Why the deliberative democracy framework doesn’t quite work for me


Essay by Peter Levine: “In some ways, I came of age in the field of deliberative democracy. I had an internship at the Kettering Foundation when I was a college sophomore (when the foundation defined itself more purely in deliberative terms than it does today). By that time, I had already taken a philosophy seminar on the great deliberative theorist Jürgen Habermas. In the three decades since then, I’ve served on the boards of Kettering, Everyday Democracy, and AmericaSPEAKS. I wrote a book with “deliberative democracy” in its subtitle and co-edited The Deliberative Democracy Handbook with John Gastil. I was one of many co-founders of the Deliberative Democracy Consortium and have served on its steering committee since the last century.

None of these groups is committed to deliberation in a narrow sense (although opinions differ within the field). For me, these are the main limitations of focusing on deliberation as the central topic or unit of analysis:

Deliberative values are worthy ones, but they are not the only worthy ones. My own values would also include personal liberties and nonnegotiable rights, concerns for nature, and virtues of the inner life, such as equanimity and personal development. Stating my values doesn’t substitute for an argument, but it may suffice to make the point that deliberation is not the only good thing, and it’s in tension with other goods. A deliberative democrat will reply that I should discuss my values with other people. And so I should–but that doesn’t mean that the norms intrinsic to deliberation trump all other norms. Nor are fellow citizens the only sources of guidance; introspecting, reading ancient texts, consulting legal precedents, and conducting scientific experiments are helpful, too.

By the same token, deliberative virtues are not the only civic virtues. Deliberation is about discourse–talking and listening–so its virtues are discursive ones: humility and openness, empathy, sincerity, and perhaps eloquence. (The list is contested.) But a good citizen may be hard-working, physically courageous, or aesthetically creative instead of especially good at deliberating. The people who physically built the Athenian agora were as important as the people who exchanged ideas in it.

Deliberation depends on social organization. In order for people to have something that’s worth discussing, they must already make, control, or influence things of value together. That requires social organization, whether in the form of a market, a commons, a voluntary association, a functional network, or a political institution. Discussion rarely precedes these forms, because people can’t and won’t come together in completely amorphous groupings. Discussion is more typically a moment in an ongoing process of governance. Often a small group of founders chooses the rules-in-use that create a group in which deliberation can occur.

Thus we should ask about leadership and rules, not just about deliberation….(More)”.

Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor


Book by Virginia Eubanks: “The State of Indiana denies one million applications for healthcare, foodstamps and cash benefits in three years—because a new computer system interprets any mistake as “failure to cooperate.” In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for an inadequate pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.

Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.

In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.

The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values….(More)”.

“Crowdsourcing” ten years in: A review


Kerri Wazny at the Journal of Global Health: “First coined by Howe in 2006, the field of crowdsourcing has grown exponentially. Despite its growth and its transcendence across many fields, the definition of crowdsourcing has still not been agreed upon, and examples are poorly indexed in peer–reviewed literature. Many examples of crowdsourcing have not been scaled–up past the pilot phase.

In spite of this, crowdsourcing has great potential, especially in global health where resources are lacking. This narrative review seeks to review both indexed and grey crowdsourcing literature broadly in order to explore the current state of the field….(More)”.

Visualizing the Uncertainty in Data


Nathan Yau at FlowingData: “Data is a representation of real life. It’s an abstraction, and it’s impossible to encapsulate everything in a spreadsheet, which leads to uncertainty in the numbers.

How well does a sample represent a full population? How likely is it that a dataset represents the truth? How much do you trust the numbers?

Statistics is a game where you figure out these uncertainties and make estimated judgements based on your calculations. But standard errors, confidence intervals, and likelihoods often lose their visual space in data graphics, which leads to judgements based on simplified summaries expressed as means, medians, or extremes.

That’s no good. You miss out on the interesting stuff. The important stuff. So here are some visualization options for the uncertainties in your data, each with its pros, cons, and examples….(More)”.

AI System Sorts News Articles By Whether or Not They Contain Actual Information


Michael Byrne at Motherboard:”… in a larger sense it’s worth wondering to what degree the larger news feed is being diluted by news stories that are not “content dense.” That is, what’s the real ratio between signal and noise, objectively speaking? To start, we’d need a reasonably objective metric of content density and a reasonably objective mechanism for evaluating news stories in terms of that metric.

In a recent paper published in the Journal of Artificial Intelligence Research, computer scientists Ani Nenkova and Yinfei Yang, of Google and the University of Pennsylvania, respectively, describe a new machine learning approach to classifying written journalism according to a formalized idea of “content density.” With an average accuracy of around 80 percent, their system was able to accurately classify news stories across a wide range of domains, spanning from international relations and business to sports and science journalism, when evaluated against a ground truth dataset of already correctly classified news articles.

At a high level this works like most any other machine learning system. Start with a big batch of data—news articles, in this case—and then give each item an annotation saying whether or not that item falls within a particular category. In particular, the study focused on article leads, the first paragraph or two in a story traditionally intended to summarize its contents and engage the reader. Articles were drawn from an existing New York Times linguistic dataset consisting of original articles combined with metadata and short informative summaries written by researchers….(More)”.

Universities must prepare for a technology-enabled future


 in the Conversation: “Automation and artificial intelligence technologies are transforming manufacturingcorporate work and the retail business, providing new opportunities for companies to explore and posing major threats to those that don’t adapt to the times. Equally daunting challenges confront colleges and universities, but they’ve been slower to acknowledge them.

