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Alt Banking in Huffington Post #OWS

November 11, 2014 1 comment

Great news! The Alt Banking group had a piece published today in the Huffington Post entitled With Economic Justice For All, about our hopes for the next Attorney General.

For the sake of the essay, we coined the term “marble columns” to mean the opposite of “broken windows.” Instead of getting arrested for nothing, you never get arrested, as long as you work at a company with marble columns. For more, take a look at the whole piece!

Also, my good friend and bandmate Tom Adams (our band, the Tomtown Ramblers, is named after him) will be covering for me on mathbabe for the next few days while I’m away in Haiti. Please make him feel welcome!

Guest post: The dangers of evidence-based sentencing

This is a guest post by Luis Daniel, a research fellow at The GovLab at NYU where he works on issues dealing with tech and policy. He tweets @luisdaniel12. Crossposted at the GovLab.

What is Evidence-based Sentencing?

For several decades, parole and probation departments have been using research-backed assessments to determine the best supervision and treatment strategies for offenders to try and reduce the risk of recidivism. In recent years, state and county justice systems have started to apply these risk and needs assessment tools (RNA’s) to other parts of the criminal process.

Of particular concern is the use of automated tools to determine imprisonment terms. This relatively new practice of applying RNA information into the sentencing process is known as evidence-based sentencing (EBS).

What the Models Do

The different parameters used to determine risk vary by state, and most EBS tools use information that has been central to sentencing schemes for many years such as an offender’s criminal history. However, an increasing amount of states have been utilizing static factors such as gender, age, marital status, education level, employment history, and other demographic information to determine risk and inform sentencing. Especially alarming is the fact that the majority of these risk assessment tools do not take an offender’s particular case into account.

This practice has drawn sharp criticism from Attorney General Eric Holder who says “using static factors from a criminal’s background could perpetuate racial bias in a system that already delivers 20% longer sentences for young black men than for other offenders.” In the annual letter to the US Sentencing Commission, the Attorney General’s Office states that “utilizing such tools for determining prison sentences to be served will have a disparate and adverse impact on offenders from poor communities already struggling with social ills.” Other concerns cite the probable unconstitutionality of using group-based characteristics in risk assessments.

Where the Models Are Used

It is difficult to precisely quantify how many states and counties currently implement these instruments, although at least 20 states have implemented some form of EBS. Some of the states or states with counties that have implemented some sort of EBS (any type of sentencing: parole, imprisonment, etc) are: Pennsylvania, Tennessee, Vermont, Kentucky, Virginia, Arizona, Colorado, California, Idaho, Indiana, Missouri, Nebraska, Ohio, Oregon, Texas, and Wisconsin.

The Role of Race, Education, and Friendship

Overwhelmingly states do not include race in the risk assessments since there seems to be a general consensus that doing so would be unconstitutional. However, even though these tools do not take race into consideration directly, many of the variables used such as economic status, education level, and employment correlate with race. African-Americans and Hispanics are already disproportionately incarcerated and determining sentences based on these variables might cause further racial disparities.

The very socioeconomic characteristics such as income and education level used in risk assessments are the characteristics that are already strong predictors of whether someone will go to prison. For example, high school dropouts are 47 times more likely to be incarcerated than people in their similar age group who received a four-year college degree. It is reasonable to suspect that courts that include education level as a risk predictor will further exacerbate these disparities.

Some states, such as Texas, take into account peer relations and considers associating with other offenders as a “salient problem”. Considering that Texas is in 4th place in the rate of people under some sort of correctional control (parole, probation, etc) and that the rate is 1 in 11 for black males in the United States it is likely that this metric would disproportionately affect African-Americans.

Sonja Starr’s paper

Even so, in some cases, socioeconomic and demographic variables receive significant weight. In her forthcoming paper in the Stanford Law Review, Sonja Starr provides a telling example of how these factors are used in presentence reports. From her paper:

For instance, in Missouri, pre-sentence reports include a score for each defendant on a scale from -8 to 7, where “4-7 is rated ‘good,’ 2-3 is ‘above average,’ 0-1 is ‘average’, -1 to -2 is ‘below average,’ and -3 to -8 is ‘poor.’ Unlike most instruments in use, Missouri’s does not include gender. However, an unemployed high school dropout will score three points worse than an employed high school graduate—potentially making the difference between “good” and “average,” or between “average” and “poor.” Likewise, a defendant under age 22 will score three points worse than a defendant over 45. By comparison, having previously served time in prison is worth one point; having four or more prior misdemeanor convictions that resulted in jail time adds one point (three or fewer adds none); having previously had parole or probation revoked is worth one point; and a prison escape is worth one point. Meanwhile, current crime type and severity receive no weight.

Starr argues that such simple point systems may “linearize” a variable’s effect. In the underlying regression models used to calculate risk, some of the variable’s effects do not translate linearly into changes in probability of recidivism, but they are treated as such by the model.

Another criticism Starr makes is that they often make predictions on an individual based on averages of a group. Starr says these predictions can predict with reasonable precision the average recidivism rate for all offenders who share the same characteristics as the defendant, but that does not make it necessarily useful for individual predictions.

