Stefaan Verhulst

Karen Hao at MIT Technology Review: “The pandemic, in other words, has turned into a gateway for AI adoption in health care—bringing both opportunity and risk. On the one hand, it is pushing doctors and hospitals to fast-track promising new technologies. On the other, this accelerated process could allow unvetted tools to bypass regulatory processes, putting patients in harm’s way.

“At a high level, artificial intelligence in health care is very exciting,” says Chris Longhurst, the chief information officer at UC San Diego Health. “But health care is one of those industries where there are a lot of factors that come into play. A change in the system can have potentially fatal unintended consequences.”

Before the pandemic, health-care AI was already a booming area of research. Deep learning, in particular, has demonstrated impressive results for analyzing medical images to identify diseases like breast and lung cancer or glaucoma at least as accurately as human specialists. Studies have also shown the potential of using computer vision to monitor elderly people in their homes and patients in intensive care units.

But there have been significant obstacles to translating that research into real-world applications. Privacy concerns make it challenging to collect enough data for training algorithms; issues related to bias and generalizability make regulators cautious to grant approvals. Even for applications that do get certified, hospitals rightly have their own intensive vetting procedures and established protocols. “Physicians, like everybody else—we’re all creatures of habit,” says Albert Hsiao, a radiologist at UCSD Health who is now trialing his own covid detection algorithm based on chest x-rays. “We don’t change unless we’re forced to change.”

As a result, AI has been slow to gain a foothold. “It feels like there’s something there; there are a lot of papers that show a lot of promise,” said Andrew Ng, a leading AI practitioner, in a recent webinar on its applications in medicine. But “it’s not yet as widely deployed as we wish.”…

In addition to the speed of evaluation, Durand identifies something else that may have encouraged hospitals to adopt AI during the pandemic: they are thinking about how to prepare for the inevitable staff shortages that will arise after the crisis. Traumatic events like a pandemic are often followed by an exodus of doctors and nurses. “Some doctors may want to change their way of life,” he says. “What’s coming, we don’t know.”…(More)”

Andy Haldane at the Financial Times: “Yet one source of capital, as in past pandemics, is bucking these trends: social capital. This typically refers to the network of relationships across communities that support and strengthen societies. From surveys, we know that people greatly value these networks, even though social capital itself is rarely assigned a monetary value.

The social distancing policies enacted across the world to curb the spread of Covid-19 might have been expected to weaken social networks and damage social capital. In fact, the opposite has happened. People have maintained physical distance while pursuing social togetherness. Existing networks have been strengthened and new ones ­created, often digitally. Even as other capital has crumbled, the stock of social capital has risen, acting as a counter­cyclical stabiliser across communities. We see this daily on our doorsteps through small acts of neighbourly kindness.

We see it in the activities of community groups, charities and philanthropic movements, whose work has risen in importance and prominence. And we see it too in the vastly increased numbers of people volunteering to help. Before the crisis struck, the global volunteer corps numbered a staggering 1bn people. Since then, more people than ever have signed up for civic service, including 750,000 volunteers who are supporting the UK National Health Service. They are the often-invisible army helping fight this invisible enemy.

This same pattern appeared during past periods of societal stress, from pandemics to wars. Then, as now, faith and community groups provided the glue bonding societies together. During the 19th century, the societal stresses arising from the Industrial Revolution — homelessness, family separation, loneliness — were the catalyst for the emergence of the charitable sector.

The economic and social progress that followed the Industrial Revolution came courtesy of a three-way partnership among the private, public and social sectors. The private sector provided the innovative spark; the state provided insurance to the incomes, jobs and health of citizens; and the social sector provided the support network to cope with disruption to lives and livelihoods. Back then, social capital (every bit as much as human, financial and physical capital) provided the foundations on which capitalism was built….(More)”.

James Temple at MIT Technology Review: “…A crucial point of the work—which Steinhardt and MIT’s Andrew Ilyas​ wrote up in a draft paper that hasn’t yet been published or peer-reviewed—is that communities need to get much better at tracking infections. “With the data we currently have, we actually just don’t know what the level of safe mobility is,” Steinhardt says. “We need much better mechanisms for tracking prevalence in order to do any of this safely.”

