The Data Dividend Project

Curated on Posted on by Stefaan Verhulst

About: “The Data Dividend Project is a movement dedicated to taking back control of our personal data: our data is our property, and if we allow companies to use it, we should get paid for it. The DDP is the brainchild of former presidential candidate Andrew Yang. Its primary objective is to establish and enforce data property rights under laws such as the California Consumer Privacy Act (CCPA), which went into effect on January 1, 2020.

Every day, people are generating data simply by going about the business of living in an ever connected and digital world. Unbeknownst to most people, technology companies are tracking their every move online, extracting this data, and then buying and selling it for big money. The sale and resale of consumer data is called data brokering, which is itself a $200 billion industry.

For example, technology companies can extract location data from your mobile phone and sell it to advertisers who can then turn around and post local ads to you in real time. Until recently, the data collector – in this case, the technology company – was deemed to own the data. As the owner, the technology company could sell that data and profit handsomely. Meanwhile, you generated the data but received no share of those profits. DDP plans to change that.

Until this year, you, as the American consumer, had little recourse against technology companies who were profiting off your data without your consent or knowledge. Now, under the CCPA, Californians are endowed with a collection of unalienable data rights: the right to know what information is being collected on you, the right to delete that information, and the right to opt-out from technology companies collecting your data. These rights, however, are ignored and abused by technology companies. And unfortunately, individual consumers don’t have the leverage to be able to go up against these companies. That’s where DDP comes in….(More)“

Best Practices to Cover Ad Information Used for Research, Public Health, Law Enforcement & Other Uses

Curated on Posted on by Stefaan Verhulst

Press Release: “The Network Advertising Initiative (NAI) released privacy Best Practices for its members to follow if they use data collected for Tailored Advertising or Ad Delivery and Reporting for non-marketing purposes, such as sharing with research institutions, public health agencies, or law enforcement entities.

“Ad tech companies have data that can be a powerful resource for the public good if they follow this set of best practices for consumer privacy,” said Leigh Freund, NAI President and CEO. “During the COVID-19 pandemic, we’ve seen the opportunity for substantial public health benefits from sharing aggregate and de-identified location data.”

The NAI Code of Conduct – the industry’s premier self-regulatory framework for privacy, transparency, and consumer choice – covers data collected and used for Tailored Advertising or Ad Delivery and Reporting. The NAI Code has long addressed certain non-marketing uses of data collected for Tailored Advertising and Ad Delivery and Reporting by prohibiting any
eligibility uses of such data, including uses for credit, insurance, healthcare, and employment decisions.

The NAI has always firmly believed that data collected for advertising purposes should not have a negative effect on consumers in their daily lives. However, over the past year, novel data uses have been introduced, especially during the recent health crisis. In the case of opted-in data such as Precise Location Information, a company may determine a user would benefit from more detailed disclosure in a just-in-time notice about non-marketing uses of the data being collected….(More)”.

Social Distancing and Social Capital: Why U.S. Counties Respond Differently to Covid-19

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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

Curated on Posted on by Stefaan Verhulst

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)”.