The Future of EBS Tools

The Model Penal Code is currently in the process of being revised and is set to include these risk assessment tools in the sentencing process. According to Starr, this is a serious development because it reflects the increased support of these practices and because of the Model Penal Code’s great influence in guiding penal codes in other states. Attorney General Eric Holder has already spoken against the practice, but it will be interesting to see whether his successor will continue this campaign.

Even if EBS can accurately measure risk of recidivism (which is uncertain according to Starr), does that mean that a greater prison sentence will result in less future offenses after the offender is released? EBS does not seek to answer this question. Further, if knowing there is a harsh penalty for a particular crime is a deterrent to commit said crime, wouldn’t adding more uncertainty to sentencing (EBS tools are not always transparent and sometimes proprietary) effectively remove this deterrent?

Even though many questions remain unanswered and while several people have been critical of the practice, it seems like there is great support for the use of these instruments. They are especially easy to support when they are overwhelmingly regarded as progressive and scientific, something Starr refutes. While there is certainly a place for data analytics and actuarial methods in the criminal justice system, it is important that such research be applied with the appropriate caution. Or perhaps not at all. Even if the tools had full statistical support, the risk of further exacerbating an already disparate criminal justice system should be enough to halt this practice.

Both Starr and Holder believe there is a strong case to be made that the risk prediction instruments now in use are unconstitutional. But EBS has strong advocates, so it’s a difficult subject. Ultimately, evidence-based sentencing is used to determine a person’s sentencing not based on what the person has done, but who that person is.

Bad Paper by Jake Halpern

Yesterday I finished Jake Halpern’s new book, Bad Paper: Chasing Debt From Wall Street To The Underground.

It’s an interesting series of close-up descriptions of the people who have been buying and selling revolving debt since the credit crisis, as well as the actual business of debt collecting. He talks about the very real problem, for debt collectors, of having no proof of debt, of having other people who have stolen on your debt trying to collect on it at the same time, and of course the fact that some debt collectors resort to illegal threats and misleading statements to get debtors – or possibly ex-debtors, it’s never entirely clear – to pay up or suffer the consequences. An arms race of quasi-legal and illegal cultural practices.

Halpern does a good job explaining the plight of the debt collectors, including the people hired for the call centers. It’s the poor pitted against the poorer here, a dirty fight where information asymmetry is absolutely essential to the profit margin of any given tier of the system.

Halpern outlines those tiers well, as well as the interesting lingo created by this subculture centered, at least until recently, in Buffalo, New York. People at the top are credit card companies themselves or hedge fund buyers from credit card companies; in other words, people who get “fresh debt” lists in the form of excel spreadsheets, where the people listed have recently stopped paying and might have some resources to pull. Then there are people who deal in older debt, which is harder to collect on. After that are people who have yet older debt which may or may not be stolen, so other collectors might simultaneously be picking over the carcasses. At the very bottom of the pile, from Halpern’s perspective, come the lawyers. They bring debtors to court and try to garnish wages.

Somewhat buried at very end of Halpern’s book is some quite useful information for the debtors. So for example, if you ever get dragged to court by a debt collection lawyer,

  1. definitely show up (or else they will just garnish your wages)
  2. ask for proof that they own the debt and how you spent it. They will likely not have such documentation and will dismiss your case.

Overall Bad Paper is a good book, and it explains a lot of interesting and useful information, but from my perspective, being firmly on the side of (most of) the debtors, everyone who gets a copy of the book should also get a copy of Strike Debt’s Debt Resistors’ Operation Manual, which has way more useful information, and even form letters, for the debtor.

As far as real solutions, we see the usual problems: underfunded and impotent regulators in the FTC, the CFPB, and the Attorney General’s office, as well as ridiculously small fines when actually caught that amount to fractions of the profit already made by illegal tactics. Everyone is feasting, even when they don’t find much meat on the bones.

Given how big a problem this is, and how many people are being pursued by debt collectors, you’d think they might set up a system of incentives so lawyers can make money by nailing illegal actions instead of just leveraging outdated information and trying to squeeze poor people out of their paychecks.

The bigger problem, once again, is that so many people are flat broke and largely go into debt for things like emergency expenses. And yes, of course there are people who buy a bunch of things they don’t need and then refuse to pay off their debts – Halpern profiles one such person – but the vast majority of the people we’re talking about are the struggling poor. It would be nice to see our country become a place where we don’t need so much damn debt in the first place, then the scavengers wouldn’t have so many rubbish piles to live off of.

Categories: #OWS, economics, journalism

Reverse-engineering Chinese censorship

This recent paper written by Gary King, Jennifer Pan, and Margaret Roberts explores the way social media posts are censored in China. It’s interesting, take a look, or read this article on their work.

Here’s their abstract:

Existing research on the extensive Chinese censorship organization uses observational methods with well-known limitations. We conducted the first large-scale experimental study of censorship by creating accounts on numerous social media sites, randomly submitting different texts, and observing from a worldwide network of computers which texts were censored and which were not. We also supplemented interviews with confidential sources by creating our own social media site, contracting with Chinese firms to install the same censoring technologies as existing sites, and—with their software, documentation, and even customer support—reverse-engineering how it all works. Our results offer rigorous support for the recent hypothesis that criticisms of the state, its leaders, and their policies are published, whereas posts about real-world events with collective action potential are censored.