The analysis relies on other noisy and less-than-optimal measurements as well, including using hospitalization admissions and deaths to estimate disease prevalence before the lockdowns. They also had to make informed assumptions, which others might disagree with, about how much shelter-in-place rules have altered the spread of the disease. Much of the overall uncertainty is due to the spottiness of testing to date. If case counts are rising, but so is testing, it’s difficult to decipher whether infections are still increasing or a greater proportion of infected people are being evaluated.

This produces some confusing results in the study for any policymaker looking for clear direction. Notably, in Los Angeles, the estimated growth rate of the disease since the shelter-in-place order went into effect ranges from negative to positive. This suggests either that the city could start loosening restrictions or that it needs to tighten them further.

Ultimately, the researchers stress that communities need to build up disease surveillance measures to reduce this uncertainty, and strike an appropriate balance between reopening the economy and minimizing public health risks.

They propose several ways to do so, including conducting virological testing on a random sample of some 20,000 people per day in a given area; setting up wide-scale online surveys that ask people to report potential symptoms, similar to what Carnegie Mellon researchers are doing through efforts with both Facebook and Google; and potentially testing for the prevalence of viral material in wastewater, a technique that has “sounded the alarm” on polio outbreaks in the past.

A team of researchers affiliated with MIT, Harvard, and startup Biobot Analytics recently analyzed water samples from a Massachusetts treatment facility, and detected levels of the coronavirus that were “significantly higher” than expected on the basis of confirmed cases in the state, according to a non-peer-reviewed paper released earlier this month….(More)”.

Marcus Fairs at DeZeen: “Hospitals “desperately need designers” to improve everything from the way they tackle coronavirus to the layout of operating theatres and the design of medical charts, according to a senior US doctor.

“We desperately need designers to help organize the environment and products to help keep the correct focus on a patient, and reduce distraction,” said Dr Sam Smith, a clinical physician at Massachusetts General Hospital in Boston.

“We need designers at every turn, but they are so infrequently consulted,” he added. “In the end, most physicians burn out early because, in part, we are lacking well designed cognitive and physical spaces to help process the information smoothly.”…

“Visual hierarchy is a huge problem in medicine,” Smith said, giving an example. “This is very evident in online medical charts. Very poor visual hierarchy exists because designers were not consulted in the platform or details of the patient information organization or presentation.”

“This inability to incorporate good visual hierarchy, for example organizing a complex medical history in a visual way to emphasize what really needs attention for the patient, has led to ineffective care, and even patient harm on occasions over the years,” he explained.

“I have seen it in my 20 years of practice time and time again. Doctors are humans too, and the demands on them processing huge amounts of information are high.”…(More)”.

Marc Santolini at the Conversation: “All around the world, scientists and practitioners are relentlessly harnessing data on the pandemic to model its progression, predict the impact of possible interventions and develop solutions to medical equipment shortages, generating open-source data and codes to be reused by others.

Research and innovation is now in a collaborative frenzy just as contagious as the coronavirus. Is this the rise of the famous “collective intelligence” supposed to solve our major global problems?

The rise of a global collective intelligence

The beginning of the epidemic saw “traditional” research considerably accelerate and open its means of production, with journals such as Science, Nature and The Lancet immediately granting public access to publications on the coronavirus and Covid-19.

The academic world is in ebullition. Every day, John Hopkins University updates an open and collaborative stream of data on the epidemic, which have already been reused more than 11,000 times. Research results are published immediately on pre-print servers or laboratory websites. Algorithms and interactive visualizations are flourishing on GitHub; outreach videos on YouTube. The figures are staggering, with nearly 9,000 academic articles published on the subject to date.

