Collection

The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement

Curated on October 8, 2017August 3, 2018 by Stefaan Verhulst

Book by Andrew Guthrie Ferguson on “The consequences of big data and algorithm-driven policing and its impact on law enforcement…In a high-tech command center in downtown Los Angeles, a digital map lights up with 911 calls, television monitors track breaking news stories, surveillance cameras sweep the streets, and rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence.

This is just a glimpse into a future where software predicts future crimes, algorithms generate virtual “most-wanted” lists, and databanks collect personal and biometric information. The Rise of Big Data Policing introduces the cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool.

Andrew Guthrie Ferguson reveals how these new technologies —viewed as race-neutral and objective—have been eagerly adopted by police departments hoping to distance themselves from claims of racial bias and unconstitutional practices. After a series of high-profile police shootings and federal investigations into systemic police misconduct, and in an era of law enforcement budget cutbacks, data-driven policing has been billed as a way to “turn the page” on racial bias.

But behind the data are real people, and difficult questions remain about racial discrimination and the potential to distort constitutional protections.

In this first book on big data policing, Ferguson offers an examination of how new technologies will alter the who, where, when and how we police. These new technologies also offer data-driven methods to improve police accountability and to remedy the underlying socio-economic risk factors that encourage crime….(More)”

Paraguay’s transparency alchemists

Curated on October 5, 2017May 29, 2019 by Stefaan Verhulst

Story by the Open Contracting Partnership: “….The “Cocido de oro” scandal is seen as part of a well-organized and well-informed youth movement that has sprung up in Paraguay in recent years. An equally dramatic controversyinvolving alleged corruption and unfair staff appointments at one of the country’s top public universities led to the resignation of the Chancellor and other senior staff in September 2015. Mostly high school and university students, they are no longer willing to tolerate the waste and corruption in public spending — a hangover from 35 years of authoritarian rule. They expect their government to be more open and accountable, and public decision-making processes to be more inclusive and democratic.

Thanks to government initiatives that have sought to give citizens greater access to information about public institutions, these students, along with investigative journalists and other civil society groups, are starting to engage actively in civic affairs. And they are data-savvy, basing recommendations on empirical evidence about government policies and processes, how they are implemented, and whether they are working.

Leading the pack is the country’s public procurement office, which runs a portal that ranks among the most open government data sources in the world. Together with information about budgets, public bodies’ payrolls, and other government data, this is helping Paraguayans to tackle some of the biggest long-standing problems faced by the government, like graft, overpricing, nepotism and influence-peddling….

The government recognizes there’s still a long way to go in their quest to open up public data. Few institutions have opened their databases or publish their data on an open data portal, and use of the data that has been published is still limited, according to a report on the country’s third OGP Action Plan. Priority data sets aren’t accessible in ways that meet the needs of civil society, the report adds.

And yet, the tremors of a tectonic shift in transparency and accountability in Paraguay are already being felt. In a short time, armed with access to information, citizens have started engaging with how public money is and should be spent.

The government is now doubling down on its strategy of fostering public participation, using cutting-edge technology to increase citizens’ access to data about their state institutions. Health, education, and municipal-level government, and procurement spending across these areas are being prioritized….(More).

Comparing Models of Collaborative Journalism

Curated on October 5, 2017May 29, 2019 by Stefaan Verhulst

Center for Cooperative Media: “Working cooperatively is nothing new, to be sure, but how frequently and impactfully news organizations have been collaborating over the last few years is certainly something new. Dramatically shifting business models, technological advances and seismic shifts in audience have lead to groundbreaking and award-winning collaborations around the world, including the Panama Papers and Electionland.

Today the Center released its first full research paper on this topic, identifying six distinct models of collaborative journalism. The report, authored by Center research director Sarah Stonbely, explains the underpinnings of each model and also explores the history of collaborative journalism.

“As we document, collaborative journalism is now being practiced on a scale that constitutes a revolution in journalism,” Stonbely writes. “The many trials and errors of the last decade have generated cooperative efforts that have stood the test of time and are showing the way for others.

“While lessons are still being learned, collaborative journalism has evolved from experiment to common practice.”

In her research, Stonbely focused on cooperative arrangements, formal and informal, between two or more news and information organizations which aim to supplement each group’s resources and maximize the impact of the content produced.

She separates various kinds of collaboration by comparing levels of integration versus time, which, when viewed on a matrix, creates six models of collaborative journalism:

Millions of dollars are being poured into such collaborative reporting projects and cooperative arrangements around the world. According to the Center’s report, for example, the Corporation for Public Broadcasting has put nearly $32 million dollars into funding 29 local and regional partnerships as of earlier this year — and that number is still growing….(More)”

How We Can Stop Earthquakes From Killing People Before They Even Hit

Curated on October 5, 2017August 3, 2018 by Stefaan Verhulst

Justin Worland in Time Magazine: “…Out of that realization came a plan to reshape disaster management using big data. Just a few months later, Wani worked with two fellow Stanford students to create a platform to predict the toll of natural disasters. The concept is simple but also revolutionary. The One Concern software pulls geological and structural data from a variety of public and private sources and uses machine learning to predict the impact of an earthquake down to individual city blocks and buildings. Real-time information input during an earthquake improves how the system responds. And earthquakes represent just the start for the company, which plans to launch a similar program for floods and eventually other natural disasters….