Interesting that they got so much help from the Chinese to censor their posts. Also keep in mind a caveat from the article:

Yu Xie, a sociologist at the University of Michigan, Ann Arbor, says that although the study is methodologically sound, it overemphasizes the importance of coherent central government policies. Political outcomes in China, he notes, often rest on local officials, who are evaluated on how well they maintain stability. Such officials have a “personal interest in suppressing content that could lead to social movements,” Xie says.

I’m a sucker for reverse-engineering powerful algorithms, even when there are major caveats.

When the story IS the interaction with the public

Here at the Lede Program we’ve been getting lots of different perspectives on what data journalism is and what it could be. As usual I will oversimplify for the sake of clarity, and apologies in advance to anyone I might offend.

The old school version of data journalism, which is called computer assisted reporting, maintains that a data story is first and foremost a story and should be viewed as such: you are investigating and interrogating the data as you would a witness, but the data isn’t itself a story, but rather a way of gathering evidence for the claims posed in the story. Every number cited needs to be independently supported with a secondary source.

Really important journalism lives in this context and is supported by the data, and the journalists in this realm are FOIA experts and speak truth to power in an exciting way. Think leaks and whistleblowers.

The new school vision of data journalism – again, entirely oversimplified – is that, by creating interesting data interactives that allow people to see how the news affects them – whether that means a map of “stuff happening” where they can see the stuff happening near them, or a big dataset that people can interact with in a tailored way, or a jury duty quiz that allows people to see how answers might get them kicked off or kept on a jury.

I imagine that some of these new-fangled approaches don’t even seem like stories at all to the old-school journalists, who want to see a bad guy caught, or a straight-up story told with a twist and a surprise and a “human face”. I’m not sure many of them would even get past the pitch stage if proffered to a curmudgeonly editor (and all editors are curmudgeonly, that’s just a fact).

The new interactive stories do not tell one story. Instead, they tell a bunch of stories to a bunch of people, and that interaction itself becomes the story. They also educate the public in a somewhat untamed way: by interacting with a database a reader can see variations in time, or in space, or in demographic, at least if the data is presented carefully.

Similarly, by seeing how each question on a jury duty quiz nudges you towards the plaintiff or the defendant, you can begin to see how seemingly innocuous information collected about you accumulates, which is how profiles are formed, on and offline.

The problem with charter schools

Today I read this article written by Allie Gross (hat tip Suresh Naidu), a former Teach for America teacher whose former idealism has long been replaced by her experiences in the reality of education in this country. Her article is entitled The Charter School Profiteers.

It’s really important, and really well written, and just one of the articles in the online magazine Jacobin that I urge you to read and to subscribe to. In fact that article is part of a series (here’s another which focuses on charter schools in New Orleans) and it comes with a booklet called Class Action: An Activist Teacher’s Handbook. I just ordered a couple of hard copies.

I’d really like you to read the article, but as a teaser here’s one excerpt, a rant which she completely backs up with facts on the ground:

You haven’t heard of Odeo, the failed podcast company the Twitter founders initially worked on? Probably not a big deal. You haven’t heard about the failed education ventures of the person now running your district? Probably a bigger deal.

When we welcome schools that lack democratic accountability (charter school boards are appointed, not elected), when we allow public dollars to be used by those with a bottom line (such as the for-profit management companies that proliferate in Michigan), we open doors for opportunism and corruption. Even worse, it’s all justified under a banner of concern for poor public school students’ well-being.

While these issues of corruption and mismanagement existed before, we should be wary of any education reformer who claims that creating an education marketplace is the key to fixing the ills of DPS or any large city’s struggling schools. Letting parents pick from a variety of schools does not weed out corruption. And the lax laws and lack of accountability can actually exacerbate the socioeconomic ills we’re trying to root out.

Surveillance in NYC

There’s a CNN video news story explaining how the NYC Mayor’s Office of Data Analytics is working with private start-up Placemeter to count and categorize New Yorkers, often with the help of private citizens who install cameras in their windows. Here’s a screenshot from the Placemeter website:

From placemeter.com

From placemeter.com

You should watch the video and decide for yourself whether this is a good idea.

Personally, it disturbs me, but perhaps because of my priors on how much we can trust other people with our data, especially when it’s in private hands.

To be more precise, there is, in my opinion, a contradiction coming from the Placemeter representatives. On the one hand they try to make us feel safe by saying that, after gleaning a body count with their video tapes, they dump the data. But then they turn around and say that, in addition to counting people, they will also categorize people: gender, age, whether they are carrying a shopping bag or pushing strollers.

That’s what they are talking about anyway, but who knows what else? Race? Weight? Will they use face recognition software? Who will they sell such information to? At some point, after mining videos enough, it might not matter if they delete the footage afterwards.

Since they are a private company I don’t think such information on their data methodologies will be accessible to us via Freedom of Information Laws either. Or, let me put that another way. I hope that MODA sets up their contract so that such information is accessible via FOIL requests.