It is available here and is based on a related essay written by Susan Webber entitled “Management’s Great Addiction: It’s time we recognized that we just can’t measure everything.” It is being published by O’Reilly as an e-book.
No, I don’t know who that woman is looking skeptical on the cover. I wish they’d asked me for a picture of a skeptical person, I think my 11-year-old son would’ve done a better job.
Did you think public radio doesn’t have advertising? Think again.
Last week Here and Now’s host Jeremy Hobson set up College Board’s James Montoya for a perfect advertisement regarding a story on SAT scores going down. The transcript and recording are here (hat tip Becky Jaffe).
To set it up, they talk about how GPA’s are going up on average over the country but how, at the same time, the average SAT score went down last year.
Somehow the interpretation of this is that there’s grade inflation and that kids must be in need of more test prep because they’re dumber.
What is the College Board?
You might think, especially if you listen to this interview, that the college board is a thoughtful non-profit dedicated to getting kids prepared for college.
Make no mistake about it: the College Board is a big business, and much of their money comes from selling test prep stuff on top of administering tests. Here are a couple of things you might want to know about College Board through its wikipedia page:
Consumer rights organization Americans for Educational Testing Reform (AETR) has criticized College Board for violating its non-profit status through excessive profits and exorbitant executive compensation; nineteen of its executives make more than $300,000 per year, with CEO Gaston Caperton earning $1.3 million in 2009 (including deferred compensation). AETR also claims that College Board is acting unethically by selling test preparation materials, directly lobbying legislators and government officials, and refusing to acknowledge test-taker rights.
Anyhoo, let’s just say it this way: College Board has the ability to create an “emergency” about SAT scores, by say changing the test or making it harder, and then the only “reasonable response” is to pay for yet more test prep. And somehow Here and Now’s host Jeremy Hobson didn’t see this coming at all.
Here’s an excerpt:
HOBSON: It also suggests, when you look at the year-over-year scores, the averages, that things are getting worse, not better, because if I look at, for example, in critical reading in 2006, the average being 503, and now it’s 496. Same deal in math and writing. They’ve gone down.
MONTOYA: Well, at the same time that we have seen the scores go down, what’s very interesting is that we have seen the average GPAs reported going up. So, for example, when we look at SAT test takers this year, 48 percent reported having a GPA in the A range compared to 45 percent last year, compared to 44 percent in 2011, I think, suggesting that there simply have to be more rigor in core courses.
HOBSON: Well, and maybe that there’s grade inflation going on.
MONTOYA: Well, clearly, that there is grade inflation. There is no question about that. And it’s one of the reasons why standardized test scores are so important in the admission office. I know that, as a former dean of admission, test scores help gauge the meaning of a GPA, particularly given the fact that nearly half of all SAT takers are reporting a GPA in the A range.
Just to be super clear about the shilling, here’s Hobson a bit later in the interview:
HOBSON: Well – and we should say that your report noted – since you mentioned practice – that as is the case with the ACT, the students who take the rigorous prep courses do better on the SAT.
What does it really mean when SAT scores go down?
Here’s the thing. SAT scores are fucked with ALL THE TIME. Traditionally, they had to make SAT’s harder since people were getting better at them. As test-makers, they want a good bell curve, so they need to adjust the test as the population changes and as their habits of test prep change.
The result is that SAT tests are different every year, so just saying that the scores went down from year to year is meaningless. Even if the same group of kids took those two different tests in the same year, they’d have different scores.
Also, according to my friend Becky who works with kids preparing for the SAT, they really did make substantial changes recently in the math section, changing the function notation, which makes it much harder for kids to parse the questions. In other words, they switched something around to give kids reason to pay for more test prep.
Important: this has nothing to do with their knowledge, it has to do with their training for this specific test.
If you want to understand the issues outside of math, take for example the essay. According to this critique, the number one criterion for essay grade is length. Length trumps clarity of expression, relevance of the supporting arguments to the thesis, mechanics, and all other elements of quality writing. As my friend Becky says:
I have coached high school students on the SAT for years and have found time and again, much to my chagrin, that students receive top scores for long essays even if they are desultory, tangent-filled and riddled with sentence fragments, run-ons, and spelling errors.
Similarly, I have consistently seen students receive low scores for shorter essays that are thoughtful and sophisticated, logical and coherent, stylish and articulate.
As long as the number one criterion for receiving a high score on the SAT essay is length, students will be confused as to what constitutes successful college writing and scoring well on the written portion of the exam will remain essentially meaningless. High-scoring students will have to unlearn the strategies that led to success on the SAT essay and relearn the fundamentals of written expression in a college writing class.
If the College Board (the makers of the SAT) is so concerned about the dumbing down of American children, they should examine their own role in lowering and distorting the standards for written expression.
Two things. First, shame on College Board and James Montoya for acting like SAT scores are somehow beacons of truth without acknowledging the fiddling that goes on time and time again by his company. And second, shame on Here and Now and Jemery Hobson for being utterly naive and buying in entirely to this scare tactic.
The 2013 PopTech & Rockefeller Foundation Bellagio Fellows - Kate Crawford, Patrick Meier, Claudia Perlich, Amy Luers, Gustavo Faleiros and Jer Thorp - yesterday published “Seven Principles for Big Data and Resilience Projects” on Patrick Meier’s blog iRevolution.
Although they claim that these principles are meant for “best practices for resilience building projects that leverage Big Data and Advanced Computing,” I think they’re more general than that (although I’m not sure exactly what a resilience building project is) I and I really like them. They are looking for public comments too. Go to the post for the full description of each, but here is a summary:
1. Open Source Data Tools
Wherever possible, data analytics and manipulation tools should be open source, architecture independent and broadly prevalent (R, python, etc.).
2. Transparent Data Infrastructure
Infrastructure for data collection and storage should operate based on transparent standards to maximize the number of users that can interact with the infrastructure.
3. Develop and Maintain Local Skills
Make “Data Literacy” more widespread. Leverage local data labor and build on existing skills.
