How Congress can improve productivity by looking to the rest of the world

Curated on Posted on by Stefaan Verhulst

Beth Noveck and Dane Gambrell at the Hill: “…While an important first step in helping to resume operations, Congress needs to follow the lead of those many legislatures around the world who have changed their laws and rules and are using technology to continue to legislate, conduct oversight and even innovate. 

Though efforts to restart by adopting proxy voting are a step in the right direction, they do not go far enough to create what Georgetown University’s Lorelei Kelly calls the “modern and safe digital infrastructure for the world’s most powerful national legislature.” 

Congress has all but shut down since March. While the Senate formally “re-opened” on May 4, the chamber is operating under restrictive new guidelines, with hearings largely closed to the public and lawmakers advised to bring only a skeleton crew to run their offices. Considering that the average age of a senator is 63 and the average age of a Member of the House is 58, this caution comes as no surprise.

Yet when we take into account that parliaments around the world from New Zealand to the Maldives are holding committee meetings, running plenary sessions, voting and even engaging the public in the lawmaking process online, we should be asking Congress to do more faster. 

Instead, bitter partisan wrangling — with Republicans accusing Democrats of taking advantage of social distancing to launch a power grab and Democrats accusing Republicans of failing to exercise oversight — is delaying the adoption of long available and easy to use technologies. More than a left-right issue, moving online is a top-down issue with leadership of both parties using the crisis to consolidate power.

Working online

The Parliament of the United Kingdom, for example, is one of dozens of legislatures turning to online video conferencing tools such as Zoom, Microsoft Teams, Cisco Web Meetings and Google Hangouts to do plenary or committee meetings. After 800 years, lawmakers in the House of Commons convened the first-ever “virtual Parliament” at the end of April. In this hybrid approach, some MPs were present in the legislative chamber while most joined remotely using Zoom…(More)”.

Considering the Source: Varieties of COVID-19 Information

Curated on Posted on by Stefaan Verhulst

Congressional Research Service: “In common parlance, the terms propaganda, misinformation, and disinformation are often used interchangeably, often with connotations of deliberate untruths of nefarious origin. In a national security context, however, these terms refer to categories of information that are created and disseminated with different intent and serve different strategic purposes. This primer examines these categories to create a framework for understanding the national security implications of information related to the Coronavirus Disease 2019 (COVID-19) pandemic….(More)”.

Our weird behavior during the pandemic is messing with AI models

Curated on Posted on by Stefaan Verhulst

Will Douglas Heaven at MIT Technology Review: “In the week of April 12-18, the top 10 search terms on Amazon.com were: toilet paper, face mask, hand sanitizer, paper towels, Lysol spray, Clorox wipes, mask, Lysol, masks for germ protection, and N95 mask. People weren’t just searching, they were buying too—and in bulk. The majority of people looking for masks ended up buying the new Amazon #1 Best Seller, “Face Mask, Pack of 50”.

When covid-19 hit, we started buying things we’d never bought before. The shift was sudden: the mainstays of Amazon’s top ten—phone cases, phone chargers, Lego—were knocked off the charts in just a few days. Nozzle, a London-based consultancy specializing in algorithmic advertising for Amazon sellers, captured the rapid change in this simple graph.

It took less than a week at the end of February for the top 10 Amazon search terms in multiple countries to fill up with products related to covid-19. You can track the spread of the pandemic by what we shopped for: the items peaked first in Italy, followed by Spain, France, Canada, and the US. The UK and Germany lag slightly behind. “It’s an incredible transition in the space of five days,” says Rael Cline, Nozzle’s CEO. The ripple effects have been seen across retail supply chains.

But they have also affected artificial intelligence, causing hiccups for the algorithms that run behind the scenes in inventory management, fraud detection, marketing, and more. Machine-learning models trained on normal human behavior are now finding that normal has changed, and some are no longer working as they should. 

How bad the situation is depends on whom you talk to. According to Pactera Edge, a global AI consultancy, “automation is in tailspin.” Others say they are keeping a cautious eye on automated systems that are just about holding up, stepping in with a manual correction when needed.

What’s clear is that the pandemic has revealed how intertwined our lives are with AI, exposing a delicate codependence in which changes to our behavior change how AI works, and changes to how AI works change our behavior. This is also a reminder that human involvement in automated systems remains key. “You can never sit and forget when you’re in such extraordinary circumstances,” says Cline….(More)”.

