The Pandemic Is No Excuse to Surveil Students

Curated on Posted on by Stefaan Verhulst

 Zeynep Tufekci in the Atlantic: “In Michigan, a small liberal-arts college is requiring students to install an app called Aura, which tracks their location in real time, before they come to campus. Oakland University, also in Michigan, announced a mandatory wearable that would track symptoms, but, facing a student-led petition, then said it would be optional. The University of Missouri, too, has an app that tracks when students enter and exit classrooms. This practice is spreading: In an attempt to open during the pandemic, many universities and colleges around the country are forcing students to download location-tracking apps, sometimes as a condition of enrollment. Many of these apps function via Bluetooth sensors or Wi-Fi networks. When students enter a classroom, their phone informs a sensor that’s been installed in the room, or the app checks the Wi-Fi networks nearby to determine the phone’s location.

As a university professor, I’ve seen surveillance like this before. Many of these apps replicate the tracking system sometimes installed on the phones of student athletes, for whom it is often mandatory. That system tells us a lot about what we can expect with these apps.

There is a widespread charade in the United States that university athletes, especially those who play high-profile sports such as football and basketball, are just students who happen to be playing sports as amateurs “in their free time.” The reality is that these college athletes in high-level sports, who are aggressively recruited by schools, bring prestige and financial resources to universities, under a regime that requires them to train like professional athletes despite their lack of salary. However, making the most of one’s college education and training at that level are virtually incompatible, simply because the day is 24 hours long and the body, even that of a young, healthy athlete, can only take so much when training so hard. Worse, many of these athletes are minority students, specifically Black men, who were underserved during their whole K–12 education and faced the same challenge then as they do now: Train hard in hopes of a scholarship and try to study with what little time is left, often despite being enrolled in schools with mediocre resources. Many of them arrive at college with an athletic scholarship but not enough academic preparation compared with their peers who went to better schools and could also concentrate on schooling….(More)”

We the Possibility: Harnessing Public Entrepreneurship to Solve Our Most Urgent Problems

Curated on Posted on by Stefaan Verhulst

Book by Mitchell Weiss: “During his years as a public official, Mitchell Weiss was told that government can’t do new things or solve tough challenges–it’s too big and slow and bureaucratic. Sadly, this is what so many of us have come to believe. But in the wake of the Boston Marathon bombings, he and his city hall colleagues raced to support survivors in new, innovative ways. This kind of entrepreneurial spirit and savvy in government is growing, transforming the public sector’s response to big problems at all levels.

In this inspiring and instructive book, Weiss, now a professor at Harvard Business School, argues that we must shift from a mindset of “Probability Government”–overly focused on performance management and on mimicking “best” practices–to “Possibility Government.” This means a leap to public leadership and management that embraces more imagination and riskier projects.

Weiss shares the basic tenets of this new way of governing in the book’s three sections:

  • Government that can imagine. Seeing problems as opportunities, and designing solutions with citizens.
  • Government that can try new things. Testing and experimentation as a regular part of solving public problems.
  • Government that can scale. Harnessing platform techniques for innovation and growth; and how public entrepreneurship can reinvigorate democracy.

The lessons unfold in the timely episodes Weiss has seen and studied: a heroin hackathon in opioid-ravaged Cincinnati; a series of blockchain experiments in Tbilisi to protect Georgian property from the Russians; the U.S. Special Operations Command prototyping of a hoverboard for chasing pirates, among many others.

At a crucial moment in the evolution of government’s role in our society, We the Possibility provides both inspiration and a positive model to help shape progress for generations to come….(More)”.

Designing Nudges for Success in Health Care

Curated on Posted on by Stefaan Verhulst

Joseph D. Harrison at AMA Journal of Ethics: “Nudges are subtle changes to the design of the environment or the framing of information that can influence our behaviors. There is significant potential to use nudges in health care to improve patient outcomes and transform health care delivery. However, these interventions must be tested and implemented using a systematic approach. In this article, we describe several ways to design nudges for success by focusing on optimizing and fitting them into the clinical workflow, engaging the right stakeholders, and rapid experimentation….(More)”.

Behavioral Contagion Could Spread the Benefits of a Carbon Tax

Curated on Posted on by Stefaan Verhulst

Robert H. Frank at the New York Times: “…Why, then, hasn’t the United States adopted a carbon tax? One hurdle is the fear that emissions would fall too slowly in response to a carbon tax, that more direct measures are needed. Another difficulty is that political leaders have reason to fear voter opposition to taxation of any kind. But there are persuasive rejoinders to both objections.

Regarding the first, critics are correct that a carbon tax alone won’t parry the climate threat. It is also true that as creatures of habit, humans tend to change their behavior only slowly, even in the face of significant financial incentives. But even small changes in behavior are greatly amplified by behavioral contagion — the social scientist’s term for how ideas and behaviors spread from person to person like infectious diseases. And if a carbon tax were to shift the behavior of some individuals now, those changes would quickly spread more widely.