4. Local Data Ownership
Use Creative Commons and licenses that state that data is not to be used for commercial purposes.
5. Ethical Data Sharing
Adopt existing data sharing protocols like the ICRC’s (2013). Permission for sharing is essential. How the data will be used should be clearly articulated. An opt in approach should be the preference wherever possible, and the ability for individuals to remove themselves from a data set after it has been collected must always be an option.
6. Right Not To Be Sensed
Local communities have a right not to be sensed. Large scale city sensing projects must have a clear framework for how people are able to be involved or choose not to participate.
7. Learning from Mistakes
Big Data and Resilience projects need to be open to face, report, and discuss failures.
My friend Suresh just reminded me about this article written a couple of years ago by Malcolm Gladwell and published in the New Yorker.
It concerns various scoring models that claim to be both comprehensive (which means it covers the whole thing, not just one aspect of the thing) and heterogeneous (which means it is broad enough to cover all things in a category), say for cars or for colleges.
Weird things happen when you try to do this, like not caring much about price or exterior detailing for sports cars.
Two things. First, this stuff is actually really hard to do well. I like how Gladwell addresses this issue:
At no point, however, do the college guides acknowledge the extraordinary difficulty of the task they have set themselves.
Second of all, I think the issue of combining heterogeneity and comprehensiveness is addressable, but it has to be addressed interactively.
Specifically, what if instead of a single fixed score, there was a place where a given car-buyer or college-seeker could go to fill out a form of preferences? For each defined and rated aspect, the user would fill answer a question about how much they cared about that aspect. They’d assign a weight to each aspect. A given question would look something like this:
For colleges, some people care a lot about whether their college has a ton of alumni giving, other people care more about whether the surrounding town is urban or rural. Let’s let people create their own scoring system. It’s technically easy.
I’ve suggested this before when I talked about rating math articles on various dimensions (hard, interesting, technical, well-written) and then letting people come and search based on weighting those dimensions and ranking. But honestly we can start even dumber, with car ratings and college ratings.
I recently took a job in the NYC Mayor’s Office as an unpaid consultant. It’s an interesting time to be working for the Mayor, to be sure – everyone’s waiting to see what happens this week with the election, and all sorts of things are up in the air. Planning essentially stops at December 31st.
I’m working in a data group which deals with social service agency data. That means Child Services, Homeless Services, and the like. Any agency where there there is direct contact with lots of people and their data. The idea is for me to help them out with a project that, if successful, I might be able to take to another city as a product. I’m still working full-time at the same job.
Specifically, my goal is to figure out a way to use data to help the people involved – the homeless, for example – get connected to better services. As a side effect I think this should make the agency more efficient. Far too many data studies only care about efficiency – how to make do with fewer police or fewer ambulances – with no thought or care about whether the people experiencing the services are being affected. I want to start with the people, and hope for efficiency gains, which I believe will come.
One thing that has already amazed me about this job, which I’ve just started, is the conversations people have about the ethics of data privacy.
It is a well-known fact that, as you link more and more data about people together, you can predict their behavior better. So for example, you could theoretically link all the different agency data for a given person into a profile, including crime data, health data, education and the like.
This might help you profile that person, and that might help you offer them better services. But it also might not be what that person wants you to do, especially if you start adding social media information. There’s a tension between the best model and reasonable limits of privacy and decency, even when the model is intended to be used in a primarily helpful manner. It’s more obvious when you’re attempting something insidious like predictive policing, of course.
Now, it shouldn’t shock me to have such conversations, because after all we are talking about some of the most vulnerable populations here. But even so, it does.
In all my time as a predictive modeler, I’ve never been in that kind of conversation, about the malicious things people could do with such-and-such profile information, or with this or that model, unless I started it myself.
When you work as a quant in finance, the data you work with is utterly sanitized to the point where, although it eventually trickles down to humans, you are asked to think of it as generated by some kind of machine, which we call “the market.”
Similarly, when you work in ad tech or other internet modeling, you think of users as the targets of your predatory goals: click on this, user, or buy that, user! They are prey, and the more we know about them the better our aim will be. If we can buy their profiles from Acxiom, all the better for our purposes.
This is the opposite of all of that. Super interesting, and glad I am being given this opportunity.
Yesterday’s New York Times ran a piece by Gina Kolata on randomized experiments in education. Namely, they’ve started to use randomized experiments like they do in medical trials. Here’s what’s going on:
… a little-known office in the Education Department is starting to get some real data, using a method that has transformed medicine: the randomized clinical trial, in which groups of subjects are randomly assigned to get either an experimental therapy, the standard therapy, a placebo or nothing.
They have preliminary results:
The findings could be transformative, researchers say. For example, one conclusion from the new research is that the choice of instructional materials — textbooks, curriculum guides, homework, quizzes — can affect achievement as profoundly as teachers themselves; a poor choice of materials is at least as bad as a terrible teacher, and a good choice can help offset a bad teacher’s deficiencies.
So far, the office — the Institute of Education Sciences — has supported 175 randomized studies. Some have already concluded; among the findings are that one popular math textbook was demonstrably superior to three competitors, and that a highly touted computer-aided math-instruction program had no effect on how much students learned.
Other studies are under way. Cognitive psychology researchers, for instance, are assessing an experimental math curriculum in Tampa, Fla.
If you go to any of the above links, you’ll see that the metric of success is consistently defined as a standardized test score. That’s the only gauge of improvement. So any “progress” that’s made is by definition measured by such a test.
In other words, if we optimize to this system, we will optimize for textbooks which raise standardized test scores. If it doesn’t improve kids’ test scores, it might as well not be in the book. In fact it will probably “waste time” with respect to raising scores, so there will effectively be a penalty for, say, fun puzzles, or understanding why things are true, or learning to write.
Now, if scores are all we cared about, this could and should be considered progress. Certainly Gina Kolata, the NYTimes journalist, didn’t mention that we might not care only about this – she recorded it as unfettered good, as she was expected to by the Education Department, no doubt. But, as a data scientist who gets paid to think about the feedback loops and side effects of choices like “metrics of success,” I have a problem with it.
