as one, and “male” could go in as two — and at the next, they’d be reversed….(More)”.
Report by Anat Gofen and Esti Golan: “To address both persistent and emerging social and environmental problems, governments around the world have been seeking innovative ways to generate policy solutions in collaboration with citizens. One prominent trend during recent decades is the proliferation of Policy Innovation Labs (PILs), in which the search for policy solutions is embedded within scientific laboratory-like structures. Spread across the public, private, and non-profit sectors, and often funded by local, regional, or national governments, PILs utilize experimental methods, testing, and measurement to generate innovative, evidence-based policy solutions to complex public issues.
This catalog lists PILs in Europe. For each lab, a one-page profile specifies its vision, policy innovation approaches, methodologies, major projects, parent entity, funding sources, and its alignment with the United Nations Sustainable Development Goals (SDG) call to action. For each lab we identify governmental, municipal, multi-sectorial, academic, non-profit, or private sector affiliation.
The goals of compiling this catalog and making it available to citizens, scholars, NGOs, and public officials are to call attention to the growing spirit of citizen engagement in developing innovative policy solutions for their own communities and to facilitate collaboration and cross-pollination of ideas between organizations. Despite their increasing importance in public policy making, PILs are as yet understudied. This catalog will provide an opportunity for scholars to explore the function and value of community-oriented policy innovation as well as the effects of approaching policy making around disruptive social problems in a “scientific” way.
Methodology: This catalog of policy innovation labs was compiled based on published reports, as well as a Google search for each individual country using the terms “policy lab” and “innovation lab,” first in English, then in the native language. Sometimes the labs themselves came up in the search results; for others, an article or a blog that mentioned them appeared. Next, each lab was searched specifically by name or by using an identified link. Each lab website that was identified was searched for other labs that were mentioned. Some labs were identified more than once, and a few that were found to be defunct or lacking a website were excluded. Innovation labs that referred only to technical or technological innovations were omitted. Only labs that relate to policy and to so-called “public innovation” were included in this catalog. Eligible PILs could be run and/or sponsored by local, regional, or national governments, universities, non-profit organizations, or the private sector. This resulted in a total of 212 European PILs.
Notably, while the global proliferation of policy innovation labs is acknowledged by formal, global organizations, there are no clear-cut criteria to determine which organizations are considered PILs. Therefore, this catalog follows the precedent set by previous catalogs and identifies PILs as organizations that generate policy recommendations for social problems and public issues by employing a user-oriented design approach and utilizing experimental methods.
Information about every lab was collected form its website, with minimal editing for coherence. For some labs, information was presented in English on its website; for others, information in the native language was translated into English using machine translation followed by human editing. Data for the catalog was collected between December 2019 and July 2020. PILs are opening and closing with increasing frequency so this catalog serves as a snapshot in time, featuring PILs that are currently active as of the time of compilation….(More)”.
Chapter by Geoff Boeing in Book edited by Justin B. Hollander and Ann Sussman: “This chapter introduces OpenStreetMap—a crowd-sourced, worldwide mapping project and geospatial data repository—to illustrate its usefulness in quickly and easily analyzing and visualizing planning and design outcomes in the built environment. It demonstrates the OSMnx toolkit for automatically downloading, modeling, analyzing, and visualizing spatial big data from OpenStreetMap. We explore patterns and configurations in street networks and buildings around the world computationally through visualization methods—including figure-ground diagrams and polar histograms—that help compress urban complexity into comprehensible artifacts that reflect the human experience of the built environment. Ubiquitous urban data and computation can open up new urban form analyses from both quantitative and qualitative perspectives….(More)”.
Book by Mike Hodson, Julia Kasmire, Andrew McMeekin, John G. Stehlin, and Kevin Ward: “This title takes the broadest possible scope to interrogate the emergence of “platform urbanism”, examining how it transforms urban infrastructure, governance, knowledge production, and everyday life, and brings together leading scholars and early-career researchers from across five continents and multiple disciplines.
The volume advances theoretical debates at the leading edge of the intersection between urbanism, governance, and the digital economy, by drawing on a range of empirically detailed cases from which to theorize the multiplicity of forms that platform urbanism takes. It draws international comparisons between urban platforms across sites, with attention to the leading edges of theory and practice and explores the potential for a renewal of civic life, engagement, and participatory governance through “platform cooperativism” and related movements. A breadth of tangible and diverse examples of platform urbanism provides critical insights to scholars examining the interface of digital technologies and urban infrastructure, urban governance, urban knowledge production, and everyday urban life.
The book will be invaluable on a range of undergraduate and postgraduate courses, as well as for academics and researchers in these fields, including anthropology, geography, innovation studies, politics, public policy, science and technology studies, sociology, sustainable development, urban planning, and urban studies. It will also appeal to an engaged, academia-adjacent readership, including city and regional planners, policymakers, and third-sector researchers in the realms of citizen engagement, industrial strategy, regeneration, sustainable development, and transport….(More)”.
Rebecca Root at Devex: “A lack of data on groundwater is impeding water management and could jeopardize climate resilience efforts in some places, according to recent research by WaterAid and the HSBC Water Programme.
Groundwater is found underground in gaps between soil, sand, and rock. Over 2.5 million people are thought to depend on groundwater — which has a higher tolerance to droughts than other water sources — for drinking.
The report looked at groundwater security and sustainability in Bangladesh, Ghana, India, Nepal, and Nigeria, where collectively more than 160 million people lack access to clean water close to home. It found that groundwater data tends to be limited — including on issues such as overextraction, pollution, and contamination — leaving little evidence for decision-makers to consider for its management.
“There’s a general lack of information and data … which makes it very hard to manage the resource sustainably,” said Vincent Casey, senior water, sanitation, and hygiene manager for waste at WaterAid…(More)”.
