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Illegal PayDay syndicate in New York busted

There’s an interesting and horrible New York Time story by Jessica Silver-Greenberg about a PayDay loan syndicate being run out of New York State. The syndicate consists of twelve companies owned by a single dude, Carey Vaughn Brown, with help from a corrupt lawyer and another corrupt COO. Manhattan District Attorneys are charging him and his helpers with usury under New York law.

The complexity of the operation was deliberate and intended to obscure the chain of events that would start with a New Yorker online looking for quick cash online and end with a predatory loan. They’d interface with a company called MyCashNow.com, which would immediately pass their application on to a bunch of other companies in different states or overseas.

Important context: in New York, the usury law caps interest rates at 25 percent annually, and these PayDay operations were charging between 350 and 650 percent annually. Also key, the usury laws apply to where the borrower is, not where the lender is, so even though some of the companies were located (at least on paper) in the West Indies, they were still breaking the law.

They don’t know exactly how big the operation was in New York, but one clue is that in 2012, one of the twelve companies had $50 million in proceeds from New York.

Here’s my question: how did MyCashNow.com advertise? Did it use Google ads, or Facebook ads, or something else, and if so, what were the attributes of the desperate New Yorkers that it looked for to do its predatory work?

One side of this is that vulnerable people were somehow targeted. The other side is that well-off people were not, which meant they didn’t see ads like this, which makes it harder for people like the Manhattan District Attorney to even know about shady operations like this.

Categories: data science, modeling

Weapon of Math Destruction: “risk-based” sentencing models

There was a recent New York Times op-ed by Sonja Starr entitled Sentencing, by the Numbers (hat tip Jordan Ellenberg and Linda Brown) which described the widespread use – in 20 states so far and growing – of predictive models in sentencing.

The idea is to use a risk score to help inform sentencing of offenders. The risk is, I guess, supposed to tell us how likely the person is to commit another act in the future, although that’s not specified. From the article:

The basic problem is that the risk scores are not based on the defendant’s crime. They are primarily or wholly based on prior characteristics: criminal history (a legitimate criterion), but also factors unrelated to conduct. Specifics vary across states, but common factors include unemployment, marital status, age, education, finances, neighborhood, and family background, including family members’ criminal history.

I knew about the existence of such models, at least in the context of prisoners with mental disorders in England, but I didn’t know how widespread it had become here. This is a great example of a weapon of math destruction and I will be using this in my book.

A few comments:

  1. I’ll start with the good news. It is unconstitutional to use information such as family member’s criminal history against someone. Eric Holder is fighting against the use of such models.
  2. It is also presumably unconstitutional to jail someone longer for being poor, which is what this effectively does. The article has good examples of this.
  3. The modelers defend this crap as “scientific,” which is the worst abuse of science and mathematics imaginable.
  4. The people using this claim they only use it for as a way to mitigate sentencing, but letting a bunch of rich white people off easier because they are not considered “high risk” is tantamount to sentencing poor minorities more.
  5. It is a great example of confused causality. We could easily imagine a certain group that gets arrested more often for a given crime (poor black men, marijuana possession) just because the police have that practice for whatever reason (Stop & Frisk). Then model would then consider any such man at a higher risk of repeat offending, but that’s not because any particular person is actually more likely to do it, but because the police are more likely to arrest that person for it.
  6. It also creates a negative feedback loop on the most vulnerable population: the model will impose longer sentencing on the population it considers most risky, which will in turn make them even riskier in the future, if “length of time in prison previously” is used as an attribute in the model, which is surely is.
  7. Not to be cynical, but considering my post yesterday, I’m not sure how much momentum will be created to stop the use of such models, considering how discriminatory it is.
  8. Here’s an extreme example of preferential sentencing which already happens: rich dude Robert H Richards IV raped his 3-year-old daughter and didn’t go to jail because the judge ruled he “wouldn’t fare well in prison.”
  9. How great would it be if we used data and models to make sure rich people went to jail just as often and for just as long as poor people for the same crime, instead of the other way around?

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.

Critical Questions for Big Data by danah boyd & Kate Crawford

I’m teaching a class this summer in the Lede Program, starting in mid-July, which is called The Platform. Here’s the course description:

This course begins with the idea that computing tools are the products of human ingenuity and effort. They are never neutral and carry with them the biases of their designers and their design process. “Platform studies” is a new term used to describe investigations into these relationships between computing technologies and the creative or research products that they help to generate. How you understand how data, code, and algorithms affect creative practices can be an effective first step toward critical thinking about technology. This will not be purely theoretical, however, and specific case studies, technologies, and project work will make the ideas concrete.

