Stefaan Verhulst
fs.blog: “There are costs to falling community participation. Rather than simply lamenting the loss of a past golden era (as people have done in every era), Harvard political scientist Robert D. Putnam explains these costs, as well as how we might bring community participation back.
First published twenty years ago, Bowling Alone is an exhaustive, hefty work. In its 544 pages, Putnam negotiated mountains of data to support his thesis that the previous few decades had seen Americans retreat en masse from public life. Putnam argued Americans had become disconnected from their wider communities, as evidenced by changes such as a decline in civic engagement and dwindling membership rates for groups such as bowling leagues and PTAs.
Though aspects of Bowling Alone are a little dated today (“computer-mediated communication” isn’t a phrase you’re likely to have heard recently), a quick glance at 2021’s social landscape would suggest many of the trends Putnam described have only continued and apply in other parts of the world too.
Right now, polarization and social distancing have forced us apart from any sense of community to a degree that can seem irresolvable.
Will we ever bowl in leagues alongside near strangers and turn them into friends again? Will we ever bowl again at all, even if alone, or will those gleaming aisles, too-tight shoes, and overpriced sodas fade into a distant memory we recount to our children?
The idea of going into a public space for a non-essential reason can feel incredibly out of reach for many of us right now. And who knows how spaces like bowling alleys will survive in the long run without the social scenes that fuelled them. Now is a perfect time to revisit Bowling Alone to see what it can still teach us, because many of its warnings and lessons are perhaps more relevant now than at its time of publication.
One key lesson we can derive from Bowling Alone is that the less we trust each other—something which is both a cause and consequence of declining community engagement—the more it costs us. Mistrust is expensive.…(More)”
Mark Sullivan at FastCompany: “Amnesty International is producing a map of all the places in New York City where surveillance cameras are scanning residents’ faces.
The project will enlist volunteers to use their smartphones to identify, photograph, and locate government-owned surveillance cameras capable of shooting video that could be matched against people’s faces in a database through AI-powered facial recognition.
The map that will eventually result is meant to give New Yorkers the power of information against an invasive technology the usage of which and purpose is often not fully disclosed to the public. It’s also meant to put pressure on the New York City Council to write and pass a law restricting or banning it. Other U.S. cities, such as Boston, Portland, and San Francisco, have already passed such laws.
Facial recognition technology can be developed by scraping millions of images from social media profiles and driver’s licenses without people’s consent, Amnesty says. Software from companies like Clearview AI can then use computer vision algorithms to match those images against facial images captured by closed-circuit television (CCTV) or other video surveillance cameras and stored in a database.
Starting in May, volunteers will be able to use a software tool to identify all the facial recognition cameras within their view—like at an intersection where numerous cameras can often be found. The tool, which runs on a phone’s browser, lets users place a square around any cameras they see. The software integrates Google Street View and Google Earth to help volunteers label and attach geolocation data to the cameras they spot.
The map is part of a larger campaign called “Ban the Scan” that’s meant to educate people around the world on the civil rights dangers of facial recognition. Research has shown that facial recognition systems aren’t as accurate when it comes to analyzing dark-skinned faces, putting Black people at risk of being misidentified. Even when accurate, the technology exacerbates systemic racism because it is disproportionately used to identify people of color, who are already subject to discrimination by law enforcement officials. The campaign is sponsored by Amnesty in partnership with a number of other tech advocacy, privacy, and civil liberties groups.
In the initial phase of the project, which was announced last Thursday, Amnesty and its partners launched a website that New Yorkers can use to generate public comments on the New York Police Department’s (NYPD’s) use of facial recognition….(More)”.
Elizabeth Dwoskin at the Washington Post: “A pilot program called Birdwatch lets selected users write corrections and fact checks on potentially misleading tweets…
The presidential election is over, but the fight against misinformation continues.
The latest volley in that effort comes from Twitter, which on MondayannouncedBirdwatch, a pilot project that uses crowdsourcing techniques to combat falsehoods and misleading statements on its service.
The pilot, which is open to only about 1,000 select users who can apply to be contributors, will allow people to write notes with corrections and accurate information directly into misleading tweets — a method that has the potential to get quality information to people more quickly than traditional fact-checking. Fact checks that are rated by other contributors as high quality may get bumped up or rewarded with greater visibility.
Birdwatch represents Twitter’s most experimental response to one of the biggest lessons that social media companies drew from the historic events of 2020: that their existing efforts to combat misinformation — including labeling, fact-checking and sometimes removing content — were not enough to prevent falsehoods about a stolen election or the coronavirus from reaching and influencing broad swaths of the population. Researchers who studied enforcement actions by social media companies last year found that fact checks and labels are usually implemented too late, after a post or a tweet has gone viral.
