What factors influence transparency in US local government?

Curated on Posted on by Stefaan Verhulst

Grichawat Lowatcharin and Charles Menifield at LSE Impact Blog: “The Internet has opened a new arena for interaction between governments and citizens, as it not only provides more efficient and cooperative ways of interacting, but also more efficient service delivery, and more efficient transaction activities. …But to what extent does increased Internet access lead to higher levels of government transparency? …While we found Internet access to be a significant predictor of Internet-enabled transparency in our simplest model, this finding did not hold true in our most extensive model. This does not negate that fact that the variable is an important factor in assessing transparency levels and Internet access. …. Our data shows that total land area, population density, percentage of minority, education attainment, and the council-manager form of government are statistically significant predictors of Internet-enabled transparency.  These findings both confirm and negate the findings of previous researchers. For example, while the effect of education on transparency appears to be the most consistent finding in previous research, we also noted that the rural/urban (population density) dichotomy and the education variable are important factors in assessing transparency levels. Hence, as governments create strategic plans that include growth models, they should not only consider the budgetary ramifications of growth, but also the fact that educated residents want more web based interaction with government. This finding was reinforced by a recent Census Bureau report indicating that some of the cities and counties in Florida and California had population increases greater than ten thousand persons per month during the period 2013-2014.

This article is based on the paper ‘Determinants of Internet-enabled Transparency at the Local Level: A Study of Midwestern County Web Sites’, in State and Local Government Review. (More)”

Mining Administrative Data to Spur Urban Revitalization

Curated on Posted on by Stefaan Verhulst

New paper by Ben Green presented at the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: “After decades of urban investment dominated by sprawl and outward growth, municipal governments in the United States are responsible for the upkeep of urban neighborhoods that have not received sufficient resources or maintenance in many years. One of city governments’ biggest challenges is to revitalize decaying neighborhoods given only limited resources. In this paper, we apply data science techniques to administrative data to help the City of Memphis, Tennessee improve distressed neighborhoods. We develop new methods to efficiently identify homes in need of rehabilitation and to predict the impacts of potential investments on neighborhoods. Our analyses allow Memphis to design neighborhood-improvement strategies that generate greater impacts on communities. Since our work uses data that most US cities already collect, our models and methods are highly portable and inexpensive to implement. We also discuss the challenges we encountered while analyzing government data and deploying our tools, and highlight important steps to improve future data-driven efforts in urban policy….(More)”

Push, Pull, and Spill: A Transdisciplinary Case Study in Municipal Open Government

Curated on Posted on by Stefaan Verhulst

New paper by Jan Whittington et al: “Cities hold considerable information, including details about the daily lives of residents and employees, maps of critical infrastructure, and records of the officials’ internal deliberations. Cities are beginning to realize that this data has economic and other value: If done wisely, the responsible release of city information can also release greater efficiency and innovation in the public and private sector. New services are cropping up that leverage open city data to great effect.

Meanwhile, activist groups and individual residents are placing increasing pressure on state and local government to be more transparent and accountable, even as others sound an alarm over the privacy issues that inevitably attend greater data promiscuity. This takes the form of political pressure to release more information, as well as increased requests for information under the many public records acts across the country.

The result of these forces is that cities are beginning to open their data as never before. It turns out there is surprisingly little research to date into the important and growing area of municipal open data. This article is among the first sustained, cross-disciplinary assessments of an open municipal government system. We are a team of researchers in law, computer science, information science, and urban studies. We have worked hand-in-hand with the City of Seattle, Washington for the better part of a year to understand its current procedures from each disciplinary perspective. Based on this empirical work, we generate a set of recommendations to help the city manage risk latent in opening its data….(More)”

IBM using Watson to build a “SIRI for Cities”

Curated on Posted on by Stefaan Verhulst

 at FastCompany: “A new app that incorporates IBM’s Watson cognitive computing platform is like Siri for ordering city services.

IBM said today that the city of Surrey, in British Columbia, Canada, has rolled out the new app, which leverages Watson’s sophisticated language and data analysis system to allow residents to make requests for things like finding out why their trash wasn’t picked up or how to find a lost cat using natural language.