Since my first class is coming soon, I’m actively thinking about what to talk about and which readings to assign. I’ve got wonderful guest lecturers coming, and for the most part the class will focus on those guest lecturers and their topics, but for the first class I want to give them an overview of a very large subject.

I’ve decided that danah boyd and Kate Crawford’s recent article, Critical Questions for Big Data, is pretty much perfect for this goal. I’ve read and written a lot about big data but even so I’m impressed by how clearly and comprehensively they have laid out their provocations. And although I’ve heard many of the ideas and examples before, some of them are new to me, and are directly related to the theme of the class, for example:

Twitter and Facebook are examples of Big Data sources that offer very poor archiving and search functions. Consequently, researchers are much more likely to focus on something in the present or immediate past – tracking reactions to an election, TV finale, or natural disaster – because of the sheer difficulty or impossibility of accessing older data.

Of course the students in the Lede are journalists, not academic researchers, which the article mostly addresses, and moreover they are not necessarily working with big data per se, but even so they are increasingly working with social media data, and moreover they are probably covering big data even if they don’t directly analyze it. So I think it’s still relevant to them. Or another way to express this is that one thing we will attempt to do in class is examine the extent to which their provocations are relevant.

Here’s another gem, directly related to the Facebook experiment I discussed yesterday:

As computational scientists have started engaging in acts of social science, there is a tendency to claim their work as the business of facts and not interpretation. A model may be mathematically sound, an experiment may seem valid, but as soon as a researcher seeks to understand what it means, the process of interpretation has begun. This is not to say that all interpretations are created equal, but rather that not all numbers are neutral.

In fact, what with this article and that case study, I’m pretty much set for my first day, after combining them with a discussion of the students’ projects and some related statistical experiments.

I also hope to invite at least one of the authors to come talk to the class, although I know they are both incredibly busy. Danah boyd, who recently came out with a book called It’s Complicated: the social lives of networked teensalso runs the Data & Society Research Institute, a NYC-based think/do tank focused on social, cultural, and ethical issues arising from data-centric technological development. I’m hoping she comes and talks about the work she’s starting up there.

Thanks for a great case study, Facebook!

I’m super excited about the recent “mood study” that was done on Facebook. It constitutes a great case study on data experimentation that I’ll use for my Lede Program class when it starts mid-July. It was first brought to my attention by one of my Lede Program students, Timothy Sandoval.

My friend Ernest Davis at NYU has a page of handy links to big data articles, and at the bottom (for now) there are a bunch of links about this experiment. For example, this one by Zeynep Tufekci does a great job outlining the issues, and this one by John Grohol burrows into the research methods. Oh, and here’s the original research article that’s upset everyone.

It’s got everything a case study should have: ethical dilemmas, questionable methodology, sociological implications, and questionable claims, not to mention a whole bunch of media attention and dissection.

By the way, if I sound gleeful, it’s partly because I know this kind of experiment happens on a daily basis at a place like Facebook or Google. What’s special about this experiment isn’t that it happened, but that we get to see the data. And the response to the critiques might be, sadly, that we never get another chance like this, so we have to grab the opportunity while we can.

The dark matter of big data

A tiny article in The Cap Times was recently published (hat tip Jordan Ellenberg) which describes the existence of a big data model which claims to help filter and rank school teachers based on their ability to raise student test scores. I guess it’s a kind of pre-VAM filtering system, and if it was hard to imagine a more vile model than the VAM, here you go. The article mentioned that the Madison School Board was deliberating on whether to spend $273K on this model.

One of the teachers in the district wrote her concerns about this model in her blog and then there was a debate at the school board meeting, and a journalist covered the meeting, so we know about it. But it was a close call, and this one could have easily slipped under the radar, or at least my radar.

Even so, now I know about it, and once I looked at the website of the company promoting this model, I found links to an article where they name a customer, for example in the Charlotte-Mecklenburg School District of North Carolina. They claim they only filter applications using their tool, they don’t make hiring decisions. Cold comfort for people who got removed by some random black box algorithm.

I wonder how many of the teachers applying to that district knew their application was being filtered through such a model? I’m going to guess none. For that matter, there are all sorts of application screening algorithms being regularly used of which applicants are generally unaware.