The Birdwatch project — which for the duration of the pilot will function as a separate website — is novel in that it attempts to build new mechanisms into Twitter’s product that foreground fact-checking by its community of 187 million daily users worldwide. Rather than having to comb through replies to tweets to sift through what’s true or false — or having Twitter employees append to a tweet a label providing additional context — users will be able to click on a separate notes folder attached to a tweet where they can see the consensus-driven responses from the community. Twitter will have a team reviewing winning responses to prevent manipulation, though a major question is whether any part of the process will be automated and therefore more easily gamed….(More)”
Memo by the Center for Democracy and Technology: “As schools respond to COVID-19 on their campuses, some have shared student information with state and local health agencies, often to aid in contact tracing or to provide services to students. Federal and state student privacy laws, however, do not necessarily permit that sharing, and schools should seek to protect both student health and student privacy.
How Are Schools Sharing COVID-Related Student Data?
When it comes to sharing student data, schools’ practices vary widely. For example, the New York City Department of Education provides a consent form for sharing COVID-related student data. Other schools do not have consent forms, but instead, share COVID-related data as required by local or state health agencies. For instance, Orange County Public Schools in Florida assists the local health agency in contact tracing by collecting information such as students’ names and dates of birth. Some districts, such as the Dallas Independent School District in Texas, report positive cases to the county, but do not publicly specify what information is reported. Many schools, however, do not publicly disclose their collection and sharing of COVID-related student data….(More)”
Paper by Harrison Wilde et al: “Rough sleeping is a chronic experience faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link (HL), a UK-based charity, in developing a data-driven approach to better connect people sleeping rough on the streets with outreach service providers. HL’s platform has grown exponentially in recent years, leading to thousands of alerts per day during extreme weather events; this overwhelms the volunteer-based system they currently rely upon for the processing of alerts. In order to solve this problem, we propose a human-centered machine learning system to augment the volunteers’ efforts by prioritizing alerts based on the likelihood of making a successful connection with a rough sleeper. This addresses capacity and resource limitations whilst allowing HL to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation using historical data shows that our approach increases the rate at which rough sleepers are found following a referral by at least 15% based on labeled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modeling process is done with careful considerations of ethics, transparency, and explainability due to the sensitive nature of the data involved and the vulnerability of the people that are affected….(More)”.
Paper by Westerlund, Mika; Isabelle, Diane A; Leminen, Seppo: “News media and human rights organizations are warning about the rise of the surveillance state that builds on distrust and mass surveillance of its citizens. Further, the global pandemic has fostered public-private collaboration such as the launch of contact tracing apps to tackle COVID-19. Thus, such apps also contribute to the diffusion of technologies that can collect and analyse large amounts of sensitive data and the growth of the surveillance society. This study examines the impacts of citizens’ concerns about digital identity, government’s intelligence activities, and security of the increasing biodata on their trust in and acceptance of government’s use of personal data. Our analysis of survey data from 1,486 Canadians suggest that those concerns have direct effects on people’s acceptance of government’s use of personal data, but not necessarily on the trust in the government being respectful of privacy. Authorities should be more transparent about the collection and uses of data….(More)”
Article by Catherine Cheney: “Trust is a key reason for the wide variance in how countries have fared during the COVID-19 pandemic, determining why some have succeeded in containing the virus while others have failed, according to new research on responses across 23 countries.
The work, supported by Schmidt Futures and the National Science Foundation and carried out by teams at Columbia, Harvard, and Cornell Universities, studied national responses to COVID-19 based on public health, economy, and politics.
It organizes countries into three categories: control, consensus, and chaos. The researchers call the United States the leading example of high levels of polarization, decentralized decision-making, and distrust in expertise leading to policy chaos. The category also includes Brazil, India, Italy, and the United Kingdom.
To prepare for future pandemics, countries must build trust in public health, government institutions, and expert advice, according to a range of speakers at last week’s Futures Forum on Preparedness. Schmidt Futures, which co-hosted the event, announced that it is launching a new challenge to source the best ideas from around the world for developing trust in public health interventions. This request for proposals is likely just the beginning as funders explore how to learn from the pandemic and build trust moving forward….(More)”.
Review Article by Evan Selinger: “Every so often a sound critical thinker and superb writer asks the wrong questions of a wheel-spinning topic like privacy and then draws the wrong conclusions. This is the case with Firmin DeBrabander in his Life After Privacy: Reclaiming Democracy in a Surveillance Society. Professor of Philosophy at the Maryland Institute College of Art, DeBrabander has a gift for clearly expressing complex ideas and explaining why underappreciated moments in the history of ideas have contemporary relevance. In this book, he aims “to understand the prospects and future of democracy without privacy, or very little of it.” That attempt necessarily leads him both to undervalue privacy and make a case for accepting a severely weakened democracy.