I don’t have a thing against randomized tests – using them is a good idea, and will maybe even quiet some noise around all the different curriculums, online and in person. I do think, though, that we need to have more ways of evaluating an educational experience than a test score.
After all, if I take a pill once a day to prevent a disease, then what I care about is whether I get the disease, not which pill I took or what color it was. Medicine is a very outcome- focused discipline in a way that education is not. Of course, there are exceptions, say when the treatment has strong and negative side-effects, and the overall effect is net negative. Kind of like when the teacher raises his or her kids’ scores but also causes them to lose interest in learning.
If we go the way of the randomized trial, why not give the students some self-assessments and review capabilities of their text and their teacher (which is not to say teacher evaluations give clean data, because we know from experience they don’t)? Why not ask the students how they liked the book and how much they care about learning? Why not track the students’ attitudes, self-assessment, and goals for a subject for a few years, since we know longer-term effects are sometimes more important that immediate test score changes?
In other words, I’m calling for collecting more and better data beyond one-dimensional test scores. If you think about it, teenagers get treated better by their cell phone companies or Netflix than by their schools.
I know what you’re thinking – that students are all lazy and would all complain about anyone or anything that gave them extra work. My experience is that kids actually aren’t like this, know the difference between rote work and real learning, and love the learning part.
Another complaint I hear coming – long-term studies take too long and are too expensive. But ultimately these things do matter in the long term, and as we’ve seen in medicine, skimping on experiments often leads to bigger and more expensive problems. Plus, we’re not going to improve education overnight.
And by the way, if and/or when we do this, we need to implement strict privacy policies for the students’ answers – you don’t want a 7-year-old’s attitude about math held against him when he of she applies to college.
Yet another aspect of Gary Shteyngart’s dystopian fiction novel Super Sad True Love Story is coming true for reals this week.
Besides anticipating Occupy Wall Street, as well as Bloomberg’s sweep of Zuccotti Park (although getting it wrong on how utterly successful such sweeping would be), Shteyngart proposed the idea of instant, real-time and broadcast credit ratings.
Anyone walking around the streets of New York, as they’d pass a certain type of telephone pole – the kind that identifies you via your cell phone and communicates with data warehousing services and databases – would have their credit rating flashed onto a screen. If you went to a party, depending on how you impressed the other party go-ers, your score could plummet or rise in real time, and everyone would be able to keep track and treat you accordingly.
I mean, there were other things about the novel too, but as a data person these details certainly stuck with me since they are both extremely gross and utterly plausible.
And why do I say they are coming true now? I base my claim on two news stories I’ve been sent by my various blog readers recently.
[Aside: if you read my blog and find an awesome article that you want to send me, by all means do! My email address is available on my "About" page.]
First, coming via Suresh and Marcos, we learn that data broker Acxiom is letting people see their warehoused data. A few caveats, bien sûr:
- You get to see your own profile, here, starting in 2 days, but only your own.
- And actually, you only get to see some of your data. So they won’t tell you if you’re a suspected gambling addict, for example. It’s a curated view, and they want your help curating it more. You know, for your own good.
- And they’re doing it so that people have clarity on their business.
- Haha! Just kidding. They’re doing it because they’re trying to avoid regulations and they feel like this gesture of transparency might make people less suspicious of them.
- And they’re counting on people’s laziness. They’re allowing people to opt out, but of course the people who should opt out would likely never even know about that possibility.
- Just keep in mind that, as an individual, you won’t know what they really think they know about you, but as a corporation you can buy complete information about anyone who hasn’t opted out.
In any case those credit scores that Shteyngart talks about are already happening. The only issue is who gets flashed those numbers and when. Instead of the answers being “anyone walking down the street” and “when you walk by a pole” it’s “any corporation on the interweb” and “whenever you browse”.
After all, why would they give something away for free? Where’s the profit in showing the credit scores of anyone to everyone? Hmmmm….
That brings me to my second news story of the morning coming to me via Constantine, namely this TechCrunch story which explains how a startup called Fantex is planning to allow individuals to invest in celebrity athletes’ stocks. Yes, you too can own a tiny little piece of someone famous, for a price. From the article:
People can then buy shares of that player’s brand, like a stock, in the Fantex-consumer market. Presumably, if San Francisco 49ers tight end Vernon Davis has a monster year and looks like he’s going to get a bigger endorsement deal or a larger contract in a few years, his stock would rise and a fan could sell their Davis stock and cash out with a real, monetary profit. People would own tracking or targeted stocks in Fantex that would depend on the specific brand that they choose; these stocks would then rise and fall based on their own performance, not on the overall performance of Fantex.
Let’s put these two things together. I think it’s not too much of a stretch to acknowledge a reason for everyone to know everyone else’s credit score! Namely, we can can bet on each other’s futures!
I can’t think of any set-up more exhilarating to the community of hedge fund assholes than a huge, new open market – containing profit potentials for every single citizen of earth – where you get to make money when someone goes to the wrong college, or when someone enters into an unfortunate marriage and needs a divorce, or when someone gets predictably sick. An orgy in the exact center of tech and finance.
Are you with me peoples?!
I don’t know what your Labor Day plans are, but I’m getting ready my list of people to short in this spanking new market.
Don’t know about you, but for some reason I have a sinking feeling when it comes to the idea of Larry Summers. Word on the CNBC street is that he’s about to be named new Fed Chair, and I am living in a state of cognitive dissonance.
To distract myself, I’m going to try better to explain what I started to explain here, when I talked about the online peer-to-peer lending company Lending Club. Summers sits on the board of Lending Club, and from my perspective it’s a logical continuation of his career of deregulation and/or bypassing of vital regulation to enrich himself.
In this case, it’s a vehicle for bypassing the FTC’s Equal Credit Opportunities Rights. It’s not perfect, but it “prohibits credit discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because you get public assistance.” It forces credit scores to be relatively behavior based, like you see here. Let me contrast that to Lending Club.