More recently, popular initiatives bringing together a variety of actors have emerged outside institutional frameworks, using online platforms. For example, a community of biologists, engineers and developers has emerged on the Just One Giant Lab (JOGL) collaborative platform to develop low-cost, open-source solutions against the virus. This platform, which we developed with Leo Blondel (Harvard University) and Thomas Landrain (La Paillasse, PILI) over the past three years, is designed as a virtual, open and distributed research institute aimed at developing solutions to the Sustainable Development Goals (SDGs) defined by the United Nations. Communities use it to self-organize and provide innovative solutions to urgent problems requiring fundamentally interdisciplinary skills and knowledge. The platform facilitates coordination by linking needs and resources within the community, animating research programs, and organising challenges….(More)”.

Aakash Desai et al in Nature Medicine: “Crowdsourcing efforts are currently underway to collect and analyze data from patients with cancer who are affected by the COVID-19 pandemic. These community-led initiatives will fill key knowledge gaps to tackle crucial clinical questions on the complexities of infection with the causative coronavirus SARS-Cov-2 in the large, heterogeneous group of vulnerable patients with cancer…(More)”

Stefaan G. Verhulst at Open Data Policy Lab: “The belief that we are living in a data age — one characterized by unprecedented amounts of data, with unprecedented potential — has become mainstream. We regularly read phrases such as “data is the most valuable commodity in the global economy” or that data provides decision-makers with an “ever-swelling flood of information.”

Without a doubt, there is truth in such statements. But they also leave out a major shortcoming — the fact that much of the most useful data continue to remain inaccessible, hidden in silos, behind digital walls, and in untapped “treasuries.”

For close to a decade, the technology and public interest community have pushed the idea of open data. At its core, open data represents a new paradigm of data availability and access. The movement borrows from the language of open source and is rooted in notions of a “knowledge commons”, a concept developed, among others, by scholars like Nobel Prize winner Elinor Ostrom.

Milestones and Limitations in Open Data

Significant milestones have been achieved in the short history of the open data movement. Around the world, an ever-increasing number of governments at the local, state and national levels now release large datasets for the public’s benefit. For example, New York City requires that all public data be published on a single web portal. The current portal site contains thousands of datasets that fuel projects on topics as diverse as school bullying, sanitation, and police conduct. In California, the Forest Practice Watershed Mapper allows users to track the impact of timber harvesting on aquatic life through the use of the state’s open data. Similarly, Denmark’s Building and Dwelling Register releases address data to the public free of charge, improving transparent property assessment for all interested parties.

A growing number of private companies have also initiated or engaged in “Data Collaborative”projects to leverage their private data toward the public interest. For example, Valassis, a direct-mail marketing company, shared its massive address database with community groups in New Orleans to visualize and track block-by-block repopulation rates after Hurricane Katrina. A wide number of data collaboratives are also currently being launched to respond to the COVID-19 pandemic. Through its COVID-19 Data Collaborative Program, the location-intelligence company Cuebiq is providing researchers access to the company’s data to study, for instance, the impacts of social distancing policies in Italy and New York City. The health technology company Kinsa Health’s US Health Weather initiative is likewise visualizing the rate of fever across the United States using data from its network of Smart Thermometers, thereby providing early indications regarding the location of likely COVID-19 outbreaks.

Yet despite such initiatives, many open data projects (and data collaboratives) remain fledgling — especially those at the state and local level.

Among other issues, the field has trouble scaling projects beyond initial pilots, and many potential stakeholders — private sector and government “owners” of data, as well as public beneficiaries — remain skeptical of open data’s value. In addition, terabytes of potentially transformative data remain inaccessible for re-use. It is absolutely imperative that we continue to make the case to all stakeholders regarding the importance of open data, and of moving it from an interesting idea to an impactful reality. In order to do this, we need a new resource — one that can inform the public and data owners, and that would guide decision-makers on how to achieve open data in a responsible manner, without undermining privacy and other rights.

Purpose of the Open Data Policy Lab

Today, with support from Microsoft and under the counsel of a global advisory board of open data leaders, The GovLab is launching an initiative designed precisely to build such a resource.

