Stefaan Verhulst
Kashmir Hill at the New York Times: “In what may be the first known case of its kind, a faulty facial recognition match led to a Michigan man’s arrest for a crime he did not commit….
The Shinola shoplifting occurred in October 2018. Katherine Johnston, an investigator at Mackinac Partners, a loss prevention firm, reviewed the store’s surveillance video and sent a copy to the Detroit police, according to their report.
Five months later, in March 2019, Jennifer Coulson, a digital image examiner for the Michigan State Police, uploaded a “probe image” — a still from the video, showing the man in the Cardinals cap — to the state’s facial recognition database. The system would have mapped the man’s face and searched for similar ones in a collection of 49 million photos.
The state’s technology is supplied for $5.5 million by a company called DataWorks Plus. Founded in South Carolina in 2000, the company first offered mug shot management software, said Todd Pastorini, a general manager. In 2005, the firm began to expand the product, adding face recognition tools developed by outside vendors.
When one of these subcontractors develops an algorithm for recognizing faces, DataWorks attempts to judge its effectiveness by running searches using low-quality images of individuals it knows are present in a system. “We’ve tested a lot of garbage out there,” Mr. Pastorini said. These checks, he added, are not “scientific” — DataWorks does not formally measure the systems’ accuracy or bias.
“We’ve become a pseudo-expert in the technology,” Mr. Pastorini said.
In Michigan, the DataWorks software used by the state police incorporates components developed by the Japanese tech giant NEC and by Rank One Computing, based in Colorado, according to Mr. Pastorini and a state police spokeswoman. In 2019, algorithms from both companies were included in a federal study of over 100 facial recognition systems that found they were biased, falsely identifying African-American and Asian faces 10 times to 100 times more than Caucasian faces….(More)“.
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)“
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)”.
Book edited by Alain Samson. Introduction by Colin Camerer: “The goal of science is to accumulate knowledge, full stop. In my opinion, there is a lot of leakage in how we currently do this. The reproducibility “upgrade” (a term I prefer to “crisis”) going on in many areas of science is an example of trying to minimize leakage. Solid accumulation depends on not getting led too far or frequently astray by false positives which do not reproduce. A good infrastructure for rapidly evaluating and cumulating results is of special use for “hurry-up” social science. For example, as I write this there are probably hundreds of social science studies being done about COVID-19. It is essentially impossible for all those scientists to know what the other scientists are doing. There will be duplication and poorly designed
studies. (It is often said in design that everyone wants cheap, fast, and good. But you can only have two.)
When studies are written and circulated in preprints, a lot of null effects won’t be written up. Which studies will get the most attention? It will be a scrum of social media, presenting at seminars, slow and fast reviewing paces. The one thing that would undoubtedly be most useful—a giant dashboard summarizing weekly progress on each of those hundreds of studies—does not exist. This is a failure of good informatics.
Behavioral economics is accumulating knowledge about how different kinds of nudges influence behavior at a rapid pace. The challenge is that carefully assessing what an entire body of knowledge is telling us is actually quite difficult and is under-rewarded (by academic incentives). A lot of academic publishing, and similar career concerns within government or NGOs, depend on creativity and doing something new. This creates an incentive to exaggerate the novelty of one’s contribution compared to what is known from past studies….(More)”.
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)”
Essay by Paul Waller: “This essay aims to analyse and debunk several technology-related concepts commonly discussed in papers, reports and speeches by academics, consultancies, politicians and governmental bodies. Each reflects a presumption about how technology, the internet in particular, and technology-enabled social and political processes might affect the practice of governing. The discussion characterizes the concepts as “socio-technical imaginaries”, a term for ideas that link the socio-political environment with technology. Socio-technical imaginaries start as a description of potentially attainable futures, turn into a prescription of futures that ought to be attained, then become received wisdom about the present day. They are speculation that takes root through reuse and endorsement by authoritative figures, becoming an asserted present reality on the basis of little or no evidence. Once imaginaries become widely accepted and used, they may shape trajectories of research and innovation, steering technological progress as well as public and private expenditure. The imaginaries addressed are: Public Sector Innovation, Digital Transformation of Government, Co-creation & Co-production of Public Services, Crowd-sourcing, Wisdom of Crowds, Collaborative Governance, Customer/Citizen Centricity, Once-only Principle, Personalisation, Big Data, Nudge (Behavioral Insights), Platform Government/GaaP, and Online Participation.