A call for a new generation of COVID-19 models

Curated on Posted on by Stefaan Verhulst

Blog post by Alex Engler: “Existing models have been valuable, but they were not designed to support these types of critical decisions. A new generation of models that estimate the risk of COVID-19 spread for precise geographies—at the county or even more localized level—would be much more informative for these questions. Rather than produce long-term predictions of deaths or hospital utilization, these models could estimate near-term relative risk to inform local policymaking. Going forward, governors and mayors need local, current, and actionable numbers.

Broadly speaking, better models would substantially aid in the “adaptive response” approach to re-opening the economy. In this strategy, policymakers cyclically loosen and re-tighten restrictions, attempting to work back towards a healthy economy without moving so fast as to allow infections to take off again. In an ideal process, restrictions would be eased at such a pace that balances a swift return to normalcy with reducing total COVID-19 infections. Of course, this is impossible in practice, and thus some continued adjustments—the flipping of various controls off and on again—will be necessary. More precise models can help improve this process, providing another lens into when it will be safe to relax restrictions, thus making it easier to do without a disruptive back-and-forth. A more-or-less continuous easing of restrictions is especially valuable, since it is unlikely that second or third rounds of interventions (such as social distancing) would achieve the same high rates of compliance as the first round.

The proliferation of Covid19 Data

These models can incorporate cases, test-positive rates, hospitalization information, deaths, excess deaths, and other known COVID-19 data. While all these data sources are incomplete, an expanding body of research on COVID-19 is making the data more interpretable. This research will become progressively more valuable with more data on the spread of COVID-19 in the U.S. rather than data from other countries or past pandemics.

Further, a broad range of non-COVID-19 data can also inform risk estimates: Population density, age distributions, poverty and uninsured rates, the number of essential frontline workers, and co-morbidity factors can also be included. Community mobility reports from Google and Unacast’s social distancing scorecard can identify how easing restrictions are changing behavior. Small area estimates also allow the models to account for the risk of spread from other nearby geographies. Geospatial statistics cannot account for infectious spread between two large neighboring states, but they would add value for adjacent zip codes. Lastly, many more data sources are in the works, like open patient data registries, the National Institutes of Health’s (NIH) study of asymptomatic persons, self-reported symptoms data from Facebook, and (potentially) new randomized surveys. In fact, there are so many diverse and relevant data streams, that models can add value simply be consolidating daily information into just a few top-line numbers that are comparable across the nation.

FiveThirtyEight has effectively explained that making these models is tremendously difficult due to incomplete data, especially since the U.S. is not testing enough or in statistically valuable ways. These challenges are real, but decision-makers are currently using this same highly flawed data to make inferences and policy choices. Despite the many known problems, elected officials and public health services have no choice. Frequently, they are evaluating the data without the time and expertise to make reasoned statistical interpretations based on epidemiological research, leaving significant opportunity for modeling to help….(More)”.

National Academies, National Science Foundation Create Network to Connect Decision-Makers with Social Scientists on Pressing COVID-19 Questions

Curated on Posted on by Stefaan Verhulst

Press Release: “The National Academies of Sciences, Engineering, and Medicine and the National Science Foundation announced today the formation of a Societal Experts Action Network (SEAN) to connect social and behavioral science researchers with decision-makers who are leading the response to COVID-19. SEAN will respond to the most pressing social, behavioral, and economic questions that are being asked by federal, state, and local officials by working with appropriate experts to quickly provide actionable answers.

The new network’s activities will be overseen by an executive committee in coordination with the National Academies’ Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats, established earlier this year to provide rapid expert input on urgent questions facing the federal government on the COVID-19 pandemic. Standing committee members Robert Groves, executive vice president and provost at Georgetown University, and Mary T. Bassett, director of the François-Xavier Bagnoud Center for Health and Human Rights at Harvard University, will co-chair the executive committee to manage SEAN’s solicitation of questions and expert responses, anticipate leaders’ research needs, and guide the dissemination of network findings.

SEAN will include individual researchers from a broad range of disciplines as well as leading national social and behavioral science institutions. Responses to decision-maker requests may range from individual phone calls and presentations to written committee documents such as Rapid Expert Consultations.

“This pandemic has broadly impacted all aspects of life — not just our health, but our work, families, education, supply chains, and even the global environment,” said Marcia McNutt, president of the National Academy of Sciences. “Therefore, to address the myriad questions that are being raised by mayors, governors, local representatives, and other leaders, we must recruit the full range of scientific expertise from across the social, natural, and biomedical sciences.”   