Watson is best known as the computer system that autonomously vanquished the world’s best Jeopardy players during a highly publicized competition in 2011. In the years since, IBM has applied the system to a wide range of computing problems in industries like health care, banking, retail, and education. The system is based on Watson’s ability to understand natural language queries and to analyze huge data sets.

Recently, Watson rolled out a tool designed to help people detect the tone in their writing.

Surrey worked with the developer Purple Forge to build the new city services app, which will be combined with the city’s existing “My Surrey” mobile and web tools. IBM said that residents can ask a wide range of questions on devices like smartphones, laptops, or even Apple Watches. Big Blue said Surrey’s app is the first time Watson has been utilized in a “citizen services” app.

The tool offers a series of frequently asked questions, but also allows residents in the city of nearly half a million to come up with their own. IBM said Surrey officials are hopeful that the app will help them be more responsive to residents’ concerns.

Among the services users can ask about are those provided by Surrey’s police and fire departments, animal control, parking enforcement, trash pickup, and others….(More)”

The Trouble With Disclosure: It Doesn’t Work

Curated on Posted on by Stefaan Verhulst

Jesse Eisinger at ProPublica: “Louis Brandeis was wrong. The lawyer and Supreme Court justice famously declared that sunlight is the best disinfectant, and we have unquestioningly embraced that advice ever since.

 Over the last century, disclosure and transparency have become our regulatory crutch, the answer to every vexing problem. We require corporations and government to release reams of information on food, medicine, household products, consumer financial tools, campaign finance and crime statistics. We have a booming “report card” industry for a range of services, including hospitals, public schools and restaurants.

All this sunlight is blinding. As new scholarship is demonstrating, the value of all this information is unproved. Paradoxically, disclosure can be useless — and sometimes actually harmful or counterproductive.

“We are doing disclosure as a regulatory move all over the board,” says Adam J. Levitin, a law professor at Georgetown, “The funny thing is, we are doing this despite very little evidence of its efficacy.”

Let’s start with something everyone knows about — the “terms of service” agreements for the likes of iTunes. Like everybody else, I click the “I agree” box, feeling a flash of resentment. I’m certain that in Paragraph 184 is a clause signing away my firstborn to a life of indentured servitude to Timothy D. Cook as his chief caviar spoon keeper.

Our legal theoreticians have determined these opaque monstrosities work because someone, somewhere reads the fine print in these contracts and keeps corporations honest. It turns out what we laymen intuit is true: No one reads them, according to research by a New York University law professor, Florencia Marotta-Wurgler.

In real life, there is no critical mass of readers policing the agreements. And if there were an eagle-eyed crew of legal experts combing through these agreements, what recourse would they have? Most people don’t even know that the Supreme Court has gutted their rights to sue in court, and they instead have to go into arbitration, which usually favors corporations.

The disclosure bonanza is easy to explain. Nobody is against it. It’s politically expedient. Companies prefer such rules, especially in lieu of actual regulations that would curtail bad products or behavior. The opacity lobby — the remora fish class of lawyers, lobbyists and consultants in New York and Washington — knows that disclosure requirements are no bar to dodgy practices. You just have to explain what you’re doing in sufficiently incomprehensible language, a task that earns those lawyers a hefty fee.

Of course, some disclosure works. Professor Levitin cites two examples. The first is an olfactory disclosure. Methane doesn’t have any scent, but a foul smell is added to alert people to a gas leak. The second is ATM. fees. A study in Australia showed that once fees were disclosed, people avoided the high-fee machines and took out more when they had to go to them.

But to Omri Ben-Shahar, co-author of a recent book, ” More Than You Wanted To Know: The Failure of Mandated Disclosure,” these are cherry-picked examples in a world awash in useless disclosures. Of course, information is valuable. But disclosure as a regulatory mechanism doesn’t work nearly well enough, he argues….(More)

Algorithms and Bias

Curated on Posted on by Stefaan Verhulst

Q. and A. With Cynthia Dwork in the New York Times: “Algorithms have become one of the most powerful arbiters in our lives. They make decisions about the news we read, the jobs we get, the people we meet, the schools we attend and the ads we see.