Lending Club also uses mathematical models to score people who want to borrow money. These act as credit scores. But in this case, they use data like browsing history or anything they can grab about you on the web or from data warehousing companies like Acxiom (which I’ve written about here). From this Bloomberg article on Lending Club:
“What we’ve done is radically transform the way consumer lending operates,” Laplanche says in his speech. He says that LendingClub keeps staffing low by using algorithms to screen prospective borrowers for risk — rejecting 90 percent of them – - and has no physical branches like banks. “The savings can be passed on to more borrowers in terms of lower interest rates and investors in terms of attractive returns.”
I’d focus on the benefit for investors. Big money is now involved in this stuff. Turns out that bypassing credit score regulation is great for business, so of course.
For example, such models might look at your circle of friends on Facebook to see if you “run with the right crowd” before loaning you money. You can now blame your friends if you don’t get that loan! From this CNN article on the subject (hat tip David):
“It turns out humans are really good at knowing who is trustworthy and reliable in their community,” said Jeff Stewart, a co-founder and CEO of Lenddo. “What’s new is that we’re now able to measure through massive computing power.”
Moving along from taking out loans to getting jobs, there’s this description of how recruiters work online to perform digital background checks for potential employees. It’s a different set of laws this time that is subject to arbitrage but it’s exactly the same idea:
Non-discrimination laws prohibit employers from asking job applicants certain questions. They’re not supposed to ask about things like age, race, gender, disability, marital, and veteran status. (As you can imagine, sometimes a picture alone can reveal this privileged information. These safeguards against discrimination urge employers to simply not use this knowledge to make hiring decisions.) In addition to protecting people from systemic prejudice, these employment laws intend to shield us from capricious bias and whimsy. While casually snooping, however, a recruiter can’t unsee your Facebook rant on immigration amnesty, the same for your baby bump on Instagram. From profile pics and bios, blog posts and tweets, simple HR reconnaissance can glean tons of off-limits information.
Along with forcing recruiters to gaze with eyes wide shut, straddling legal liability and ignorance, invisible employment screens deny American workers the robust protections afforded by the FTC and the Fair Credit Reporting Act. The FCRA ensures that prospective employees are notified before their backgrounds and credit scores are verified. Employees are free to decline the checks, but employers are also free to deny further consideration unless a screening is allowed to take place. What’s important here is that employees must first give consent.
When a report reveals unsavory information about a candidate, and the employer chooses to take what’s called “adverse action,”—like deny a job offer—the employer is required to share the content of the background reports with the candidate. The applicant then has the right to explain or dispute inaccurate and incomplete aspects of the background check. Consent, disclosure, and recourse constitute a straightforward approach to employment screening.
Contrast this citizen-empowering logic with the casual Google search or to the informal, invisible social-media exam. As applicants, we don’t know if employers are looking, we’re not privy to what they see, and we have no way to appeal.
As legal scholars Daniel Solove and Chris Hoofnagle discuss, the amateur Google screens that are now a regular feature of work-life go largely unnoticed. Applicants are simply not called back. And they’ll never know the real reason.
I think the silent failure is the scariest part for me – people who don’t get jobs won’t know why.
Similarly, people denied loans from Lending Club by a secret algorithm don’t know why either. Maybe it’s because I made friends with the wrong person on Facebook? Maybe I should just go ahead and stop being friends with anyone who might put my electronic credit score at risk?
Of course this rant is predicated on the assumption that we think anti-discrimination laws are a good thing. In an ideal world, of course, we wouldn’t need them. But that’s not where we live.
Last week Obama began to making threats regarding a new college ranking system and its connection to federal funding. Here’s an excerpt of what he was talking about, from this WSJ article:
The president called for rating colleges before the 2015 school year on measures such as affordability and graduation rates—”metrics like how much debt does the average student leave with, how easy is it to pay off, how many students graduate on time, how well do those graduates do in the workforce,” Mr. Obama told a crowd at the University at Buffalo, the first stop on a two-day bus tour.
Interesting! This means that Obama is wading directly into the field of modeling. He’s probably sick of the standard college ranking system, put out by US News & World Reports. I kind of don’t blame him, since that model is flawed and largely gamed. In fact, I made a case for open sourcing that model recently just so that people would look into it and lose faith in its magical properties.
So I’m with Obama, that model sucks, and it’s high time there are other competing models so that people have more than one thing to think about.
On the other hand, what Obama is focusing on seems narrow. Here’s what he supposedly wants to do with that model (again from the WSJ article):
Once a rating system is in place, Mr. Obama will ask Congress to allocate federal financial aid based on the scores by 2018. Students at top-performing colleges could receive larger federal grants and more affordable student loans. “It is time to stop subsidizing schools that are not producing good results,” he said.
His main goal seems to be “to make college more affordable”.
I’d like to make a few comments on this overall plan. The short version is that he’s suggesting something that will have strong, mostly negative effects, and that won’t solve his problem of college affordability.
Why strong negative effects?
What Obama seems to realize about the existing model is that it’s had side effects because of the way college administrators have gamed the model. Presumably, given that this new proposed model will be directly tied to federal funding, it will be high-impact and will thus be thoroughly gamed by administrators as well.
The first complaint, then, is that Obama didn’t address this inevitably gaming directly – and that doesn’t bode well about his ability to put into place a reasonable model.
But let’s not follow his lead. Let’s think about what kind of gaming will occur once such a model is in place. It’s not pretty.
Here are the attributes he’s planning to use for colleges. I’ve substituted reasonably numerical proxies for his descriptions above:
- Cost (less is better)
- Percentage of people able to pay off their loans within 10 years (more is better)
- Graduation rate (more is better)
- Percentage of people graduating within 4 years (more is better)
- Percentage of people who get high-paying jobs after graduating (more is better)
Nobody is going to argue against optimizing for lower cost. Unfortunately, what with the cultural assumption of the need for a college education, combined with the ignorance and naive optimism of young people, not to mention start-ups like Upstart that allow young people to enter indentured servitude, the pressure is upwards, not downwards.