To be sure, DeBrabander doesn’t dismiss privacy with any sort of enthusiasm. On the contrary, he loves his privacy, depicting himself as someone who has to block his “beloved” but overly disclosive students on Facebook. “If I had my druthers, my personal data would be sacrosanct,” he writes. But he is convinced privacy is a lost cause. He makes what are essentially six claims about privacy — some of them seemingly obvious and others more startling — to buttress his eulogy.
Prosecuting Privacy
It’s worth listing DeBrabander’s six propositions here before then rebutting or at least complicating their veracity. The first privacy proposition repeats what has become a seeming truism: we’re living in a “confessional culture” that normalizes oversharing. His second has two components, both much discussed in the last several years: companies participating in the “surveillance economy” have an insatiable appetite for our personal information, and consumers don’t fully comprehend just how much value these companies are able to extract from it through data analytics. He emphasizes Charles Duhigg’s much-discussed 2012 New York Times article about Target using predictive analytics on big data to identify pregnant customers and present them with relevant coupons. Consumers, he contends, will continue giving away massive amounts of personal information to make their cars, homes, cities, and even bodies smarter; it’s likely they won’t be any more equipped in the future to assess tradeoffs and determine when they’re being exploited….(More)”.
Written Testimony by Stefaan G. Verhulst before the New York City Council Committee on Technology: “…In crises such as these, calls for the city to harness technology and data to help policy-makers find solutions grow louder and stronger. Many have spoken about accelerating already ongoing work to turn New York into “a smart city” — using digital technology to connect, protect, and improve the lives of its residents. Some of this proposed work could involve the use of sensors to collect data on how people live and work across New York City. Other work could involve expanding the city’s relationships with private organizations through data collaboratives. Data collaboratives, which are central to our work at the GovLab, are a new form of collaboration that extends beyond the conventional public-private partnership model, in which participants from different sectors exchange their data to create public value. The city already operates one such data collaborative in the form of the NYC Recovery Data Partnership, a partnership that allows New York-based private and civic organizations to provide their data to analysts at city agencies to inform the COVID-19 pandemic response. I have the privilege of serving as an advisor to that initiative.
Data collaboration takes place widely through a variety of institutional, contractual and technical structures and instruments. Borrowing in language and inspiration from the open data movement, the emerging data collaborative movement has proven its value and possible positive impact. Data reuse has the potential to improve disease treatment, identify better ways to source supplies, monitor adherence to non-pharmaceutical restrictions, and provide a range of other public benefits. Whether it is informing decision-making or shaping the development of new tools and techniques, it is clear that data has tremendous potential to mitigate the worst effects of this pandemic.
However, as promising and attractive as reusing data might seem, it is important to keep in mind that there also exist widespread concerns and challenges….
My colleagues and I at The GovLab believe the Data Assembly methodology offers the city a new way forward on the issues under discussion today, as they relate to smart cities. In our view, oversight cannot just be a reactive process of responding to complaints but a proactive one, inviting city residents, data holders, and advocacy groups to the table to determine what is and is not acceptable. Amid rapidly changing circumstances, the city needs ways to collect and synthesize actionable and diverse public input to identify concerns, expectations, and opportunities. We encourage the city to explore assembling mini-publics of its own or, failing that, commission legitimate partners to lead such efforts.
New York faces many challenges in 2021 but I do not doubt the capacity of its people to overcome these struggles. Through people-led innovation and processes, the city can ensure that data re-use conducted as part of the smart city is deemed legitimate and more effective and targeted. It can also support the city in ensuring work across the city is more open, collaborative, and legitimate…(More)”.
Book by on “How Tech Companies Are Profiling Us from before Birth…Our families are being turned into data, as the digital traces we leave are shared, sold, and commodified. Children are datafied even before birth, with pregnancy apps and social media postings, and then tracked through childhood with learning apps, smart home devices, and medical records. In Child Data Citizen, Veronica Barassi examines the construction of children into data subjects, describing how their personal information is collected, archived, sold, and aggregated into unique profiles that can follow them across a lifetime. Children today are the very first generation of citizens to be datafied from before birth, and Barassi points to critical implications for our democratic futures.
Barassi draws on a three-year research project with parents in London and Los Angeles, which included the collection of fifty in-depth interviews, a digital ethnography of “sharenting” activities on social media by eight families over the course of eight months, and a two-year exploration of the datafication of her own family. She complements her ethnographic findings with a platform analysis of four social media platforms, ten health tracking apps, four home hubs, and four educational platforms, investigating the privacy policies, business models, and patent applications that enable the mining of children’s data. Barassi considers the implications of building a society where data traces are made to speak for and about citizens across a lifetime. What should we do when we realize that the narratives that algorithms construct about individuals are inaccurate and biased?…(More)”.