NBER Paper by Wenzhi Ding et al: Since social distancing is the primary strategy for slowing the spread of many diseases, understanding why U.S. counties respond differently to COVID-19 is critical for designing effective public policies. Using daily data from about 45 million mobile phones to measure social distancing we examine how counties responded to both local COVID-19 cases and statewide shelter-in-place orders. We find that social distancing increases more in response to cases and official orders in counties where individuals historically (1) engaged less in community activities and (2) demonstrated greater willingness to incur individual costs to contribute to social objectives. Our work highlights the importance of these two features of social capital—community engagement and individual commitment to societal institutions—in formulating public health policies….(More)”
What Nobel Laureate Elinor Ostrom’s early work tells us about defunding the police
Blog by Aaron Vansintjan: “…As she concluded in her autobiographical reflections published two years before she died in 2012, “For policing, increasing the size of governmental units consistently had a negative impact on the level of output generated as well as on efficiency of service provision… smaller police departments… consistently outperformed their better trained and better financed larger neighbors.”
But why did this happen? To explain this, Ostrom showed how, in small communities with small police forces, citizens are more active in monitoring their neighborhoods. Officers in smaller police forces also have more knowledge of the local area and better connections with the community.
She also found that larger, more centralized police forces also have a negative effect on other public services. With a larger police bureaucracy, other local frontline professionals with less funding — social workers, mental health support centers, clinics, youth support services — have less of a say in how to respond to a community’s issues such as drug use or domestic violence. The bigger the police department, the less citizens — especially those that are already marginalized, like migrants or Black communities — have a say in how policing should be conducted.
This finding became a crucial step in Ostrom’s groundbreaking work on how communities manage their resources sustainably without outside help — through deliberation, resolving conflict and setting clear community agreements. This is what she ended up becoming famous for, and what won her the Nobel Memorial Prize in Economic Sciences, placing her next to some of the foremost economists in the world.
But her research on policing shouldn’t be forgotten: It shows that, when it comes to safer communities, having more funding or larger services is not important. What’s important is the connections and trust between the community and the service provider….(More)”.
IRS Used Cellphone Location Data to Try to Find Suspects
Byron Tau at the Wall Street Journal: “The Internal Revenue Service attempted to identify and track potential criminal suspects by purchasing access to a commercial database that records the locations of millions of American cellphones.
The IRS Criminal Investigation unit, or IRS CI, had a subscription to access the data in 2017 and 2018, and the way it used the data was revealed last week in a briefing by IRS CI officials to Sen. Ron Wyden’s (D., Ore.) office. The briefing was described to The Wall Street Journal by an aide to the senator.
IRS CI officials told Mr. Wyden’s office that their lawyers had given verbal approval for the use of the database, which is sold by a Virginia-based government contractor called Venntel Inc. Venntel obtains anonymized location data from the marketing industry and resells it to governments. IRS CI added that it let its Venntel subscription lapse after it failed to locate any targets of interest during the year it paid for the service, according to Mr. Wyden’s aide.
Justin Cole, a spokesman for IRS CI, said it entered into a “limited contract with Venntel to test their services against the law enforcement requirements of our agency.” IRS CI pursues the most serious and flagrant violations of tax law, and it said it used the Venntel database in “significant money-laundering, cyber, drug and organized-crime cases.”
The episode demonstrates a growing law enforcement interest in reams of anonymized cellphone movement data collected by the marketing industry. Government entities can try to use the data to identify individuals—which in many cases isn’t difficult with such databases.
It also shows that data from the marketing industry can be used as an alternative to obtaining data from cellphone carriers, a process that requires a court order. Until 2018, prosecutors needed “reasonable grounds” to seek cell tower records from a carrier. In June 2018, the U.S. Supreme Court strengthened the requirement to show probable cause a crime has been committed before such data can be obtained from carriers….(More)”
The Bigot in the Machine: Bias in Algorithmic Systems
Article by Barbara Fister: “We are living in an “age of algorithms.” Vast quantities of information are collected, sorted, shared, combined, and acted on by proprietary black boxes. These systems use machine learning to build models and make predictions from data sets that may be out of date, incomplete, and biased. We will explore the ways bias creeps into information systems, take a look at how “big data,” artificial intelligence and machine learning often amplify bias unwittingly, and consider how these systems can be deliberately exploited by actors for whom bias is a feature, not a bug. Finally, we’ll discuss ways we can work with our communities to create a more fair and just information environment….(More)”.
