How to Put Out Democracy’s Dumpster Fire

Curated on Posted on by Stefaan Verhulst

Yoshi Sodeoka in The Atlantic: “…With the wholesale transfer of so much entertainment, social interaction, education, commerce, and politics from the real world to the virtual world—a process recently accelerated by the coronavirus pandemic—many Americans have come to live in a nightmarish inversion of the Tocquevillian dream, a new sort of wilderness. Many modern Americans now seek camaraderie online, in a world defined not by friendship but by anomie and alienation. Instead of participating in civic organizations that give them a sense of community as well as practical experience in tolerance and consensus-building, Americans join internet mobs, in which they are submerged in the logic of the crowd, clicking Like or Share and then moving on. Instead of entering a real-life public square, they drift anonymously into digital spaces where they rarely meet opponents; when they do, it is only to vilify them.

Conversation in this new American public sphere is governed not by established customs and traditions in service of democracy but by rules set by a few for-profit companies in service of their needs and revenues. Instead of the procedural regulations that guide a real-life town meeting, conversation is ruled by algorithms that are designed to capture attention, harvest data, and sell advertising. The voices of the angriest, most emotional, most divisive—and often the most duplicitous—participants are amplified. Reasonable, rational, and nuanced voices are much harder to hear; radicalization spreads quickly. Americans feel powerless because they are.

In this new wilderness, democracy is becoming impossible. If one half of the country can’t hear the other, then Americans can no longer have shared institutions, apolitical courts, a professional civil service, or a bipartisan foreign policy. We can’t compromise. We can’t make collective decisions—we can’t even agree on what we’re deciding. No wonder millions of Americans refuse to accept the results of the most recent presidential election, despite the verdicts of state electoral committees, elected Republican officials, courts, and Congress. We no longer are the America Tocqueville admired, but have become the enfeebled democracy he feared, a place where each person,…(More)”.

How One State Managed to Actually Write Rules on Facial Recognition

Curated on Posted on by Stefaan Verhulst

Kashmir Hill at The New York Times: “Though police have been using facial recognition technology for the last two decades to try to identify unknown people in their investigations, the practice of putting the majority of Americans into a perpetual photo lineup has gotten surprisingly little attention from lawmakers and regulators. Until now.

Lawmakers, civil liberties advocates and police chiefs have debated whether and how to use the technology because of concerns about both privacy and accuracy. But figuring out how to regulate it is tricky. So far, that has meant an all-or-nothing approach. City Councils in Oakland, Portland, San Francisco, Minneapolis and elsewhere have banned police use of the technology, largely because of bias in how it works. Studies in recent years by MIT researchers and the federal government found that many facial recognition algorithms are most accurate for white men, but less so for everyone else.

At the same time, automated facial recognition has become a powerful investigative tool, helping to identify child molesters and, in a recent high-profile example, people who participated in the Jan. 6 riot at the Capitol. Law enforcement officials in Vermont want the state’s ban lifted because there “could be hundreds of kids waiting to be saved.”

That’s why a new law in Massachusetts is so interesting: It’s not all or nothing. The state managed to strike a balance on regulating the technology, allowing law enforcement to harness the benefits of the tool, while building in protections that might prevent the false arrests that have happened before….(More)”.

Why Transparency Won’t Save Us

Curated on Posted on by Stefaan Verhulst

Essay by Sun-ha Hong: “In a society beset with black-boxed algorithms and vast surveillance systems, transparency is often hailed as liberal democracy’s superhero. It’s a familiar story: inject the public with information to digest, then await their rational deliberation and improved decision making. Whether in discussions of facial recognition software or platform moderation, we run into the argument that transparency will correct the harmful effects of algorithmic systems. The trouble is that in our movies and comic books, superheroes are themselves deus ex machina: black boxes designed to make complex problems disappear so that the good guys can win. Too often, transparency is asked to save the day on its own, under the assumption that disinformation or abuse of power can be shamed away with information.

Transparency without adequate support, however, can quickly become fuel for speculation and misunderstanding….

All this is part of a broader pattern in which the very groups who should be held accountable by the data tend to be its gatekeepers. Facebook is notorious for transparency-washing strategies, in which it dangles data access like a carrot but rarely follows through in actually delivering it. When researchers worked to create more independent means of holding Facebook accountable — as New York University’s Ad Observatory did last year, using volunteer researchers to build a public database of ads on the platform — Facebook threatened to sue them. Despite the lofty rhetoric around Facebook’s Oversight Board (often described as a “Supreme Court” for the platform), it falls into the same trap of transparency without power: the scope is limited to individual cases of content moderation, with no binding authority over the company’s business strategy, algorithmic design, or even similar moderation cases in the future.

Here, too, the real bottleneck is not information or technology, but power: the legal, political and economic pressure necessary to compel companies like Facebook to produce information and to act on it. We see this all too clearly when ordinary people do take up this labour of transparency, and attempt to hold technological systems accountable. In August 2020, Facebook users reported the Kenosha Guard group more than 400 times for incitement of violence. But Facebook declined to take any action until an armed shooter travelled to Kenosha, Wisconsin, and killed two protesters. When transparency is compromised by the concentration of power, it is often the vulnerable who are asked to make up the difference — and then to pay the price.