Smoking rates, for example, changed little in the short run even as cigarette taxes rose sharply, but that wasn’t the end of the story. The most powerful predictor of whether someone will smoke is the percentage of her friends who smoke. Most smokers stick with their habit in the face of higher taxes, but a small minority quit, and still others refrain from starting.

Every peer group that includes those people thus contains a smaller proportion of smokers, which influences still others to quit or refrain, and so on. This contagion process explains why the percentage of American adults who smoke has fallen by two-thirds since the mid-1960s.

Behavioral contagion would similarly amplify the effects of a carbon tax. By making solar power cheaper in comparison with fossil fuels, for example, the tax would initially encourage a small number of families to install solar panels on their rooftops. But as with cigarette taxes, it’s the indirect effects that really matter….(More)”.

How Integrated Data Can Support COVID-19 Crisis and Recovery

Curated on Posted on by Stefaan Verhulst

Blog by Actionable Intelligence for Social Policy (AISP): “…State and local leaders are called upon to respond to the immediate harms of COVID-19. Yet with a looming recession threatening to undo gains among marginalized groups — particularly the Black middle class — tools to understand and disrupt long-term impacts on economic mobility and well-being are also urgently needed.

Administrative data[3] — the information collected during the course of routine service delivery, program administration, and business operations — provide an essential tool to help policymakers, community leaders, and researchers understand short- and long-term impacts of the pandemic. Several jurisdictions now have the capacity to link administrative data across programs in order to better understand how individuals interact with multiple systems, study longitudinal outcomes, and identify vulnerable subpopulations. As the COVID-19 crisis reveals weaknesses in the U.S. social safety net, states and localities with integrated administrative data infrastructure can use their capacity to identify populations and needs otherwise overlooked. Youth who “age out” of the child welfare system or individuals experiencing chronic homelessness often remain invisible when using traditional methods, aggregate data, or administrative records from a single source.

This blogpost demonstrates how nimble state and local data integration efforts have leveraged their capacity to quickly respond to and understand the impacts of COVID-19, while also reflecting on what can be done to mitigate harm and shift thinking about social welfare and the safety net….(More)”.

Encouraging Participation and Cooperation in Contact Tracing

Curated on Posted on by Stefaan Verhulst

Lessons from Survey Research by National Academies of Sciences, Engineering, and Medicine: “Contact tracing shares important features with the collection of survey data, as well as the taking of the U.S. Census. This rapid expert consultation suggests proven strategies from survey research that decision makers can use to encourage participation in and cooperation with contact tracing efforts along two fronts: encouraging individuals to respond to outreach from health department officials regarding participation in contact tracing and case investigation, and encouraging those who do participate to share information about people whom they may have exposed to COVID-19.

Encouraging Participation and Cooperation in Contact Tracing is intended to help decision makers in local public health departments and local governments increase participation and cooperation in contact tracing related to COVID-19. This publication focuses on contact tracing methods that involve phone, text, or email interviews with people who have tested positive and with others they may have exposed to the virus…(More)”.

The Broken Algorithm That Poisoned American Transportation

Curated on Posted on by Stefaan Verhulst

Aaron Gordon at Vice: “…The Louisville highway project is hardly the first time travel demand models have missed the mark. Despite them being a legally required portion of any transportation infrastructure project that gets federal dollars, it is one of urban planning’s worst kept secrets that these models are error-prone at best and fundamentally flawed at worst.

Recently, I asked Renn how important those initial, rosy traffic forecasts of double-digit growth were to the boondoggle actually getting built.

“I think it was very important,” Renn said. “Because I don’t believe they could have gotten approval to build the project if they had not had traffic forecasts that said traffic across the river is going to increase substantially. If there isn’t going to be an increase in traffic, how do you justify building two bridges?”

ravel demand models come in different shapes and sizes. They can cover entire metro regions spanning across state lines or tackle a small stretch of a suburban roadway. And they have gotten more complicated over time. But they are rooted in what’s called the Four Step process, a rough approximation of how humans make decisions about getting from A to B. At the end, the model spits out numbers estimating how many trips there will be along certain routes.

As befits its name, the model goes through four steps in order to arrive at that number. First, it generates a kind of algorithmic map based on expected land use patterns (businesses will generate more trips than homes) and socio-economic factors (for example, high rates of employment will generate more trips than lower ones). Then it will estimate where people will generally be coming from and going to. The third step is to guess how they will get there, and the fourth is to then plot their actual routes, based mostly on travel time. The end result is a number of how many trips there will be in the project area and how long it will take to get around. Engineers and planners will then add a new highway, transit line, bridge, or other travel infrastructure to the model and see how things change. Or they will change the numbers in the first step to account for expected population or employment growth into the future. Often, these numbers are then used by policymakers to justify a given project, whether it’s a highway expansion or a light rail line…(More)”.