Exploring Blockchain Technology for Government Transparency
Report by the World Economic Forum: “The costs to society of public-sector corruption and weak accountability are staggering. In many parts of the world, public-sector corruption is the single-largest challenge, stifling social, economic and environmental development. Often, corruption centres around a lack of transparency, inadequate record-keeping and low public accountability.
Blockchain and distributed ledger technologies, when applied thoughtfully to certain corruption-prone government processes, can potentially increase transparency and accountability in these systems, reducing the risk or prevalence of corrupt activity.
In partnership with the Inter-American Development Bank (IDB) and the Office of the Inspector General of Colombia (Procuraduría General de Colombia), the Forum has led a multistakeholder team to investigate, design and trial the use of blockchain technology for corruption-prone government processes, anchored in the use case of public procurement.
Using cryptography and distributed consensus mechanisms, blockchain provides the unique combination of permanent and tamper-evident record-keeping, transaction transparency and auditability, automated functions with “smart contracts”, and the reduction of centralized authority and information ownership within processes. These properties make blockchain a high potential emerging technology to address corruption. The project chose to focus on the public procurement process because it constitutes one of the largest sites of corruption globally, stands to benefit from these technology properties and plays a significant role in serving public interest…(More)”.
Modeling the Human Trajectory
David Roodman at Open Philanthropy: “… How much should we care about people who will live far in the future? Or about chickens today? What events could extinguish civilization? Could artificial intelligence (AI) surpass human intelligence?
One strand of analysis that has caught our attention is about the pattern of growth of human society over many millennia, as measured by number of people or value of economic production. Perhaps the mathematical shape of the past tells us about the shape of the future. I dug into that subject. A draft of my technical paper is here. (Comments welcome.) In this post, I’ll explain in less technical language what I learned.
It’s extraordinary that the larger the human economy has become—the more people and the more goods and services they produce—the faster it has grown on average. Now, especially if you’re reading quickly, you might think you know what I mean. And you might be wrong, because I’m not referring to exponential growth. That happens when, for example, the number of people carrying a virus doubles every week. Then the growth rate (100% increase per week) holds fixed. The human economy has grown super-exponentially. The bigger it has gotten, the faster it has doubled, on average. The global economy churned out $74 trillion in goods and services in 2019, twice as much as in 2000.1 Such a quick doubling was unthinkable in the Middle Ages and ancient times. Perhaps our earliest doublings took millennia.
If global economic growth keeps accelerating, the future will differ from the present to a mind-boggling degree. The question is whether there might be some plausibility in such a prospect. That is what motivated my exploration of the mathematical patterns in the human past and how they could carry forward. Having now labored long on the task, I doubt I’ve gained much perspicacity. I did come to appreciate that any system whose rate of growth rises with its size is inherently unstable. The human future might be one of explosion, perhaps an economic upwelling that eclipses the industrial revolution as thoroughly as it eclipsed the agricultural revolution. Or the future could be one of implosion, in which environmental thresholds are crossed or the creative process that drives growth runs amok, as in an AI dystopia. More likely, these impulses will mix.
I now understand more fully a view that shapes the work of Open Philanthropy. The range of possible futures is wide. So it is our task as citizens and funders, at this moment of potential leverage, to lower the odds of bad paths and raise the odds of good ones….(More)”.
Gender gaps in urban mobility
Paper by Laetitia Gauvin, Michele Tizzoni, Simone Piaggesi, Andrew Young, Natalia Adler, Stefaan Verhulst, Leo Ferres & Ciro Cattuto in Humanities and Social Sciences Communications: “Mobile phone data have been extensively used to study urban mobility. However, studies based on gender-disaggregated large-scale data are still lacking, limiting our understanding of gendered aspects of urban mobility and our ability to design policies for gender equality. Here we study urban mobility from a gendered perspective, combining commercial and open datasets for the city of Santiago, Chile.
We analyze call detail records for a large cohort of anonymized mobile phone users and reveal a gender gap in mobility: women visit fewer unique locations than men, and distribute their time less equally among such locations. Mapping this mobility gap over administrative divisions, we observe that a wider gap is associated with lower income and lack of public and private transportation options. Our results uncover a complex interplay between gendered mobility patterns, socio-economic factors and urban affordances, calling for further research and providing insights for policymakers and urban planners….(More)”.
Scraping Court Records Data to Find Dirty Cops
Article by Lawsuit.org: “In the 2002 dystopian sci-fi film “Minority Report,” law enforcement can manage crime by “predicting” illegal behavior before it happens. While fiction, the plot is intriguing and contributes to the conversation on advanced crime-fighting technology. However, today’s world may not be far off.