“Our communities and our society at large are facing a range of complex issues on multiple fronts due to COVID-19,” said Arthur Lupia, head of the Directorate for Social, Behavioral, and Economic Sciences at the National Science Foundation. “These are human-centered issues affecting our daily lives — the education and well-being of our children, the strength of our economy, the health of our loved ones, neighbors, and so many more. Through SEAN, social and behavioral scientists will provide actionable, evidence-driven guidance to our leaders across the U.S. who are working to support our communities and speed their recovery.”…(More)”.

A data sharing method in the open web environment: Data sharing in hydrology

Curated on Posted on by Stefaan Verhulst

Paper by Jin Wang et al: “Data sharing plays a fundamental role in providing data resources for geographic modeling and simulation. Although there are many successful cases of data sharing through the web, current practices for sharing data mostly focus on data publication using metadata at the file level, which requires identifying, restructuring and synthesizing raw data files for further usage. In hydrology, because the same hydrological information is often stored in data files with different formats, modelers should identify the required information from multisource data sets and then customize data requirements for their applications. However, these data customization tasks are difficult to repeat, which leads to repetitive labor. This paper presents a data sharing method that provides a solution for data manipulation based on a structured data description model rather than raw data files. With the structured data description model, multisource hydrological data can be accessed and processed in a unified way and published as data services using a designed data server. This study also proposes a data configuration manager to customize data requirements through an interactive programming tool, which can help in using the data services. In addition, a component-based data viewer is developed for the visualization of multisource data in a sharable visualization scheme. A case study that involves sharing and applying hydrological data is designed to examine the applicability and feasibility of the proposed data sharing method….(More)”.

Public Service and Good Governance for the Twenty-First Century

Curated on Posted on by Stefaan Verhulst

Book edited by James L. Perry: “Two big ideas serve as the catalyst for the essays collected in this book. The first is the state of governance in the United States, which Americans variously perceive as broken, frustrating, and unresponsive. Editor James Perry observes in his Introduction that this perception is rooted in three simultaneous developments: government’s failure to perform basic tasks that once were taken for granted, an accelerating pace of change that quickly makes past standards of performance antiquated, and a dearth of intellectual capital that generate the capacity to bridge the gulf between expectations and performance. The second idea hearkens back to the Progressive era, when Americans revealed themselves to be committed to better administration of their government at all levels—federal, state, and local.

These two ideas—the diminishing capacity for effective governance and Americans’ expectations for reform—are veering in opposite directions. Contributors to Public Service and Good Governance for the Twenty-First Century explore these central ideas by addressing such questions as: what is the state of government today? Can future disruptions of governance and public service be anticipated? What forms of government will emerge from the past and what institutions and structures will be needed to meet future challenges? And lastly, and perhaps most importantly, what knowledge, skills, and abilities will need to be fostered for tomorrow’s civil servants to lead and execute effectively?

Public Service and Good Governance for the Twenty-First Century offers recommendations for bending the trajectories of governance capacity and reform expectations toward convergence, including reversing the trend of administrative disinvestment, developing talent for public leadership through higher education, creating a federal civil service to meet future needs, and rebuilding bipartisanship so that the sweeping changes needed to restore good government become possible….(More)”

Data Sharing in the Context of Health-Related Citizen Science

Curated on Posted on by Stefaan Verhulst

Paper by Mary A. Majumder and Amy L. McGuire: “As citizen science expands, questions arise regarding the applicability of norms and policies created in the context of conventional science. This article focuses on data sharing in the conduct of health-related citizen science, asking whether citizen scientists have obligations to share data and publish findings on par with the obligations of professional scientists. We conclude that there are good reasons for supporting citizen scientists in sharing data and publishing findings, and we applaud recent efforts to facilitate data sharing. At the same time, we believe it is problematic to treat data sharing and publication as ethical requirements for citizen scientists, especially where there is the potential for burden and harm without compensating benefit…(More)”.

Protecting Data Privacy and Rights During a Crisis are Key to Helping the Most Vulnerable in Our Community

Curated on Posted on by Stefaan Verhulst

Blog by Amen Ra Mashariki: “Governments should protect the data and privacy rights of their communities even during emergencies. It is a false trade-off to require more data without protection. We can and should do both — collect the appropriate data and protect it. Establishing and protecting the data rights and privacy of our communities’ underserved, underrepresented, disabled, and vulnerable residents is the only way we can combat the negative impact of COVID-19 or any other crisis.

Building trust is critical. Governments can strengthen data privacy protocols, beef up transparency mechanisms, and protect the public’s data rights in the name of building trust — especially with the most vulnerable populations. Otherwise, residents will opt out of engaging with government, and without their information, leaders like first responders will be blind to their existence when making decisions and responding to emergencies, as we are seeing with COVID-19.