The supply of money for college is large and growing, and the answer to rising tuition costs is not to supply more money. Colleges have already responded to the existence of federal loans, for example, by raising tuition in the amount of the loan. Ironically, much of the rise in tuition cost has gone to administrators, whose job it is to game the system for more money.
Which is to say, you can penalize certain colleges for being at the front of the pack in terms of price, but if the overall cost is rising constantly, you’re not doing much.
If you really wanted to make costs low, then fund state universities and make them really good, and make them basically free. That would actually make private colleges try to compete on cost.
Paying off loans quickly
Here’s where we get to the heart of the problem with Obama’s plan.
What are you going to do, as an administrator tasked with making sure you never lose federal funding under the new regime?
Are you going to give all the students fairer terms on their debt? Or are you going to select for students that are more likely to get finance jobs? I’m guessing the latter.
So much for liberal arts educations. So much for learning about art, philosophy, or for that matter anything that isn’t an easy entrance into the tech or finance sector. Only colleges that don’t care a whit about federal money will even have an art history department.
Gaming the graduation rate is easy. Just lower your standards for degrees, duh.
How quickly people graduate
Again, a general lowering of standards is quick and easy.
How well graduates do in the workforce
Putting this into your model is toxic, and measures a given field directly in terms of market forces. Economics, Computer Science, and Business majors will be the kings of the hill. We might as well never produce writers, thinkers, or anything else creative again.
Note this pressure already exists today: many of our college presidents are becoming more and more corporate minded and less interested in education itself, mostly as a means to feed their endowments. As an example, I don’t need to look further than across my street to Barnard, where president Debora Spar somehow decided to celebrate Ina Drew as an example of success in front of a bunch of young Barnard students. I can’t help but think that was related to a hoped-for gift.
Obama needs to think this one through. Do we really want to build the college system in this country in the image of Wall Street and Silicon Valley? Do we want to intentionally skew the balance towards those industries even further?
Building a better college ranking model
The problem is that it’s actually really hard to model quality of education. The mathematical models that already exist and are being proposed are just pathetically bad at it, partly because college, ultimately, isn’t only about the facts you learn, or the job you get, or how quickly you get it. It’s actually a life experience which, in the best of cases, enlarges your world view, and gets you to strive for something you might not have known existed before going.
I’d suggest that, instead of building a new ranking system, we on the one hand identify truly fraudulent colleges (which really do exist) and on the other, invest heavily in state schools, giving them enough security so they can do without their army of expensive administrators.
You’ve probably heard rumors about this here and there, but the Wall Street Journal convincingly reported yesterday that websites charge certain people more for the exact thing.
Specifically, poor people were more likely to pay more for, say, a stapler from Staples.com than richer people. Home Depot and Lowes does the same for their online customers, and Discover and Capitol One make different credit card offers to people depending on where they live (“hey, do you live in a PayDay lender neighborhood? We got the card for you!”).
They got pretty quantitative for Staples.com, and did tests to determine the cost. From the article:
It is possible that Staples’ online-pricing formula uses other factors that the Journal didn’t identify. The Journal tested to see whether price was tied to different characteristics including population, local income, proximity to a Staples store, race and other demographic factors. Statistically speaking, by far the strongest correlation involved the distance to a rival’s store from the center of a ZIP Code. That single factor appeared to explain upward of 90% of the pricing pattern.
If anyone’s ever seen a census map, race is highly segregated by ZIP code, and my guess is we’d see pretty high correlations along racial lines as well, although they didn’t mention it in the article except to say that explicit race-related pricing is illegal. The article does mentions that things get more expensive in rural areas, which are also poorer, so there’s that acknowledged correlation.
But wait, how much of a price difference are we talking about? From the article:
Prices varied for about a third of the more than 1,000 randomly selected Staples.com products tested. The discounted and higher prices differed by about 8% on average.
In other words, a really non-trivial amount.
The messed up thing about this, or at least one of them, is that we could actually have way more control over our online personas than we think. It’s invisible to us, typically, so we don’t think about our cookies and our displayed IP addresses. But we could totally manipulate these signatures to our advantage if we set our minds to it.
Hackers, get thyselves to work making this technology easily available.
For that matter, given the 8% difference, there’s money on the line so some straight-up capitalist somewhere should be meeting that need. I for one would be willing to give someone a sliver of the amount saved every time they manipulated my online persona to save me money. You save me $1.00, I’ll give you a dime.
Here’s my favorite part of this plan: it would be easy for Staples to keep track of how much people are manipulating their ZIP codes. So if Staples.com infers a certain ZIP code for me to display a certain price, but then in check-out I ask them to send the package to a different ZIP code, Staples will know after-the-fact that I fooled them. But whatever, last time I looked it didn’t cost more or less to send mail to California or wherever than to Manhattan [Update: they do charge differently for packages, though. That's the only differential in cost I think is reasonable to pay].
I’d love to see them make a case for how this isn’t fair to them.
1. When an unemployed black woman pretends to be white her job offers skyrocket (Urban Intellectuals, h/t Mike Loukides). Excerpt from the article: “Two years ago, I noticed that Monster.com had added a “diversity questionnaire” to the site. This gives an applicant the opportunity to identify their sex and race to potential employers. Monster.com guarantees that this “option” will not jeopardize your chances of gaining employment. You must answer this questionnaire in order to apply to a posted position—it cannot be skipped. At times, I would mark off that I was a Black female, but then I thought, this might be hurting my chances of getting employed, so I started selecting the “decline to identify” option instead. That still had no effect on my getting a job. So I decided to try an experiment: I created a fake job applicant and called her Bianca White.”
2. How big data could identify the next felon – or blame the wrong guy (Bloomberg). From the article: “The use of physical characteristics such as hair, eye and skin color to predict future crimes would raise ‘giant red privacy flags’ since they are a proxy for race and could reinforce discriminatory practices in hiring, lending or law enforcement, said Chi Chi Wu, staff attorney at the National Consumer Law Center.”
3. How algorithms magnify misbehavior (the Guardian, h/t Suresh Naidu). From the article: “For one British university, what began as a time-saving exercise ended in disgrace when a computer model set up to streamline its admissions process exposed – and then exacerbated - gender and racial discrimination.”