Data’s role in our lives and more accessibility to artificial intelligence is changing the way we approach topics such as research, real estate, and law enforcement. In fact, recent investigative reporting has shown that “dozens of [American] cities” are now experimenting with predictive policing technology.
Despite the current controversy surrounding predictive policing, it seems to be a growing trend that has been met with little real resistance. We may be closer to policing that mirrors the frightening depictions in “Minority Report” than we ever thought possible.
Fighting Fire With Fire
In its current state, predictive policing is defined as:
“The usage of mathematical, predictive analytics, and other analytical techniques in law enforcement to identify potential criminal activity. Predictive policing methods fall into four general categories: methods for predicting crimes, methods for predicting offenders, methods for predicting perpetrators’ identities, and methods for predicting victims of crime.”
While it might not be possible to prevent predictive policing from being employed by the criminal justice system, perhaps there are ways we can create a more level playing field: One where the powers of big data analysis aren’t just used to predict crime, but also are used to police law enforcement themselves.
Below, we’ve provided a detailed breakdown of what this potential reality could look like when applied to one South Florida county’s public databases, along with information on how citizens and communities can use public data to better understand the behaviors of local law enforcement and even individual police officers….(More)”.
Normalizing Health-Positive Technology
Article by By Sara J. Singer, Stephen Downs, Grace Ann Joseph, Neha Chaudhary, Christopher Gardner, Nina Hersher, Kelsey P. Mellard, Norma Padrón & Yennie Solheim: “….Aligning the technology sector with a societal goal of greater health and well-being entails a number of shifts in thinking. The most fundamental is understanding health not as a vertical market segment, but as a horizontal value: In addition to developing a line of health products or services, health should be expressed across a company’s full portfolio of products and services. Rather than pushing behaviors on people through information and feedback, technology companies should also pull behaviors from people by changing the environment and products they are offered; in addition to developing technology to help people overcome the challenge of being healthy, we need to envision technology that helps to reduce the challenges to being healthy. And in addition to holding individuals responsible for choices that they make, we also need to recognize the collective responsibility that society bears for the choices it makes available.
How to catalyze these shifts?
To find out, we convened a “tech-enabled health,” in which 50 entrepreneurs, leaders from large technology companies, investors, policymakers, clinicians, and public health experts designed a hands-on, interactive, and substantively focused agenda. Participants brainstormed ways that consumer-facing technologies could help people move more, eat better, sleep well, stay socially connected, and reduce stress. In groups and collectively, participants also considered ways in which ideas related and might be synergistic, potential barriers and contextual conditions that might impede or support transformation, and strategies for catalyzing the desired shift. Participants were mixed in terms of sector, discipline, and gender (though the attendees were not as diverse in terms of race/ethnicity or economic strata as the users we potentially wanted to impact—a limitation noted by participants). We intentionally maintained a positive tone, emphasizing potential benefits of shifting toward a health-positive approach, rather than bemoaning the negative role that technology can play….(More)”.
Defining a ‘new normal’ for data privacy in the wake of COVID-19
Jack Dunn at IAPP: “…It is revealing that our relationship with privacy is amorphous and requires additional context in light of transformative technologies, new economic realities and public health emergencies. How can we reasonably evaluate the costs and benefits of Google or Facebook sharing location data with the federal government when it has been perfectly legal for Walgreen’s to share access to customer data with pharmaceutical advertisers? How does aggregating and anonymizing data safeguard privacy when a user’s personal data can be revealed through other data points?
The pandemic is only revealing that we’ve yet to reach a consensus on privacy norms that will come to define the digital age.
This isn’t the first time that technology confounded notions of privacy and consumer protection. In fact, the constitutional right to privacy was born out of another public health crisis. Before 1965, 32 women per 100,000 live births died while giving birth. Similarly, 25 infants died per 100,000 live births. As a result, medical professionals and women’s rights advocates began arguing for greater access to birth control. When state legislatures sought to minimize access, birth control advocates filed lawsuits that eventually lead to the Supreme Court’s seminal case regarding the right to privacy, Griswold v. Connecticut.…
Today, there is growing public concern over the way in which consumer data is used to consolidate economic gain among the few while steering public perception among the many — particularly at a time when privacy seems to be the price for ending public health emergencies.
But the COVID-19 outbreak is also highlighting how user data has the capacity to improve consumer well being and public health. While strict adherence to traditional notions of privacy may be ineffectual in a time of exponential technological growth, the history of our relationship to privacy and technology suggests regulatory policies can strike a balance between otherwise competing interests….(More)“.