Our Open Data Policy Lab will draw on lessons and experiences from around the world to conduct analysis, provide guidance, build community, and take action to accelerate the responsible re-use and opening of data for the benefit of society and the equitable spread of economic opportunity…(More)”.

Article by Alan Marcus: “…The best responses to Covid-19 have harmonised top-down policies and grassroots organisation. In the UK, more than 700,000 volunteers for the National Health Service are being organised through GoodSAM—an app that, like many gig economy platforms, allows individuals to switch on availability for delivering supplies to vulnerable people.

Perhaps the best example is Taiwan, where officials have kept the rate of infection to a fraction of even highly-rated Singapore. Coordinating public and private groups, the country has deployed a range of online services, including a system for mapping and allocating rationed face masks developed by Digital Minister Audrey Tang and members of an online hacktivist chatroom. …

Effective responses to the crisis show the value of inclusive government and hint at more resilient models for managing our communities. So far, governments, businesses and individuals have pooled resources to deliver country-wide responses. However, this model should be pushed further. Digital tools should be provided to communities to organise themselves, develop locally tailored solutions and get involved in the governance of their town or neighbourhood.

This model requires open communication between local people and the organisations responsible for administrating neighbourhoods—be they governments or businesses. … 

The platform provides significant opportunities for optimising crisis response and elevating quality of life. For example, a popular solution for market vendors forced to close by Covid-19 has been offering delivery services. As well as the businesses, this benefits local people, who can bypass overcrowded superstores or overcapacity online grocery deliveries. While grassroots movements are largely left to organise themselves, this is a missed opportunity for collaboration with local administrators.

By communicating with vendors, the administrator can not only establish an online platform to coordinate their services, but also connect them with local people to help deliver the service, such as van owners who can loan their vehicles. Moreover, the administrator can collect feedback on local infrastructure needed to improve services, such as communal cold lockers for receiving groceries when no-one is home.

By integrating this model into the day-to-day governance of our communities, we can unite community action with top-down resources, empowering local people to co-own the evolution of their neighbourhoods and helping administrators prioritise projects that maximise quality of life.

As Solnit wrote: “A disaster is a lot like a revolution when it comes to disruption and improvisation.” Pushed to their limits, countries are pioneering ways of coordinating local and national action. From this wave of innovation, we can empower communities to become more resilient in crises, more inclusive in their governance and more engaged in the determination of their future….(More)”.

The GovLab: “In response to the COVID-19 pandemic, legislatures at the national, state and local level are adapting to keep the lawmaking process going while minimizing the need for face-to-face meetings. While some have simply lowered quorum thresholds or reduced the number of sessions while continuing to meet in person, others are trialing more ambitious remote participation systems where lawmakers convene, deliberate, and vote virtually. Still others have used shift as an opportunity to create mechanisms for greater civic engagement.

For a short overview of how legislatures in Brazil, Chile, France, and other countries are using technology to convene, deliberate and vote remotely, see the GovLab’s short video, Continuity of Congress.”

Paper by Sarah Giest & Annemarie Samuels: “Policy and data scientists have paid ample attention to the amount of data being collected and the challenge for policymakers to use and utilize it. However, far less attention has been paid towards the quality and coverage of this data specifically pertaining to minority groups. The paper makes the argument that while there is seemingly more data to draw on for policymakers, the quality of the data in combination with potential known or unknown data gaps limits government’s ability to create inclusive policies. In this context, the paper defines primary, secondary, and unknown data gaps that cover scenarios of knowingly or unknowingly missing data and how that is potentially compensated through alternative measures.

Based on the review of the literature from various fields and a variety of examples highlighted throughout the paper, we conclude that the big data movement combined with more sophisticated methods in recent years has opened up new opportunities for government to use existing data in different ways as well as fill data gaps through innovative techniques. Focusing specifically on the representativeness of such data, however, shows that data gaps affect the economic opportunities, social mobility, and democratic participation of marginalized groups. The big data movement in policy may thus create new forms of inequality that are harder to detect and whose impact is more difficult to predict….(More)“.