Review Federal Agencies on Yelp…and Maybe Get a Response


Yelp Official Blog: “We are excited to announce that Yelp has concluded an agreement with the federal government that will allow federal agencies and offices to claim their Yelp pages, read and respond to reviews, and incorporate that feedback into service improvements.

We encourage Yelpers to review any of the thousands of agency field offices, TSA checkpoints, national parks, Social Security Administration offices, landmarks and other places already listed on Yelp if you have good or bad feedback to share about your experiences. Not only is it helpful to others who are looking for information on these services, but you can actually make an impact by sharing your feedback directly with the source.

It’s clear Washington is eager to engage with people directly through social media. Earlier this year a group of 46 lawmakers called for the creation of a “Yelp for Government” in order to boost transparency and accountability, and Representative Ron Kind reiterated this call in a letter to the General Services Administration (GSA). Luckily for them, there’s no need to create a new platform now that government agencies can engage directly on Yelp.

As this agreement is fully implemented in the weeks and months ahead, we’re excited to help the federal government more directly interact with and respond to the needs of citizens and to further empower the millions of Americans who use Yelp every day.

In addition to working with the federal government, last week we announced our our partnership 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. We’ve also partnered with local governments in expanding the LIVES open data standard to show restaurant health scores on Yelp….(More)”

Can big databases be kept both anonymous and useful?


The Economist: “….The anonymisation of a data record typically means the removal from it of personally identifiable information. Names, obviously. But also phone numbers, addresses and various intimate details like dates of birth. Such a record is then deemed safe for release to researchers, and even to the public, to make of it what they will. Many people volunteer information, for example to medical trials, on the understanding that this will happen.

But the ability to compare databases threatens to make a mockery of such protections. Participants in genomics projects, promised anonymity in exchange for their DNA, have been identified by simple comparison with electoral rolls and other publicly available information. The health records of a governor of Massachusetts were plucked from a database, again supposedly anonymous, of state-employee hospital visits using the same trick. Reporters sifting through a public database of web searches were able to correlate them in order to track down one, rather embarrassed, woman who had been idly searching for single men. And so on.

Each of these headline-generating stories creates a demand for more controls. But that, in turn, deals a blow to the idea of open data—that the electronic “data exhaust” people exhale more or less every time they do anything in the modern world is actually useful stuff which, were it freely available for analysis, might make that world a better place.

Of cake, and eating it

Modern cars, for example, record in their computers much about how, when and where the vehicle has been used. Comparing the records of many vehicles, says Viktor Mayer-Schönberger of the Oxford Internet Institute, could provide a solid basis for, say, spotting dangerous stretches of road. Similarly, an opening of health records, particularly in a country like Britain, which has a national health service, and cross-fertilising them with other personal data, might help reveal the multifarious causes of diseases like Alzheimer’s.

This is a true dilemma. People want both perfect privacy and all the benefits of openness. But they cannot have both. The stripping of a few details as the only means of assuring anonymity, in a world choked with data exhaust, cannot work. Poorly anonymised data are only part of the problem. What may be worse is that there is no standard for anonymisation. Every American state, for example, has its own prescription for what constitutes an adequate standard.

Worse still, devising a comprehensive standard may be impossible. Paul Ohm of Georgetown University, in Washington, DC, thinks that this is partly because the availability of new data constantly shifts the goalposts. “If we could pick an industry standard today, it would be obsolete in short order,” he says. Some data, such as those about medical conditions, are more sensitive than others. Some data sets provide great precision in time or place, others merely a year or a postcode. Each set presents its own dangers and requirements.

Fortunately, there are a few easy fixes. Thanks in part to the headlines, many now agree that public release of anonymised data is a bad move. Data could instead be released piecemeal, or kept in-house and accessible by researchers through a question-and-answer mechanism. Or some users could be granted access to raw data, but only in strictly controlled conditions.

All these approaches, though, are anathema to the open-data movement, because they limit the scope of studies. “If we’re making it so hard to share that only a few have access,” says Tim Althoff, a data scientist at Stanford University, “that has profound implications for science, for people being able to replicate and advance your work.”

Purely legal approaches might mitigate that. Data might come with what have been called “downstream contractual obligations”, outlining what can be done with a given data set and holding any onward recipients to the same standards. One perhaps draconian idea, suggested by Daniel Barth-Jones, an epidemiologist at Columbia University, in New York, is to make it illegal even to attempt re-identification….(More).”

Big data algorithms can discriminate, and it’s not clear what to do about it


 at the Conversation“This program had absolutely nothing to do with race…but multi-variable equations.”

That’s what Brett Goldstein, a former policeman for the Chicago Police Department (CPD) and current Urban Science Fellow at the University of Chicago’s School for Public Policy, said about a predictive policing algorithm he deployed at the CPD in 2010. His algorithm tells police where to look for criminals based on where people have been arrested previously. It’s a “heat map” of Chicago, and the CPD claims it helps them allocate resources more effectively.

Chicago police also recently collaborated with Miles Wernick, a professor of electrical engineering at Illinois Institute of Technology, to algorithmically generate a “heat list” of 400 individuals it claims have thehighest chance of committing a violent crime. In response to criticism, Wernick said the algorithm does not use “any racial, neighborhood, or other such information” and that the approach is “unbiased” and “quantitative.” By deferring decisions to poorly understood algorithms, industry professionals effectively shed accountability for any negative effects of their code.

But do these algorithms discriminate, treating low-income and black neighborhoods and their inhabitants unfairly? It’s the kind of question many researchers are starting to ask as more and more industries use algorithms to make decisions. It’s true that an algorithm itself is quantitative – it boils down to a sequence of arithmetic steps for solving a problem. The danger is that these algorithms, which are trained on data produced by people, may reflect the biases in that data, perpetuating structural racism and negative biases about minority groups.

There are a lot of challenges to figuring out whether an algorithm embodies bias. First and foremost, many practitioners and “computer experts” still don’t publicly admit that algorithms can easily discriminate.More and more evidence supports that not only is this possible, but it’s happening already. The law is unclear on the legality of biased algorithms, and even algorithms researchers don’t precisely understand what it means for an algorithm to discriminate….

While researchers clearly understand the theoretical dangers of algorithmic discrimination, it’s difficult to cleanly measure the scope of the issue in practice. No company or public institution is willing to publicize its data and algorithms for fear of being labeled racist or sexist, or maybe worse, having a great algorithm stolen by a competitor.

Even when the Chicago Police Department was hit with a Freedom of Information Act request, they did not release their algorithms or heat list, claiming a credible threat to police officers and the people on the list. This makes it difficult for researchers to identify problems and potentially provide solutions.

Legal hurdles

Existing discrimination law in the United States isn’t helping. At best, it’s unclear on how it applies to algorithms; at worst, it’s a mess. Solon Barocas, a postdoc at Princeton, and Andrew Selbst, a law clerk for the Third Circuit US Court of Appeals, argued together that US hiring law fails to address claims about discriminatory algorithms in hiring.

The crux of the argument is called the “business necessity” defense, in which the employer argues that a practice that has a discriminatory effect is justified by being directly related to job performance….(More)”

What factors influence transparency in US local government?


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


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


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”


 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


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


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


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)