Yet there is growing evidence that algorithms and other types of software can discriminate. The people who write them incorporate their biases, and algorithms often learn from human behavior, so they reflect the biases we hold. For instance, research has shown that ad-targeting algorithms have shown ads for high-paying jobs to men but not women, and ads for high-interest loans to people in low-income neighborhoods.

Cynthia Dwork, a computer scientist at Microsoft Research in Silicon Valley, is one of the leading thinkers on these issues. In an Upshot interview, which has been edited, she discussed how algorithms learn to discriminate, who’s responsible when they do, and the trade-offs between fairness and privacy.

Q: Some people have argued that algorithms eliminate discriminationbecause they make decisions based on data, free of human bias. Others say algorithms reflect and perpetuate human biases. What do you think?

A: Algorithms do not automatically eliminate bias. Suppose a university, with admission and rejection records dating back for decades and faced with growing numbers of applicants, decides to use a machine learning algorithm that, using the historical records, identifies candidates who are more likely to be admitted. Historical biases in the training data will be learned by the algorithm, and past discrimination will lead to future discrimination.

Q: Are there examples of that happening?

A: A famous example of a system that has wrestled with bias is the resident matching program that matches graduating medical students with residency programs at hospitals. The matching could be slanted to maximize the happiness of the residency programs, or to maximize the happiness of the medical students. Prior to 1997, the match was mostly about the happiness of the programs.

This changed in 1997 in response to “a crisis of confidence concerning whether the matching algorithm was unreasonably favorable to employers at the expense of applicants, and whether applicants could ‘game the system,’ ” according to a paper by Alvin Roth and Elliott Peranson published in The American Economic Review.

Q: You have studied both privacy and algorithm design, and co-wrote a paper, “Fairness Through Awareness,” that came to some surprising conclusions about discriminatory algorithms and people’s privacy. Could you summarize those?

A: “Fairness Through Awareness” makes the observation that sometimes, in order to be fair, it is important to make use of sensitive information while carrying out the classification task. This may be a little counterintuitive: The instinct might be to hide information that could be the basis of discrimination….

Q: The law protects certain groups from discrimination. Is it possible to teach an algorithm to do the same?

A: This is a relatively new problem area in computer science, and there are grounds for optimism — for example, resources from the Fairness, Accountability and Transparency in Machine Learning workshop, which considers the role that machines play in consequential decisions in areas like employment, health care and policing. This is an exciting and valuable area for research. …(More)”

Yelp’s Consumer Protection Initiative: ProPublica Partnership Brings Medical Info to Yelp

Curated on Posted on by Stefaan Verhulst

Yelp Official Blog: “…exists to empower and protect consumers, and we’re continually focused on how we can enhance our service while enhancing the ability for consumers to make smart transactional decisions along the way.

A few years ago, we partnered with local governments to launch the LIVES open data standard. Now, millions of consumers find restaurant inspection scores when that information is most relevant: while they’re in the middle of making a dining decision (instead of when they’re signing the check). Studies have shown that displaying this information more prominently has a positive impact.

Today we’re excited to announce we’ve joined forces with ProPublica to incorporate health care statistics and consumer opinion survey data onto the Yelp business pages of more than 25,000 medical treatment facilities. Read more in today’s Washington Post story.

We couldn’t be more excited to partner with ProPublica, the Pulitzer Prize winning non-profit newsroom that produces investigative journalism in the public interest.

The information is compiled by ProPublica from their own research and the Centers for Medicare and Medicaid Services (CMS) for 4,600 hospitals, 15,000 nursing homes, and 6,300 dialysis clinics in the US and will be updated quarterly. Hover text on the business page will explain the statistics, which include number of serious deficiencies and fines per nursing home and emergency room wait times for hospitals. For example, West Kendall Baptist Hospital has better than average doctor communication and an average 33 minute ER wait time, Beachside Nursing Center currently has no deficiencies, and San Mateo Dialysis Center has a better than average patient survival rate.