AI technologies — like police facial recognition — discriminate against people of colour

Curated on Posted on by Stefaan Verhulst

Jane Bailey et al at The Conversation: “…In his game-changing 1993 book, The Panoptic Sort, scholar Oscar Gandy warned that “complex technology [that] involves the collection, processing and sharing of information about individuals and groups that is generated through their daily lives … is used to coordinate and control their access to the goods and services that define life in the modern capitalist economy.” Law enforcement uses it to pluck suspects from the general public, and private organizations use it to determine whether we have access to things like banking and employment.

Gandy prophetically warned that, if left unchecked, this form of “cybernetic triage” would exponentially disadvantage members of equality-seeking communities — for example, groups that are racialized or socio-economically disadvantaged — both in terms of what would be allocated to them and how they might come to understand themselves.

Some 25 years later, we’re now living with the panoptic sort on steroids. And examples of its negative effects on equality-seeking communities abound, such as the false identification of Williams.

Pre-existing bias

This sorting using algorithms infiltrates the most fundamental aspects of everyday life, occasioning both direct and structural violence in its wake.

The direct violence experienced by Williams is immediately evident in the events surrounding his arrest and detention, and the individual harms he experienced are obvious and can be traced to the actions of police who chose to rely on the technology’s “match” to make an arrest. More insidious is the structural violence perpetrated through facial recognition technology and other digital technologies that rate, match, categorize and sort individuals in ways that magnify pre-existing discriminatory patterns.

Structural violence harms are less obvious and less direct, and cause injury to equality-seeking groups through systematic denial to power, resources and opportunity. Simultaneously, it increases direct risk and harm to individual members of those groups.

Predictive policing uses algorithmic processing of historical data to predict when and where new crimes are likely to occur, assigns police resources accordingly and embeds enhanced police surveillance into communities, usually in lower-income and racialized neighbourhoods. This increases the chances that any criminal activity — including less serious criminal activity that might otherwise prompt no police response — will be detected and punished, ultimately limiting the life chances of the people who live within that environment….(More)”.

Where are there gaps in gender data in five Latin American and Caribbean countries?

Curated on Posted on by Stefaan Verhulst

Data2X: “This report builds on our 2019 technical report, Bridging the Gap: Mapping Gender Data Availability in Africa, but shifts the geographic focus to Latin America and the Caribbean (LAC).

It reports on the availability of gender data in Colombia, Costa Rica, the Dominican Republic, Jamaica, and Paraguay at the international, national, and microdata levels, and it assesses the availability of 93 gender indicators, their disaggregations, and their frequency of observation in international and national databases and publications.

Additionally, with the assistance of the UN Economic Commission for Latin America (ECLAC), the report documents the availability of statistical indicators to support gender development plans in the five countries.

Through this report, we hope to help move the development community one step closer to producing high-quality and policy-relevant gender indicators to inform better decisions….Read the report.“

Internet Searches for Acute Anxiety During the Early Stages of the COVID-19 Pandemic

Curated on Posted on by Stefaan Verhulst

Paper by John W. Ayers et al: “There is widespread concern that the coronavirus disease 2019 (COVID-19) pandemic may harm population mental health, chiefly owing to anxiety about the disease and its societal fallout. But traditional population mental health surveillance (eg, telephone surveys, medical records) is time consuming, expensive, and may miss persons who do not participate or seek care. To evaluate the association of COVID-19 with anxiety on a population basis, we examined internet searches indicative of acute anxiety during the early stages of the COVID-19 pandemic.Methods

The analysis relied on nonidentifiable, aggregate, public data and was exempted by the University of California San Diego Human Research Protections Program. Acute anxiety, including colloquially called anxiety attacks or panic attacks, was monitored because of its higher prevalence relative to other mental health problems. It can lead to other mental health problems (including depression), it is triggered by outside stressors, and it is socially contagious. Using Google Trends (https://trends.google.com/trends) we monitored the daily fraction of all internet searches (thereby adjusting the results for any change in total queries) that included the terms anxiety or panic in combination with attack (including panic attack, signs of anxiety attack, anxiety attack symptoms) that originated from the US from January 1, 2004, through May 4, 2020. Raw search counts were inferred using Comscore estimates (comscore.com).

We compared search volumes after President Trump declared a national COVID-19 emergency on March 13, 2020, with expected search volumes if COVID-19 had not occurred, thereby taking into account the historical trend and periodicity in the data. Expected volumes were computed using an autoregressive integrated moving average model,4 based on historical trends from January 1, 2004 to March 12, 2020, to predict counterfactual trends for March 13, 2020 to May 9, 2020. The expected volumes with prediction intervals (PIs) and ratio of observed and expected volumes with bootstrap CIs were computed using R statistical software (version 3.5.3, R Foundation). The results were similar if we varied our interruption date plus or minus 1 week….(More)”.