MIT CISR research:”…has found that interorganizational data sharing is a top concern of companies; leaders often find data sharing costly, slow, and risky. Interorganizational data sharing, however, is requisite for new value creation in the digital economy. Digital opportunities require data sharing 2.0: cross-company sharing of complementary data assets and capabilities, which fills data gaps and allows companies, often collaboratively, to develop innovative solutions. This briefing introduces three sets of practices—curated content, designated channels, and repeatable controls—that help companies accelerate data sharing 2.0….(More)”.
Paper by Andreas Backhaus: “…In the public debate, one can encounter at least three concepts that measure the deadliness of SARS-CoV-2: the case fatality rate (CFR), the infection fatality rate (IFR) and the mortality rate (MR). Unfortunately, these three concepts are sometimes used interchangeably, which creates confusion as they differ from each other by definition.
In its simplest form, the case fatality rate divides the total number of confirmed deaths by COVID-19 by the total number of confirmed cases of infections with SARS-CoV-2, neglecting adjustments for future deaths among current cases here. However, the number of confirmed cases is believed to severely underestimate the true number of infections. This is due to the asymptomatic process of the infection in many individuals and the lack of testing capacities. Hence, the CFR presumably reflects rather an upper bound to the true lethality of SARS-CoV-2, as its denominator does not take the undetected infections into account.
The infection fatality rate seeks to represent the lethality more accurately by incorporating the number of undetected infections or at least an estimate thereof into its calculation. Consequently, the IFR divides the total number of confirmed deaths by COVID-19 by the total number of infections with SARS-CoV-2. Due to its larger denominator but identical numerator, the IFR is lower than the CFR. The IFR represents a crucial parameter in epidemiological simulation models, such as that presented by Ferguson et al. (2020), as it determines the number of expected fatalities given the simulated spread of the disease among the population.
The methodological challenge regarding the IFR is, of course, to find a credible estimate of the undetected cases of infection. An early estimate of the IFR was provided on the basis of data collected in the course of the SARS-CoV-2 outbreak on the Diamond Princess cruise ship in February 2020. Mizumoto et al. (2020) estimate that 17.9% (95% confidence interval: 15.5-20.2) of the cases were asymptomatic. Russell et al. (2020), after adjusting for age, estimate that the IFR among the Diamond Princess cases is 1.3% (95% confidence interval: 0.38-3.6) when considering all cases, but 6.4% (95% confidence interval: 2.6–13) when considering only cases of patients that are 70 years and older. The serological studies that are currently being conducted in several countries and localities serve to provide more estimates of the true number of infections with SARS-CoV-2 that have occurred over the past few months….(More)”.
Paper by Marcella Alsan, Luca Braghieri, Sarah Eichmeyer, Minjeong Joyce Kim, Stefanie Stantcheva, and David Y. Yang: “The respect for and protection of civil liberties are one of the fundamental roles of the state, and many consider civil liberties as sacred and “nontradable.” Using cross-country representative surveys that cover 15 countries and over 370,000 respondents, we study whether and the extent to which citizens are willing to trade off civil liberties during the COVID-19 pandemic, one of the largest crises in recent history. We find four main results. First, many around the world reveal a clear willingness to trade off civil liberties for improved public health conditions. Second, consistent across countries, exposure to health risks is associated with citizens’ greater willingness to trade off civil liberties, though individuals who are more economically disadvantaged are less willing to do so. Third, attitudes concerning such trade-offs are elastic to information. Fourth, we document a gradual decline and then plateau in citizens’ overall willingness to sacrifice rights and freedom as the pandemic progresses, though the underlying correlation between individuals’ worry about health and their attitudes over the trade-offs has been remarkably constant. Our results suggest that citizens do not view civil liberties as sacred values; rather, they are willing to trade off civil liberties more or less readily, at least in the short-run, depending on their own circumstances and information….(More)”.
Oren Etzioni and Michael Li in Wired: “…To achieve increased transparency, we advocate for auditable AI, an AI system that is queried externally with hypothetical cases. Those hypothetical cases can be either synthetic or real—allowing automated, instantaneous, fine-grained interrogation of the model. It’s a straightforward way to monitor AI systems for signs of bias or brittleness: What happens if we change the gender of a defendant? What happens if the loan applicants reside in a historically minority neighborhood?
Auditable AI has several advantages over explainable AI. Having a neutral third-party investigate these questions is a far better check on bias than explanations controlled by the algorithm’s creator. Second, this means the producers of the software do not have to expose trade secrets of their proprietary systems and data sets. Thus, AI audits will likely face less resistance.
Auditing is complementary to explanations. In fact, auditing can help to investigate and validate (or invalidate) AI explanations. Say Netflix recommends The Twilight Zone because I watched Stranger Things. Will it also recommend other science fiction horror shows? Does it recommend The Twilight Zone to everyone who’s watched Stranger Things?
Early examples of auditable AI are already having a positive impact. The ACLU recently revealed that Amazon’s auditable facial-recognition algorithms were nearly twice as likely to misidentify people of color. There is growing evidence that public audits can improve model accuracy for under-represented groups.
In the future, we can envision a robust ecosystem of auditing systems that provide insights into AI. We can even imagine “AI guardians” that build external models of AI systems based on audits. Instead of requiring AI systems to provide low-fidelity explanations, regulators can insist that AI systems used for high-stakes decisions provide auditing interfaces.
Auditable AI is not a panacea. If an AI system is performing a cancer diagnostic, the patient will still want an accurate and understandable explanation, not just an audit. Such explanations are the subject of ongoing research and will hopefully be ready for commercial use in the near future. But in the meantime, auditable AI can increase transparency and combat bias….(More)”.