This is just the beginning, unfortunately.
I offend people daily. People tell me they do “big data” and that they’ve been doing big data for years. Their argument is that they’re doing business analytics on a larger and larger scale, so surely by now it must be “big data”.
There’s an essential difference between true big data techniques, as actually performed at surprisingly few firms but exemplified by Google, and the human-intervention data-driven techniques referred to as business analytics.
No matter how big the data you use is, at the end of the day, if you’re doing business analytics, you have a person looking at spreadsheets or charts or numbers, making a decision after possibly a discussion with 150 other people, and then tweaking something about the way the business is run.
If you’re really doing big data, then those 150 people probably get
fired laid off, or even more likely are never hired in the first place, and the computer is programmed to update itself via an optimization method.
That’s not to say it doesn’t also spit out monitoring charts and numbers, and it’s not to say no person takes a look every now and then to make sure the machine is humming along, but there’s no point at which the algorithm waits for human intervention.
In other words, in a true big data setup, the human has stepped outside the machine and lets the machine do its thing. That means, of course, that it takes way more to set up that machine in the first place, and probably people make huge mistakes all the time in doing this, but sometimes they don’t. Google search got pretty good at this early on.
So with a business analytics set up we might keep track of the number of site visitors and a few sales metrics so we can later try to (and fail to) figure out whether a specific email marketing campaign had the intended effect.
But in a big data set-up it’s typically much more microscopic and detail oriented, collecting everything it can, maybe 1,000 attributed of a single customer, and figuring out what that guy is likely to do next time, how much they’ll spend, and the magic question, whether there will even be a next time.
So the first thing I offend people about is that they’re not really part of the “big data revolution”. And the second thing is that, usually, their job is potentially up for grabs by an algorithm.
So here’s something potential Fed Chair Larry Summers is involved with, a company called Lending Club, which creates a money lending system that cuts out the middle man banks.
Specifically, people looking for money come to the site and tell their stories, and try to get loans. The investors invest in whichever loans look good to them, for however much money they want. For a perspective on the risks and rewards of this kind of peer-to-peer lending operation, look at this Wall Street Journal article which explains things strictly from the investor’s point of view.
A few red flags go up for me as I learn more about Lending Club.
First, from this NYTimes article, “The company [Lending Club] itself is not regulated as a bank. But it has teamed up with a bank in Utah, one of the states that allows banks to charge high interest rates, and that bank is overseen by state regulators and the Federal Deposit Insurance Corporation.”
I’m not sure how the FDIC is involved exactly, but the Utah connection is good for something, namely allowing high interest rates. According to the same article, 37% of loans are for APR’s of between 19% and 29%.
Next, Summers is referred to in that article as being super concerned about the ability for the consumers to pay back the loans. But I wonder how someone is supposed to be both desperate enough to go for a 25% APR loan and also able to pay back the money. This sounds like loan sharking to me.
Probably what bothers me most though is that Lending Club, in addition to offering credit scores and income when they have that information, also scores people asking for loans with a proprietary model which is, as you guessed it, unregulated. Specifically, if it’s anything like ZestFinance, could use signals more correlated to being uneducated and/or poor than to the willingness or ability to pay back loans.
By the way, I’m not saying this concept is bad for everyone- there are probably winners on the side of the loanees, and it might be possible that they get a loan they otherwise couldn’t get or they get better terms than otherwise or a more bespoke contract than otherwise. I’m more worried about the idea of this becoming the new normal of how money changes hands and how that would affect people already squeezed out of the system.
I’d love your thoughts.
My friend Frank Pasquale sent me this article over twitter, about New York State attorney general Eric T. Schneiderman’s investigation into possibly unfair practices by big banks using opaque and sometimes erroneous databases to disqualify people from opening accounts.
Not much hard information is given in the article but we know that negative reports stemming from the databases have effectively banished more than a million lower-income Americans from the financial system, and we know that the number of “underbanked” people in this country has grown by 10% since 2009. Underbanked people are people who are shut out of the normal banking system and have to rely on the underbelly system including check cashing stores and payday lenders.
I can already hear the argument of my libertarian friends: if I’m a bank, and I have reason to suspect you have messed up with your finances in the past, I don’t offer you services. Done and done. Oh, and if I’m a smart bank that figures out some of these so-called “past mistakes” are actually erroneously reported, then I make extra money by serving those customers that are actually good when they look bad. And the free market works.
Two responses to this. First, at this point big banks are really not private companies, being on the taxpayer dole. In response they should reasonably be expected to provide banking services to all of not most people as part of a service. Of course this is a temporary argument, since nobody actually likes the fact that the banks aren’t truly private companies.
The second, more interesting point – at least to me – is this. We care about and defend ourselves from our constitutional rights being taken away but we have much less energy to defend ourselves against good things not happening to us.
In other words, it’s not written into the constitution that we all deserve a good checking account, nor a good college education, nor good terms on a mortgage, and so on. Even so, in a large society such as ours, such things are basic ingredients for a comfortable existence. Yet these services are rare if not nonexistent for a huge and swelling part of our society, resulting in a degradation of opportunity for the poor.
The overall effect is heinous, and at some point does seem to rise to the level of a constitutional right to opportunity, but I’m no lawyer.
In other words, instead of only worrying about the truly bad things that might happen to our vulnerable citizens, I personally spend just as much time worrying about the good things that might not happen to our vulnerable citizens, because from my perspective lots of good things not happening add up to bad things happening: they all narrow future options.
Here is an idea I’ve been hearing floating around the big data/ tech community: the idea of having algorithms embedded into law.
The argument for is pretty convincing on its face: Google has gotten its algorithms to work better and better over time by optimizing correctly and using tons of data. To some extent we can think of their business strategies and rules as a kind of “internal regulation”. So why don’t we take a page out of that book and improve our laws and specifically our regulations with constant feedback loops and big data?
No algos in law
There are some concerns I have right off the bat about this concept, putting aside the hugely self-serving dimension of it.
