Individualism During Crises: Big Data Analytics of Collective Actions amid COVID-19

Curated on Posted on by Stefaan Verhulst

Paper by Bo Bian et al: “Collective actions, such as charitable crowdfunding and social distancing, are useful for alleviating the negative impact of the COVID-19 pandemic. However, engagements in these actions across the U.S. are “consistently inconsistent” and are frequently linked to individualism in the press. We present the first evidence on how individualism shapes online and offline collective actions during a crisis through big data analytics. Following economic historical studies, we leverage GIS techniques to construct a U.S. county-level individualism measure that traces the time each county spent on the American frontier between 1790 and 1890. We then use high-dimensional fixed-effect models, text mining, geo-distributed big data computing and a novel identification strategy based on migrations to analyze GoFundMe fundraising activities as well as county- and individual-level social distancing compliance.

Our analysis uncovers several insights. First, higher individualism reduces both online donations and social distancing during the COVID-19 pandemic. An interquartile increase in individualism reduces COVID-related charitable campaigns and funding by 48% and offsets the effect of state lockdown orders on social distancing by 41%. Second, government interventions, such as stimulus checks, can potentially mitigate the negative effect of individualism on charitable crowdfunding. Third, the individualism effect may be partly driven by a failure to internalize the externality of collective actions: we find stronger results in counties where social distancing generates higher externalities (those with higher population densities or more seniors). Our research is the first to uncover the potential downsides of individualism during crises. It also highlights the importance of big data-driven, culture-aware policymaking….(More)”.

Characterizing Disinformation Risk to Open Data in the Post-Truth Era

Curated on Posted on by Stefaan Verhulst

Paper by Adrienne Colborne and Michael Smit: “Curated, labeled, high-quality data is a valuable commodity for tasks such as business analytics and machine learning. Open data is a common source of such data—for example, retail analytics draws on open demographic data, and weather forecast systems draw on open atmospheric and ocean data. Open data is released openly by governments to achieve various objectives, such as transparency, informing citizen engagement, or supporting private enterprise.

Critical examination of ongoing social changes, including the post-truth phenomenon, suggests the quality, integrity, and authenticity of open data may be at risk. We introduce this risk through various lenses, describe some of the types of risk we expect using a threat model approach, identify approaches to mitigate each risk, and present real-world examples of cases where the risk has already caused harm. As an initial assessment of awareness of this disinformation risk, we compare our analysis to perspectives captured during open data stakeholder consultations in Canada…(More)”.

Race After Technology: Abolitionist Tools for the New Jim Code

Curated on Posted on by Stefaan Verhulst

Book by Ruha Benjamin: “From everyday apps to complex algorithms, Ruha Benjamin cuts through tech-industry hype to understand how emerging technologies can reinforce White supremacy and deepen social inequity.

Benjamin argues that automation, far from being a sinister story of racist programmers scheming on the dark web, has the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent when compared to the racism of a previous era. Presenting the concept of the “New Jim Code,” she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life.

This illuminating guide provides conceptual tools for decoding tech promises with sociologically informed skepticism. In doing so, it challenges us to question not only the technologies we are sold but also the ones we ourselves manufacture….(More)”.

The technology of witnessing brutality

Curated on Posted on by Stefaan Verhulst

Axios: “The ways Americans capture and share records of racist violence and police misconduct keep changing, but the pain of the underlying injustices they chronicle remains a stubborn constant.

Driving the news: After George Floyd’s death at the hands of Minneapolis police sparked wide protests, Minnesota Gov. Tim Walz said, “Thank God a young person had a camera to video it.”

Why it matters: From news photography to TV broadcasts to camcorders to smartphones, improvements in the technology of witness over the past century mean we’re more instantly and viscerally aware of each new injustice.

  • But unless our growing power to collect and distribute evidence of injustice can drive actual social change, the awareness these technologies provide just ends up fueling frustration and despair.