Previous software might identify a general area where responders could expect damage, but it would appear as a “big red blob” that wasn’t helpful when deciding exactly where to send resources, Dayton says. The technology also integrates information from many sources and makes it easy to parse in an emergency situation when every moment matters. The instant damage evaluations mean fast and actionable information, so first responders can prioritize search and rescue in areas most likely to be worst-hit, rather than responding to 911 calls in the order they are received.

One Concern is not the only company that sees an opportunity to use data to rethink disaster response. The mapping company Esri has built rapid-response software that shows expected damage from disasters like earthquakes, wildfires and hurricanes. And the U.S. government has invested in programs to use data to shape disaster response at agencies like the National Oceanic and Atmospheric Administration (NOAA)….(More)”.

Cross-sector Collaboration in Data Science for Social Good: Opportunities, Challenges, and Open Questions Raised by Working with Academic Researchers

Curated on October 5, 2017August 3, 2018 by Stefaan Verhulst

Paper by presented by Anissa Tanweer and Brittany Fiore-Gartland at the Data Science for Social Good Conference: “Recent years have seen growing support for attempts to solve complex social problems through the use of increasingly available, increasingly combinable, and increasingly computable digital data. Sometimes referred to as “data science for social good” (DSSG), these efforts are not concentrated in the hands of any one sector of society. Rather, we see DSSG emerging as an inherently multi-sector and collaborative phenomenon, with key participants hailing from governments, nonprofit organizations, technology companies, and institutions of higher education. Based on three years of participant observation in a university-hosted DSSG program, in this paper we highlight academic contributions to multi-sector DSSG collaborations, including expertise, labor, ethics, experimentation, and neutrality. After articulating both the opportunities and challenges that accompany those contributions, we pose some key open questions that demand attention from participants in DSSG programs and projects. Given the emergent nature of the DSSG phenomenon, it is our contention that how these questions come to be answered will have profound implications for the way society is organized and governed….(More)”.

A Better Way to Trace Scattered Refugees

Curated on October 5, 2017August 3, 2018 by Stefaan Verhulst

Tina Rosenberg in The New York Times: “…No one knew where his family had gone. Then an African refugee in Ottawa told him about Refunite. He went on its website and opened an account. He gave his name, phone number and place of origin, and listed family members he was searching for.

Three-quarters of a century ago, while World War II still raged, the Allies created the International Tracing Service to help the millions who had fled their homes. Its central name index grew to 50 million cards, with information on 17.5 million individuals. The index still exists — and still gets queries — today.

Index cards have become digital databases, of course. And some agencies have brought tracing into the digital age in other ways. Unicef, for example, equips staff during humanitarian emergencies with a software called Primero, which helps them get children food, medical care and other help — and register information about unaccompanied children. A parent searching for a child can register as well. An algorithm makes the connection — “like a date-finder or matchmaker,” said Robert MacTavish, who leads the Primero project.

Most United Nations agencies rely for family tracing on the International Committee of the Red Cross, the global network of national Red Cross and Red Crescent societies. Florence Anselmo, who directs the I.C.R.C.’s Central Tracing Agency, said that the I.C.R.C. and United Nations agencies can’t look in one another’s databases. That’s necessary for privacy reasons, but it’s an obstacle to family tracing.

Another problem: Online databases allow the displaced to do their own searches. But the I.C.R.C. has these for only a few emergency situations. Anselmo said that most tracing is done by the staff of national Red Cross societies, who respond to requests from other countries. But there is no global database, so people looking for loved ones must guess which countries to search.

The organization is working on developing an algorithm for matching, but for now, the search engines are human. “When we talk about tracing, it’s not only about data matching,” Anselmo said. “There’s a whole part about accompanying families: the human aspect, professionals as well as volunteers who are able to look for people — even go house to house if needed.”

This is the mom-and-pop general store model of tracing: The customer makes a request at the counter, then a shopkeeper with knowledge of her goods and a kind smile goes to the back and brings it out, throwing in a lollipop. But the world has 65 million forcibly displaced people, a record number. Personalized help to choose from limited stock is appropriate in many cases. But it cannot possibly be enough.

Refunite seeks to become the eBay of family tracing….(More)”

Let’s create a nation of social scientists

Curated on October 4, 2017August 3, 2018 by Stefaan Verhulst

Geoff Mulgan in Times Higher Education: “How might social science become more influential, more relevant and more useful in the years to come?

Recent debates about impact have largely assumed a model of social science in which a cadre of specialists, based in universities, analyse and interpret the world and then feed conclusions into an essentially passive society. But a very different view sees specialists in the academy working much more in partnership with a society that is itself skilled in social science, able to generate hypotheses, gather data, experiment and draw conclusions that might help to answer the big questions of our time, from the sources of inequality to social trust, identity to violence.