Now the millions of consumers who use Yelp to find and evaluate everything from restaurants to retail will have even more information at their fingertips when they are in the midst of the most critical life decisions, like which hospital to choose for a sick child or which nursing home will provide the best care for aging parents….(More)“

Print Wikipedia

Curated on Posted on by Stefaan Verhulst

“Print Wikipedia is a both a utilitarian visualization of the largest accumulation of human knowledge and a poetic gesture towards the futility of the scale of big data. Michael Mandiberg has written software that parses the entirety of the English-language Wikipedia database and programmatically lays out 7600 volumes, complete with covers, and then uploads them to Lulu.com. In addition, he has compiled a Wikipedia Table of Contents, and a Wikipedia Contributor Appendix…..

Michael Mandiberg is an interdisciplinary artist, scholar, and educator living in Brooklyn, New York. He received his M.F.A. from the California Institute of the Arts and his B.A. from Brown University. His work traces the lines of political and symbolic power online, working on the Internet in order to comment on and intercede in the real flows of information. His work lives at Mandiberg.com.

Print Wikipedia by Michael Mandiberg from Lulu.com on Vimeo.”

 

How We’re Changing the Way We Respond to Petitions

Curated on Posted on by Stefaan Verhulst

Jason Goldman (White House) at Medium: “…In 2011 (years before I arrived at the White House), the team here developed a petitions platform called We the People. It provided a clear and easy way for the American people to petition their government — along with a threshold for action. Namely — once a petition gains 100,000 signatures.

This was a new system for the United States government, announced as a flagship effort in the first U.S. Open Government National Action Plan. Right now it exists only for the White House (Hey, Congress! We have anopen API! Get in touch!) Some other countries, including Germany and theUnited Kingdom, do online petitions, too. In fact, the European Parliamenthas even started its own online petitioning platform.

For the most part, we’ve been pretty good about responding — before today, the Obama Administration had responded to 255 petitions that had collectively gathered more than 11 million signatures. That’s more than 91 percent of the petitions that have met our threshold requiring a response. Some responses have taken a little longer than others. But now, I’m happy to say, we have caught up.

Today, the White House is responding to every petition in our We the Peoplebacklog — 20 in all.

This means that nearly 2.5 million people who had petitioned us to take action on something heard back today. And it’s our goal to make that response the start of the conversation, not the final page. The White House is made up of offices that research and analyze the kinds of policy issues raised by these petitions, and leaders from those offices will be taking questions today, and in the weeks to come, from petition signers, on topics such as vaccination policy, community policing, and other petition subjects.

Take a look at more We the People stats here.

We’ll start the conversation on Twitter. Follow @WeThePeople, and join the conversation using hashtag #WeThePeople. (I’ll be personally taking your questions on @Goldman44 about how we’re changing the platform specifically at 3:30 p.m. Eastern.)

We the People, Moving Forward

We’re going to be changing a few things about We the People.

  1. First, from now on, if a petition meets the signature goal within a designated period of time, we will aim to respond to it — with an update or policy statement — within 60 days wherever possible. You can read about the details of our policy in the We the People Terms of Participation.
  2. Second, other outside petitions platforms are starting to tap into the We the People platform. We’re excited to announce today that Change.org is choosing to integrate with the We the People platform, meaning the future signatures of its 100 million users will count toward the threshold for getting an official response from the Administration. We’re also opening up the code behind petitions.whitehouse.gov on Drupal.org and GitHub, which empowers other governments and outside organizations to create their own versions of this platform to engage their own citizens and constituencies.
  3. Third, and most importantly, the process of hearing from us about your petition is going to look a little different. We’ve assembled a team of people responsible for taking your questions and requests and bringing them to the right people — whether within the White House or in an agency within the Administration — who may be in a position to say something about your request….(More)

A Visual Introduction to Machine Learning

Curated on Posted on by Stefaan Verhulst

R2D3 introduction: “In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco…./

 

  1. Machine learning identifies patterns using statistical learning and computers by unearthing boundaries in data sets. You can use it to make predictions.
  2. One method for making predictions is called a decision trees, which uses a series of if-then statements to identify boundaries and define patterns in the data
  3. Overfitting happens when some boundaries are based on on distinctions that don’t make a difference. You can see if a model overfits by having test data flow through the model….(More)”