It’s just one example of the dark matter of big data. And by that I mean the enormous and growing clusters of big data models that are only inadvertently detectable by random small-town or small-city budget meeting journalism, or word-of-mouth reports coming out of conferences or late-night drinking parties with VC’s.

The vast majority of big data dark matter is still there in the shadows. You can only guess at its existence and its usage. Since the models themselves are proprietary, and are generally deployed secretly, there’s no reason for the public to be informed.

Let me give you another example, this time speculative, but not at all unlikely.

Namely, big data health models arising from the quantified self movement data. This recent Wall Street Journal article entitled Can Data From Your Fitbit Transform Medicine? articulated the issue nicely:

A recent review of 43 health- and fitness-tracking apps by the advocacy group Privacy Rights Clearinghouse found that roughly one-third of apps tested sent data to a third party not disclosed by the developer. One-third of the apps had no privacy policy. “For us, this is a big trust issue,” said Kaiser’s Dr. Young.

Consumer wearables fall into a regulatory gray area. Health-privacy laws that prevent the commercial use of patient data without consent don’t apply to the makers of consumer devices. “There are no specific rules about how those vendors can use and share data,” said Deven McGraw, a partner in the health-care practice at Manatt, Phelps, and Phillips LLP.

The key is that phrase “regulatory gray area”; it should make you think “big data dark matter lives here”.

When you have unprotected data that can be used as a proxy of HIPAA-protected medical data, there’s no reason it won’t be. So anyone who wants stands to benefit from knowing health-related information about you – think future employers who might help pay for future insurance claims – will be interested in using big data dark matter models gleaned from this kind of unregulated data.

To be sure, most people nowadays who wear fitbits are athletic, trying to improve their 5K run times. But the article explained that the medical profession is on the verge of suggesting a much larger population of patients use such devices. So it could get ugly real fast.

Secret big data models aren’t new, of course. I remember a friend of mine working for a credit card company a few decades ago. Her job was to model which customers to offer subprime credit cards to, and she was specifically told to target those customers who would end up paying the most in fees. But it’s become much much easier to do this kind of thing with the proliferation of so much personal data, including social media data.

I’m interested in the dark matter, partly as research for my book, and I’d appreciate help from my readers in trying to spot it when it pops up. For example, I remember begin told that a certain kind of online credit score is used to keep people on hold for customer service longer, but now I can’t find a reference to it anywhere. We should really compile a list at the boundaries of this dark matter. Please help! And if you don’t feel comfortable commenting, my email address is on the About page.

The business of big data audits: monetizing fairness

I gave a talk to the invitation-only NYC CTO Club a couple of weeks ago about my fears about big data modeling, namely:

  • that big data modeling is discriminatory,
  • that big data modeling increases inequality, and
  • that big data modeling threatens democracy.

I had three things on my “to do” list for the audience of senior technologists, namely:

  • test internal, proprietary models for discrimination,
  • help regulators like the CFPB develop reasonable audits, and
  • get behind certain models being transparent and publicly accessible, including credit scoring, teacher evaluations, and political messaging models.

Given the provocative nature of my talk, I was pleasantly surprised by the positive reception I was given. Those guys were great – interactive, talkative, and very thoughtful. I think it helped that I wasn’t trying to sell them something.

Even so, I shouldn’t have been surprised when one of them followed up with me to talk about a possible business model for “fairness audits.” The idea is that, what with the recent bad press about discrimination in big data modeling (some of the audience had actually worked with the Podesta team), there will likely be a business advantage to being able to claim that your models are fair. So someone should develop those tests that companies can take. Quick, someone, monetize fairness!

One reason I think this might actually work – and more importantly, be useful – is that I focused on “effects-based” discrimination, which is to say testing a model by treating it like a black box and seeing how it works on different inputs and gives different outputs. In other words, I want to give a resume-sorting algorithm different resumes with similar qualifications but different races. An algorithmically induced randomized experiment, if you will.

From the business perspective, a test that allows a model to remain a black box feels safe, because it does not require true transparency, and allows the “secret sauce” to remain secret.

One thing, though. I don’t think it makes too much sense to have a proprietary model for fairness auditing. In fact the way I was imagining this was to develop an open-source audit model that the CFPB could use. What I don’t want, and which would be worse than nothing, would be if some private company developed a proprietary “fairness audit” model that we cannot trust and would claim to solve the very real problems listed above.

Update: something like this is already happening for privacy compliance in the big data world (hat tip David Austin).

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