As Chief Analytics Officer of New York City, I often remembered the words of Defense Secretary Donald Rumsfeld, especially with regards to using data during emergencies, that there are “known knowns, known unknowns, and unknown unknowns, and we will always get hurt by the unknown unknowns.” Meaning the things we didn’t know — the data that we didn’t have — was always going to be what hurt us during times of emergencies….

There are three key steps that governments can do right now to use data most effectively to respond to emergencies — both for COVID-19 and in the future.

Seek Open Data First

In times of crisis and emergencies, many believe that government and private entities, either purposefully or inadvertently, are willing to trample on the data rights of the public in the name of appropriate crisis response. This should not be a trade-off. We can respond to crises while keeping data privacy and data rights in the forefront of our minds. Rather than dismissing data rights, governments can start using data that is already openly available. This seems like a simple step, but it does two very important things. First, it forces you to understand the data that is already available in your jurisdiction. Second, it grows your ability to fill the gaps with respect to what you know about the city by looking outside of city government. …(More)”.

How data science can ease the COVID-19 pandemic

Curated on Posted on by Stefaan Verhulst

Nigam Shah and Jacob Steinhardt at Brookings: “Social distancing and stay-at-home orders in the United States have slowed the infection rate of SARS-CoV-2, the pathogen that causes COVID-19. This has halted the immediate threat to the U.S. healthcare system, but consensus on a long-term plan or solution to the crisis remains unclear.  As the reality settles in that there are no quick fixes and that therapies and vaccines will take several months if not years to invent, validate, and mass produce, this is a good time to consider another question: How can data science and technology help us endure the pandemic while we develop therapies and vaccines?

Before policymakers reopen their economies, they must be sure that the resulting new COVID-19 cases will not force local healthcare systems to resort to crisis standards of care. Doing so requires not just prevention and suppression of the virus, but ongoing measurement of virus activity, assessment of the efficacy of suppression measures, and forecasting of near-term demand on local health systems. This demand is highly variable given community demographics, the prevalence of pre-existing conditions, and population density and socioeconomics.

Data science can already provide ongoing, accurate estimates of health system demand, which is a requirement in almost all reopening plans. We need to go beyond that to a dynamic approach of data collection, analysis, and forecasting to inform policy decisions in real time and iteratively optimize public health recommendations for re-opening. While most reopening plans propose extensive testing, contact tracing, and monitoring of population mobility, almost none consider setting up such a dynamic feedback loop. Having such feedback could determine what level of virus activity can be tolerated in an area, given regional health system capacity, and adjust population distancing accordingly.

We propose that by using existing technology and some nifty data science, it is possible to set up that feedback loop, which would maintain healthcare demand under the threshold of what is available in a region. Just as the maker community stepped up to cover for the failures of the government to provide adequate protective gear to health workers, this is an opportunity for the data and tech community to partner with healthcare experts and provide a measure of public health planning that governments are unable to do. Therefore, the question we invite the data science community to focus on is: How can data science help forecast regional health system resource needs given measurements of virus activity and suppression measures such as population distancing?…

Concretely, then, the crucial “data science” task is to learn the counterfactual function linking last week’s population mobility and today’s transmission rates to project hospital demand two weeks later. Imagine taking past measurements of mobility around April 10 in a region (such as the Santa Clara County’s report from COVID-19 Community Mobility Reports), the April 20 virus transmission rate estimate for the region (such as from http://rt.live), and the April 25 burden on the health system (such as from the Santa Clara County Hospitalization dashboard), to learn a function that uses today’s mobility and transmission rates to anticipate needed hospital resources two weeks later. It is unclear how many days of data of each proxy measurement we need to reliably learn such a function, what mathematical form this function might take, and how we do this correctly with the observational data on hand and avoid the trap of mere function-fitting. However, this is the data science problem that needs to be tackled as a priority. 

Adopting such technology and data science to keep anticipated healthcare needs under the threshold of availability in a region requires multiple privacy trade-offs, which will require thoughtful legislation so that the solutions invented for enduring the current pandemic do not lead to loss of privacy in perpetuity. However, given the immense economic as well as hidden medical toll of the shutdown, we urgently need to construct an early warning system that tells us to enhance suppression measures if the next COVID-19 outbreak peak might overwhelm our regional healthcare system. It is imperative that we focus our attention on using data science to anticipate, and manage, regional health system resource needs based on local measurements of virus activity and effects of population distancing….(More)”.