First of all, we would be adding opacity – of the mathematical modeling kind – to an already opaque system of law. It’s hard enough to read the legalese in a credit card contract without there also being a black box algorithm to make it impossible.
Second of all, whereas the incentives in Google are often aligned with the algorithm “working better”, whatever that means in any given case, the incentives of the people who write laws often aren’t.
So, for example, financial regulation is largely written by lobbyists. If you gave them a new tool, that of adding black box algorithms, then you could be sure they would use it to further obfuscate what is already a hopelessly complicated set of rules, and on top of it they’d be sure to measure the wrong thing and optimize to something random that would not interfere with their main goal of making big bets.
Right now lobbyists are used so heavily in part because they understand the complexity of their industries more than the lawmakers themselves. In other words, they actually add value in a certain way (besides in the monetary way). Adding black boxes would emphasize this asymmetric information problem, which is a terrible idea.
Third, I’m worried about the “black box” part of algorithms. There’s a strange assumption among modelers that you have to make algorithms secret or else people will game them. But as I’ve said before, if people can game your model, that just means your model sucks, and specifically that your proxies are not truly behavior-based.
So if it pertains to a law against shoplifting, say, you can’t have an embedded model which uses the proxy of “looking furtive and having bulges in your clothes.” You actually need to have proof that someone stole something.
If you think about that example for a moment, it’s absolutely not appropriate to use poor proxies in law, nor is it appropriate to have black boxes at all – we should all know what our laws are. This is true for regulation as well, since it’s after all still law which affects how people are expected to behave.
And by the way, what counts as a black box is to some extent in the eye of the beholder. It wouldn’t be enough to have the source code available, since that’s only accessible to a very small subset of the population.
Instead, anyone who is under the expectation of following a law should also be able to read and understand the law. That’s why the CFPB is trying to make credit card contracts be written in Plain English. Similarly, regulation law should be written in a way so that the employees of the regulator in question can understand it, and that means you shouldn’t have to have a Ph.D. in a quantitative field and know python.
Algos as tools
Here’s where algorithms may help, although it is still tricky: not in the law itself but in the implementation of the law. So it makes sense that the SEC has algorithms trying to catch insider trading – in fact it’s probably the only way for them to attempt to catch the bad guys. For that matter they should have many more algorithms to catch other kinds of bad guys, for example to catch people with suspicious accounting or consistently optimistic ratings.
In this case proxies are reasonable, but on the other hand it doesn’t translate into law but rather into a ranking of workflow for the people at the regulatory agency. In other words the SEC should use algorithms to decide which cases to pursue and on what timeframe.
Even so, there are plenty of reasons to worry. One could view the “Stop & Frisk” strategy in New York as following an algorithm as well, namely to stop young men in high-crime areas that have “furtive motions”. This algorithm happens to single out many innocent black and latino men.
Similarly, some of the highly touted New York City open data projects amount to figuring out that if you focus on looking for building code violations in high-crime areas, then you get a better hit rate. Again, the consequence of using the algorithm is that poor people are targeted at a higher rate for all sorts of crimes (key quote from the article: “causation is for other people”).
Think about this asymptotically: if you live in a nice neighborhood, the limited police force and inspection agencies never check you out since their algorithms have decided the probability of bad stuff happening is too low to bother. If, on the other hand, you are poor and live in a high-crime area, you get checked out daily by various inspectors, who bust you for whatever.
Said this way, it kind of makes sense that white kids smoke pot at the same rate as black kids but are almost never busted for it.
There are ways to partly combat this problem, as I’ve described before, by using randomization.
It seems to me that we can’t have algorithms directly embedded in laws, because of the highly opaque nature of them together with commonly misaligned incentives. They might be useful as tools for regulators, but the regulators who choose to use internal algorithms need to carefully check that their algorithms don’t have unreasonable and biased consequences, which is really hard.
My buddy Jordan Ellenberg just came out with a fantastic piece in Slate entitled “The Case of the Missing Zeroes: An astonishing act of statistical chutzpah in the Indiana schools’ grade-changing scandal.”
Here are the leading sentences of the piece:
Florida Education Commissioner Tony Bennett resigned Thursday amid claims that, in his former position as superintendent of public instruction in Indiana, he manipulated the state’s system for evaluating school performance. Bennett, a Republican who created an A-to-F grading protocol for Indiana schools as a way to promote educational accountability, is accused of raising the mark for a school operated by a major GOP donor.
Jordan goes on to explain exactly what happened and how that manipulation took place. Turns out it was a pretty outrageous and easy-to-understand lie about missing zeroes which didn’t make any sense. You should read the whole thing, Jordan is a great writer and his fantasy about how he would deal with a student trying the same scam in his calculus class is perfect.
A few comments to make about this story overall.
- First of all, it’s another case of a mathematical model being manipulated for political reasons. It just happens to be a really simple mathematical model in this case, namely a weighted average of scores.
- In other words, the lesson learned for corrupt politicians in the future may well to be sure the formulae are more complicated and thus easier to game.
- Or in other words, let’s think about other examples of this kind of manipulation, where people in power manipulate scores after the fact for their buddies. Where might it be happening now? Look no further than the Value-Added Model for teachers and schools, which literally nobody understands or could prove is being manipulated in any given instance.
- Taking a step further back, let’s remind ourselves that educational accountability models in general are extremely ripe for gaming and manipulation due to their high stakes nature. And the question of who gets the best opportunity to manipulate their scores is, as shown in this example of the GOP-donor-connected school, often a question of who has the best connections.
- In other words, I wonder how much the system can be trusted to give us a good signal on how well schools actually teach (at least how well they teach to the test).
- And if we want that signal to be clear, maybe we should take away the high stakes and literally measure it, with no consequences. Then, instead of punishing schools with bad scores, we could see how they need help.
- The conversation doesn’t profit from our continued crazy high expectations and fundamental belief in the existence of a silver bullet, the latest one being the Kipp Charter Schools – read this reality check if you’re wondering what I’m talking about (hat tip Jordan Ellenberg).