There are some powerful trends to suggest that this second view is gaining traction. The first of these is the extraordinary explosion of new ways to observe social phenomena. Every day each of us leaves behind a data trail of who we talk to, what we eat and where we go. It’s easier than ever to survey people, to spot patterns, to scrape the web or to pick up data from sensors. It’s easier than ever to gather perceptions and emotions as well as material facts and easier than ever for organisations to practice social science – whether investment organisations analysing market patterns, human resources departments using behavioural science, or local authorities using ethnography.

That deluge of data is a big enough shift on its own. However, it is also now being used to feed interpretive and predictive tools using artificial intelligence to predict who is most likely to go to hospital, to end up in prison, which relationships are most likely to end in divorce.

Governments are developing their own predictive tools, and have also become much more interested in systematic experimentation, with Finland and Canada in the lead, moving us closer to Karl Popper’s vision of “methods of trial and error, of inventing hypotheses which can be practically tested…”…

The second revolution is less visible but could be no less profound. This is the hunger of many people to be creators of knowledge, not just users; to be part of a truly collective intelligence. At the moment this shift towards mass engagement in knowledge is most visible in neighbouring fields. Digital humanities mobilise many volunteers to input data and interpret texts – for example making ancient Arabic texts machine-readable. Even more striking is the growth of citizen science – eBird had 1.5 million reports last January; some 1.5 million people in the US monitor river streams and lakes, and SETI@home has 5 million volunteers. Thousands of patients also take part in funding and shaping research on their own conditions….

We’re all familiar with the old idea that it’s better to teach a man to fish than just to give him fish. In essence these trends ask us a simple question: why not apply the same logic to social science, and why not reorient social sciences to enhance the capacity of society itself to observe, analyse and interpret?…(More)”.

Information Seeding and Knowledge Production in Online Communities: Evidence from OpenStreetMap

Curated on October 4, 2017August 3, 2018 by Stefaan Verhulst

Paper by Abhishek Nagaraj: “The wild success of a few online community-produced knowledge goods, notably Wikipedia, has obscured the fact that most attempts at forming online communities fail. A large body of work analyses motivations behind user contributions to successful, online communities but less is known, however, about early-stage interventions that might make online communities more or less successful.

This study evaluates information seeding, a popular practice to bootstrap online communities by enabling contributors to build on externally-sourced information rather that starting from scratch. I analyze the effects of information seeding on follow-on contributions using data from more than 350 million contributions made by over 577,000 contributors to OpenStreetMap, a Wikipedia-style digital map-making community that was seeded with data from the US Census. To estimate the effects of information seeding, I rely on a natural experiment in which an oversight caused about 60% of quasi-randomly chosen US counties to be seeded with a complete Census map, while the rest were seeded with less complete versions. While access to knowledge generally encourages follow-on knowledge production, I find that a higher level of information seeding significantly lowered follow-on knowledge production and contributor activity on OpenStreetMap and was also associated with lower levels of long-term quality. I argue that information seeding can crowd out contributors’ ability to develop ownership over baseline knowledge and disincentivize follow-on contributions in some circumstances. Empirical evidence supports this explanation as the mechanism through which a higher level of information seeding can stifle rather than spur knowledge production in online communities….(More)”.

Mobility Score

Curated on October 3, 2017October 24, 2018 by Stefaan Verhulst

“MobilityScore® helps you understand how easy it is to get around. It works at any location or address within the US and Canada and gives you a score ranging from 0 (no mobility choices) to 100 (excellent mobility choices).

What do we mean by mobility? Any transportation option that can help you move around your city. Transportation is changing massively as new choices emerge: ridesharing, bikesharing, carsharing. Private and on-demand mobility services have sprung up. However, tools for measuring transportation access have not kept up. That’s why we created MobilityScore as an easy-to-understand measure of transportation access.

Technical Details

MobilityScore includes all the transportation choices that can be found on TransitScreen displays, including the following services:

Public transit (subways, trains, buses, ferries, cable cars…)
Car sharing services (Zipcar, Enterprise, and one-way services like car2go)
Bike sharing services
Hailed ride sharing services (e.g. taxis, Uber, Lyft)

We have developed a common way of comparing how choices that might seem very different contribute to your mobility. For each mobility choice, we measure how long it will take you until you can start moving on it – for example, the time it takes you to leave your building, walk to a subway station, and wait for a train.

Because we’re measuring how easy it is for you to move around the city, we also consider what mobility choices look like at different times of the day and different days of the week. Mobility data is regularly collected for most services, while ridehailing (Uber/Lyft) data is based on a geographic model of arrival times.

MobilityScore’s framework is future-proof. Just like we do with TransitScreen, we will integrate future services into the calculation as they emerge (e.g. microtransit, autonomous vehicles, mobility-as-a-service)….(More)”