At present, colleges and universities are most worried about competition from schools or training systems using online learning technology. But that is just one aspect of the technological changes already under way. For example, some companies are moving toward requiring workers have specific skills trainings and certifications – as opposed to college degrees.

As a professor who researches artificial intelligence and offers distance learning courses, I can say that online education is a disruptive challenge for which colleges are ill-prepared. Lack of student demand is already closing 800 out of roughly 10,000 engineering colleges in India. And online learning has put as many as half the colleges and universities in the U.S. at risk of shutting down in the next couple decades as remote students get comparable educations over the internet – without living on campus or taking classes in person. Unless universities move quickly to transform themselves into educational institutions for a technology-assisted future, they risk becoming obsolete….(More)”

Letters From Congress


From-Congress is an attempt to collect letters sent by representatives to their constituents. These letters often contain statements by the rep about positions that might otherwise be difficult to discover.

This project exists to increase the amount of transparency and accountability of representatives in their districts….

If you would like to send a letter to your congress person, I would heavily recommend resistbot. If you receive a reply, please consider uploading the reply here.

If in the past year you’ve received a correspondence from your congress person, I would encourage you to upload them as well.

In the future, we would like to transcribe these letters (hopefully automatically) and put the text in each article. Along with providing accessibility for visually-impaired readers, this will also allow searching of politicians’ view points….(More)”

See also: Project Legisletters

Can This App That Lets You Sell Your Health Data Cut Your Health Costs?


Ben Schiller at FastCompany: “Americans could do with new ways to save on healthcare….

CoverUS, a startup, has one idea: monetizing our health-related data. Through a new blockchain-based data marketplace, it hopes to generate revenue that could effectively make insurance cheaper and perhaps even encourage us to become healthier, thus cutting the cost of the system overall.

It works like this: When you sign up, you download a digital wallet to your phone. Then you populate that wallet with data from an electronic health record (EHR), for which, starting in January 2018, system operators are legally obliged to offer an open API. At the same time, you can also allow wearables and other health trackers to automatically add data to the platform, and answer questions about your health and consumption habits.

A prototype of the app. [Screenshots: CoverUS]

Why bother? To create a richer picture of your health than is currently held by the EHR systems, health providers, and data brokerages that buy and sell data from doctors, clinics, pharmacies, and other sources. By collecting all our data in one place, CoverUS wants to give us more autonomy over who uses our personal information and who makes money from it….

CoverUS, which plans to launch in the first quarter of 2018, will pay for the data we gather in the form of a fixed-price cryptocurrency called CoverCoin. Users generate coins by signing up and then sharing their data. The startup then hopes users will be able to spend the coins on services that improve their health (like gym memberships) or deposit them in a health savings account where the coins can be exchanged for insurance plan savings. Sealey says it’s in discussions with several providers…(More)”.

Social Theory After the Internet: Media, Technology and Globalization


(Open Access) Book by Ralph Schroeder: “The internet has fundamentally transformed society in the past 25 years, yet existing theories of mass or interpersonal communication do not work well in understanding a digital world. Nor has this understanding been helped by disciplinary specialization and a continual focus on the latest innovations. Ralph Schroeder takes a longer-term view, synthesizing perspectives and findings from various social science disciplines in four countries: the United States, Sweden, India and China. His comparison highlights, among other observations, that smartphones are in many respects more important than PC-based internet uses.

Social Theory after the Internet focuses on everyday uses and effects of the internet, including information seeking and big data, and explains how the internet has gone beyond traditional media in, for example, enabling Donald Trump and Narendra Modi to come to power. Schroeder puts forward a sophisticated theory of the role internet plays, and how both technological and social forces shape its significance. He provides a sweeping and penetrating study, theoretically ambitious and at the same time always empirically grounded….(More)”.

A.I. and Big Data Could Power a New War on Poverty


Elisabeth A. Mason in The New York Times: “When it comes to artificial intelligence and jobs, the prognostications are grim. The conventional wisdom is that A.I. might soon put millions of people out of work — that it stands poised to do to clerical and white collar workers over the next two decades what mechanization did to factory workers over the past two. And that is to say nothing of the truckers and taxi drivers who will find themselves unemployed or underemployed as self-driving cars take over our roads.

But it’s time we start thinking about A.I.’s potential benefits for society as well as its drawbacks. The big-data and A.I. revolutions could also help fight poverty and promote economic stability.

Poverty, of course, is a multifaceted phenomenon. But the condition of poverty often entails one or more of these realities: a lack of income (joblessness); a lack of preparedness (education); and a dependency on government services (welfare). A.I. can address all three.

First, even as A.I. threatens to put people out of work, it can simultaneously be used to match them to good middle-class jobs that are going unfilled. Today there are millions of such jobs in the United States. This is precisely the kind of matching problem at which A.I. excels. Likewise, A.I. can predict where the job openings of tomorrow will lie, and which skills and training will be needed for them….

Second, we can bring what is known as differentiated education — based on the idea that students master skills in different ways and at different speeds — to every student in the country. A 2013 study by the National Institutes of Health found that nearly 40 percent of medical students held a strong preference for one mode of learning: Some were listeners; others were visual learners; still others learned best by doing….

Third, a concerted effort to drag education and job training and matching into the 21st century ought to remove the reliance of a substantial portion of the population on government programs designed to assist struggling Americans. With 21st-century technology, we could plausibly reduce the use of government assistance services to levels where they serve the function for which they were originally intended…(More)”.