For decades, still news photography was the primary channel through which the public became aware of incidents of racial injustice.

  • A horrific 1930 photo of the lynching of J. Thomas Shipp and Abraham S. Smith, two black men in Marion, Indiana, brought the incident to national attention and inspired the song “Strange Fruit.” But the killers were never brought to justice.
  • Photos of the mutilated body of Emmett Till catalyzed a nationwide reaction to his 1955 lynching in Mississippi.

In the 1960s, television news footage brought scenes of police turning dogs and water cannons on peaceful civil rights protesters in Birmingham and Selma, Alabama into viewers’ living rooms.

  • The TV coverage was moving in both senses of the word.

In 1991, a camcorder tape shot by a Los Angeles plumber named George Holliday captured images of cops brutally beating Rodney King.

  • In the pre-internet era, it was only after the King tape was broadcast on TV that Americans could see it for themselves.

Over the past decade, smartphones have enabled witnesses and protesters to capture and distribute photos and videos of injustice quickly — sometimes, as it’s happening.

  • This power helped catalyze the Black Lives Matter movement beginning in 2013 and has played a growing role in broader public awareness of police brutality.

Between the lines: For a brief moment mid-decade, some hoped that the combination of a public well-supplied with video recording devices and requirements that police wear bodycams would introduce a new level of accountability to law enforcement.

The bottom line: Smartphones and social media deliver direct accounts of grief- and rage-inducing stories…(More)”.

Centering Racial Equity Throughout Data Integration

Curated on Posted on by Stefaan Verhulst

Toolkit by AISP: “Societal “progress” is often marked by the construction of new infrastructure that fuels change and innovation. Just as railroads and interstate highways were the defining infrastructure projects of the 1800 and 1900s, the development of data infrastructure is a critical innovation of our century. Railroads and highways were drivers of development and prosperity for some investors and sites. Yet other individuals and communities were harmed, displaced, bypassed, ignored, and forgotten by
those efforts.

At this moment in our history, we can co-create data infrastructure to promote racial equity and the public good, or we can invest in data infrastructure that disregards the historical, social, and political context—reinforcing racial inequity that continues to harm communities. Building data infrastructure without a racial equity lens and understanding of historical context will exacerbate existing inequalities along the lines of race, gender, class, and ability. Instead, we commit to contextualize our work in the historical and structural oppression that shapes it, and organize stakeholders across geography, sector, and experience to center racial equity throughout data integration….(More)”.

Sharing Health Data and Biospecimens with Industry — A Principle-Driven, Practical Approach

Curated on Posted on by Stefaan Verhulst

Kayte Spector-Bagdady et al at the New England Journal of Medicine: “The advent of standardized electronic health records, sustainable biobanks, consumer-wellness applications, and advanced diagnostics has resulted in new health information repositories. As highlighted by the Covid-19 pandemic, these repositories create an opportunity for advancing health research by means of secondary use of data and biospecimens. Current regulations in this space give substantial discretion to individual organizations when it comes to sharing deidentified data and specimens. But some recent examples of health care institutions sharing individual-level data and specimens with companies have generated controversy. Academic medical centers are therefore both practically and ethically compelled to establish best practices for governing the sharing of such contributions with outside entities.1 We believe that the approach we have taken at Michigan Medicine could help inform the national conversation on this issue.

The Federal Policy for the Protection of Human Subjects offers some safeguards for research participants from whom data and specimens have been collected. For example, researchers must notify participants if commercial use of their specimens is a possibility. These regulations generally cover only federally funded work, however, and they don’t apply to deidentified data or specimens. Because participants value transparency regarding industry access to their data and biospecimens, our institution set out to create standards that would better reflect participants’ expectations and honor their trust. Using a principlist approach that balances beneficence and nonmaleficence, respect for persons, and justice, buttressed by recent analyses and findings regarding contributors’ preferences, Michigan Medicine established a formal process to guide our approach….(More)”.

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)”.