Transparency cannot solve our problems on its own. In his book The Rise of the Right to Know, journalism scholar Michael Schudson argues that transparency is better understood as a “secondary or procedural morality”: a tool that only becomes effective by other means. We must move beyond the pernicious myth of transparency as a universal solution, and address the distribution of economic and political power that is the root cause of technologically amplified irrationality and injustice….(More)”.

Balancing Privacy With Data Sharing for the Public Good

Curated on Posted on by Stefaan Verhulst

David Deming at the New York Times: “Governments and technology companies are increasingly collecting vast amounts of personal data, prompting new laws, myriad investigations and calls for stricter regulation to protect individual privacy.

Yet despite these issues, economics tells us that society needs more data sharing rather than less, because the benefits of publicly available data often outweigh the costs. Public access to sensitive health records sped up the development of lifesaving medical treatments like the messenger-RNA coronavirus vaccines produced by Moderna and Pfizer. Better economic data could vastly improve policy responses to the next crisis.

Data increasingly powers innovation, and it needs to be used for the public good, while individual privacy is protected. This is new and unfamiliar terrain for policymaking, and it requires a careful approach.

The pandemic has brought the increasing dominance of big, data-gobbling tech companies into sharp focus. From online retail to home entertainment, digitally savvy businesses are collecting data and deploying it to anticipate product demand and set prices, lowering costs and outwitting more traditional competitors.

Data provides a record of what has already happened, but its main value comes from improving predictions. Companies like Amazon choose products and prices based on what you — and others like you — bought in the past. Your data improves their decision-making, boosting corporate profits.

Private companies also depend on public data to power their businesses. Redfin and Zillow disrupted the real estate industry thanks to access to public property databases. Investment banks and consulting firms make economic forecasts and sell insights to clients using unemployment and earnings data collected by the Department of Labor. By 2013, one study estimated, public data contributed at least $3 trillion per year to seven sectors of the economy worldwide.

The buzzy refrain of the digital age is that “data is the new oil,” but this metaphor is inaccurate. Data is indeed the fuel of the information economy, but it is more like solar energy than oil — a renewable resource that can benefit everyone at once, without being diminished….(More)”.

Regulation of Algorithmic Tools in the United States

Curated on Posted on by Stefaan Verhulst

Paper by Christopher S. Yoo and Alicia Lai: “Policymakers in the United States have just begun to address regulation of artificial intelligence technologies in recent years, gaining momentum through calls for additional research funding, piece-meal guidance, proposals, and legislation at all levels of government. This Article provides an overview of high-level federal initiatives for general artificial intelligence (AI) applications set forth by the U.S. president and responding agencies, early indications from the incoming Biden Administration, targeted federal initiatives for sector-specific AI applications, pending federal legislative proposals, and state and local initiatives. The regulation of the algorithmic ecosystem will continue to evolve as the United States continues to search for the right balance between ensuring public safety and transparency and promoting innovation and competitiveness on the global stage….(More)”.

Reddit Is America’s Unofficial Unemployment Hotline

Curated on Posted on by Stefaan Verhulst

Ella Koeze at The New York Times: “In early December, Alex Branch’s car broke down. A 23-year-old former arcade employee in southern Virginia, Mr. Branch had been receiving unemployment benefits since he was laid off in March, and figured he would have no problem paying for the repairs. But when he checked his bank account, he was troubled to find that the payments had stopped.

He had failed to get useful information from his state’s unemployment office before, so he turned to the one place he figured he could get an explanation: Reddit.

“I’m very confused and have no idea what to do,” Mr. Branch wrote on r/Unemployment, a Reddit forum whose popularity has skyrocketed during the pandemic.

The next day, another user commented on Mr. Branch’s post, using a common abbreviation for Extended Benefits, an emergency unemployment program. “Were you on EB? If so, EB was cut off Nov 21.”

Mr. Branch hadn’t realized he had been on Extended Benefits, which kicked in after he exhausted 26 weeks of regular unemployment plus 13 additional weeks granted in the March pandemic stimulus bill. Virginia stopped payments because the state’s unemployment rate had fallen under 5 percent, triggering an end to federal funding for the Extended Benefits program.

“I didn’t know about it,” he said in an interview. “That’s the biggest frustration that I had about it was the fact that I never received the email that it was going to be shut off.”

For many of the millions of Americans like Mr. Branch who lost jobs because of the coronavirus, the stress of being unemployed in a pandemic has been compounded by the difficulty of navigating disorganized and often antiquated state and federal unemployment systems. Information from overwhelmed state offices and websites is often confusing, and reaching an official who can answer questions nearly impossible….

Post after post on r/Unemployment conveys bureaucratic problems with endless variations: how to file a claim depending on your circumstances, what to do if you made a mistake on your claim, what different statuses on your claim might mean, how to navigate confusing and glitch-prone online portals and even how to speak to an actual person to get issues resolved….

Many people come to r/Unemployment to offer answers, not just find them.