Four questions are posed to critique each imaginary: What is the received wisdom? What does that really mean? What is the problem/what has gone wrong? What to do better/what should it be? As a whole package, these imaginaries represent a nightmare for liberal, representative democracy. Some may enable the “panoptic” state, others may undermine existing institutions to open a void for it to step into. Many have the likelihood of creating or reinforcing inequality of opportunity, outcome or influence. But their grip is hard to loosen. The notions that they are inevitable or that issues will be resolved in due course by technology itself need to be challenged by surfacing the human, social and political dimensions and actively addressing them….(More)”.
Paper by Dimas Budi Prasetyo: “It is widely explored that problems in developing society related to think and act logically and reflectively in a social context positively correlates with the cognition skill. In most developing societies, people are busy with problems that they face daily (i.e. working overtime), limits their cognitive capacity to properly process a social stimulus, which mostly asked their thoughtful response. Thus, a better design in social stimulus to tackle problematic behaviour, such as littering, to name a few, becomes more prominent. During the last decade, nudge has been famous for its subtle approach for behaviour change – however, there is relatively little known of the method applied in the developing society. The current article reviews the nudge approach to change human behaviour from two perspectives: cognitive science and consumer psychology. The article concludes that intervention using the nudge approach could be beneficial for current problematic behaviour…(More)”.
Paper by Kevin Werbach: “Technology scholars, policy-makers, and executives in Europe and the United States disagree violently about what the digitally connected world should look like. They agree on what it shouldn’t: the Orwellian panopticon of China’s Social Credit System (SCS). SCS is a government-led initiative to promote data-driven compliance with law and social values, using databases, analytics, blacklists, and software applications. In the West, it is widely viewed as a diabolical effort to crush any spark of resistance to the dictates of the Chinese Communist Party (CCP) and its corporate emissaries. This picture is, if not wholly incorrect, decidedly incomplete. SCS is the world’s most advanced prototype of a regime of algorithmic regulation. It is a sophisticated and comprehensive effort not only to expand algorithmic control, but also to restrain it. Understanding China’s system is crucial for resolving the great challenges we face in the emerging era of relentless data aggregation, ubiquitous analytics, and algorithmic control….(More)”.
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)”.
Article by Barry Schwartz: “How did we get from that day to this one, with widespread smoking bans in public places? The answer, I believe, was the discovery of the effects of secondhand smoke. When I smoked, it harmed innocent bystanders. It harmed children, including my own. The research on secondhand smoke began in the 1960s, showing negative effects on lab animals. As the work continued, it left no doubt that secondhand smoke contributes to asthma, cardiovascular disease, many types of cancer, stroke, cognitive impairment, and countless other maladies. These sorts of findings empowered people to demand, not request, that others put out their cigarettes. The secondhand smoke research led eventually to all the regulation that we now take for granted.
Why did this research change public attitudes and change them so fast—in a single generation? The answer, I think, is that research on secondhand smoke took an individual (perhaps foolish) choice and moralized it, by emphasizing its effects on others. It was no longer simply dumb to smoke; it was immoral. And that changed everything.
Psychologist Paul Rozin has studied the process of moralization. When activities get moralized, they move from being matters of individual discretion to being matters of obligation. Smoking went from being an individual consumer decision to being a transgression. And the process of moralization can go in the other direction, as we have seen, for most people, in the case of sexuality. In recent years, homosexuality has been “demoralized,” and moral sanctions against it have slowly been melting away….(More)”.