Albert Peers, who had been working in a call center in San Diego until the pandemic, spends time every day trying to answer questions about California’s system. He lives alone and can’t easily return to work because he has a lowered immune system. After first visiting the site when he encountered a hitch in his own unemployment benefits, Mr. Peers, 56, was shocked by the number of people who had no idea what to do.

The thought that someone might go hungry or miss rent because they were simply stymied by the system was unacceptable to him. “At that point I just made a decision,” he said. “You know what, like a couple hours every day, because I just can’t turn away.”…(More)”.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Book by Kate Crawford: “What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind “automated” services, to the data AI collects from us. 

Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world…(More)”.

Governance for Innovation and Privacy: The Promise of Data Trusts and Regulatory Sandboxes

Curated on Posted on by Stefaan Verhulst

Essay by Chantal Bernier: “Innovation feeds on data, both personal, identified data and de-identified data. To protect the data from increasing privacy risks, governance structures emerge to allow the use and sharing of data as necessary for innovation while addressing privacy risks. Two frameworks proposed to fulfill this purpose are data trusts and regulatory sandboxes.

The Government of Canada introduced the concept of “data trust” into the Canadian privacy law modernization discussion through Canada’s Digital Charter in Action: A Plan by Canadians, for Canadians, to “enable responsible innovation.” At a high level, a data trust may be defined, according to the Open Data Institute, as a legal structure that is appropriate to the data sharing it is meant to govern and that provides independent stewardship of data.

Bill C-11, known as the Digital Charter Implementation Act, 2020, and tabled on November 17, 2020, lays the groundwork for the possibility of creating data trusts for private organizations to disclose de-identified data to specific public institutions for “socially beneficial purposes.” In her recent article “Replacing Canada’s 20-Year-Old Data Protection Law,” Teresa Scassa provides a superb overview and analysis of the bill.

Another instrument for privacy protective innovation is referred to as the “regulatory sandbox.” The United Kingdom’s Information Commissioner’s Office (ICO) provides a regulatory sandbox service that encourages organizations to submit innovative initiatives without fear of enforcement action. From there, the ICO sandbox team provides advice related to privacy risks and how to embed privacy protection.

Both governance measures may hold the future of privacy and innovation, provided that we accept this equation: De-identified data may no longer be considered irrevocably anonymous and therefore should not be released unconditionally, but the risk of re-identification is so remote that the data may be released under a governance structure that mitigates the residual privacy risk.  

Innovation Needs Identified Personal Data and De-identified Data   

The role of data in innovation does not need to be explained. Innovation requires a full understanding of what is, to project toward what could be. The need for personal data, however, calls for far more than an explanation. Its use must be justified. Applications abound, and they may not be obvious to the layperson. Researchers and statisticians, however, underline the critical role of personal data with one word: reliability.

Processing data that can be traced, either through identifiers or through pseudonyms, allows superior machine learning, longitudinal studies and essential correlations, which provide, in turn, better data in which to ground innovation. Statistics Canada has developed a “Continuum of Microdata Access” to its databases on the premise that “researchers require access to microdata at the individual business, household or person level for research purposes. To preserve the privacy and confidentiality of respondents, and to encourage the use of microdata, Statistics Canada offers a wide range of options through a series of online channels, facilities and programs.”

Since the first national census in 1871, Canada has put data — derived from personal data collected through the census and surveys — to good use in the public and private sectors alike. Now, new privacy risks emerge, as the unprecedented volume of data collection and the power of analytics bring into question the notion that the de-identification of data — and therefore its anonymization — is irreversible.

And yet, data to inform innovation for the good of humanity cannot exclude data about humans. So, we must look to governance measures to release de-identified data for innovation in a privacy-protective manner. …(More)”.

N.Y.’s Vaccine Websites Weren’t Working. He Built a New One for $50.

Curated on Posted on by Stefaan Verhulst

Sharon Otterman at New York Times: “Huge Ma, a 31-year-old software engineer for Airbnb, was stunned when he tried to make a coronavirus vaccine appointment for his mother in early January and saw that there were dozens of websites to check, each with its own sign-up protocol. The city and state appointment systems were completely distinct.

“There has to be a better way,” he said he remembered thinking.

So, he developed one. In less than two weeks, he launched TurboVax, a free website that compiles availability from the three main city and state New York vaccine systems and sends the information in real time to Twitter. It cost Mr. Ma less than $50 to build, yet it offers an easier way to spot appointments than the city and state’s official systems do.

“It’s sort of become a challenge to myself, to prove what one person with time and a little motivation can do,” he said last week. “This wasn’t a priority for governments, which was unfortunate. But everyone has a role to play in the pandemic, and I’m just doing the very little that I can to make it a little bit easier.”

Supply shortages and problems with access to vaccination appointments have been some of the barriers to the equitable distribution of the vaccine in New York City and across the United States, officials have acknowledged….(More)”.

Public Policy Analytics: Code & Context for Data Science in Government

Curated on Posted on by Stefaan Verhulst

Open Access Book by Ken Steif: “… teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government….(More)”.