- As any statistician could tell you, any time you have an “educational experiment” involving highly motivated students, parents, and teachers, it will seem like a success. That’s called selection bias. The proof of the pudding lies in the scaling up of the method.
- We need to think longer term and consider how we’re treating good teachers and school administration who have to live under arbitrary and unfair systems. They might just leave.
So yesterday I told you about the cool new visualizations now available on Johan’s Stack Project.
But how do we use these visualizations to infer something about either mathematics or, at the very least, the way we think about mathematics? Here’s one way we thought of with Pieter.
So, there’s a bunch of results, and each of them has its own subgraph of the entire graph which positions that result as the “base node” and shows all the other results which it logically depends on.
And each of those graphs has structure and attributes, the stupidest two of which are the just counts of the nodes and edges. So for each result, we have an ordered pair (#nodes, #edges). What can we infer about mathematics from these pairs?
Here’s a scatter plot of the nodes-vs-edges for each of the 10,445 results (email me if you want to play with this data yourself):
I also put a best-fit line in, just to illustrate that the scatter plot is super linear but not perfectly linear.
So there are a bunch of comments I can make about this, but I’ll limit myself to the following:
- There are a lot of points at (1,0), corresponding to remarks, axioms, beginning lemmas, definitions, and tags for sections.
- As a data person, let me just say that data is never this clean. There’s something going on, some internal structure to these graphs that we should try to understand.
- By “clean” I’m not exactly referring to the fact that things look pretty linear, although that’s weird and we should think about that. What I really mean is that things are so close to the curve that is being approximated. They’re all within a very tight border of this imaginary line. It’s super amazing.
- Let’s pretend it’s just plain straight. Does that make sense, that as graphs get more complex the edges don’t get more dense than some multiple (1.86) of of the number of nodes?
- Kind of: remember, we don’t depict all logical dependency edges, just the ones that are directly referred to in the proof of a result. So right off the bat you are less surprised that the edges aren’t growing quadratically in the number of nodes, even though the number of possible edges is of course quadratic in the number of nodes.
- Think about it this way: assume that every result that requires proof (so, that’s not a (1,0) result) refers to exactly 2 other results in its proof. Then those two child results each correspond to some subgraph of the entire graph, and say their subgraphs each have something like twice as many edges as nodes. Then, ignoring overlap, we’d see two graphs with a 2:1 ratio, then we’d see that parent node, plus two edges leading to each result, which is also a 2:1 ratio, and the disjoint union of all those graphs gives us a large graph with a 2:1 ratio.
- Then if you imagine now allowing the overlap, the ratio goes down a bit on average. In this toy model, the discrepancy between 2.0 and the slope we actually see, 1.86, is a measurement of the collapse of the two child graphs, which can be taken as a proxy for how much the two supporting results overlap as notions.
- Of course, not every result has exactly two children.
- Plus it doesn’t really explain how ridiculously consistent the plot above is. What would?
- If you think about it, the only real explanation of the consistency above is my husband brain.
- In other words, he’s humming along, thinking about stacks, and at some point, when he thinks things have gotten complicated enough, he says to himself “It’s time to wrap this stuff up and call it a result!” and then he does so. That moment, when he’s decided things are getting complicated enough, is very consistent internally to his brain.
- In other words, if someone else created the stacks project, I’d expect to see another kind of plot, possibly also very consistent, but possibly with a different slope.
- Also it’d be interesting to compare this plot to another kind of citation network graph, like the papers in the arXiv. Has anyone made that?
Crossposted on Not Even Wrong.
Here’s a completely biased interview I did with my husband A. Johan de Jong, who has been working with Pieter Belmans on a very cool online math project using d3js. I even made up some of his answers (with his approval).
Q: What is the Stacks Project?
A: It’s an open source textbook and reference for my field, which is algebraic geometry. It builds foundations starting from elementary college algebra and going up to algebraic stacks. It’s a self-contained exposition of all the material there, which makes it different from a research textbook or the experience you’d have reading a bunch of papers.
We were quite neurotic setting it up – everything has a proof, other results are referenced explicitly, and it’s strictly linear, which is to say there’s a strict ordering of the text so that all references are always to earlier results.
Of course the field itself has different directions, some of which are represented in the stacks project, but we had to choose a way of presenting it which allowed for this idea of linearity (of course, any mathematician thinks we can do that for all of mathematics).
Q: How has the Stacks Project website changed?
A: It started out as just a place you could download the pdf and tex files, but then Pieter Belmans came on board and he added features such as full text search, tag look-up, and a commenting system. In this latest version, we’ve added a whole bunch of features, but the most interesting one is the dynamic generation of dependency graphs.
We’ve had some crude visualizations for a while, and we made t-shirts from those pictures. I even had this deal where, if people found mathematical mistakes in the Stacks Project, they’d get a free t-shirt, and I’m happy to report that I just last week gave away my last t-shirt. Here’s an old picture of me with my adorable son (who’s now huge).
Q: Talk a little bit about the new viz.
A: First a word about the tags, which we need to understand the viz.
Every mathematical result in the Stacks Project has a “tag”, which is a four letter code, and which is a permanent reference for that result, even as other results are added before or after that one (by the way, Cathy O’Neil figured this system out).
The graphs show the logical dependencies between these tags, represented by arrows between nodes. You can see this structure in the above picture already.
So for example, if tag ABCD refers to Zariski’s Main Theorem, and tag ADFG refers to Nakayama’s Lemma, then since Zariski depends on Nakayama, there’s a logical dependency, which means the node labeled ABCD points to the node labeled ADFG in the entire graph.
Of course, we don’t really look at the entire graph, we look at the subgraph of results which a given result depends on. And we don’t draw all the arrows either, we only draw the arrows corresponding to direct references in the proofs. Which is to say, in the subgraph for Zariski, there will be a path from node ABCD to node ADFG, but not necessarily a direct link.
Q: Can we see an example?
Let’s move to an example for result 01WC, which refers to the proof that “a locally projective morphism is proper”.
First, there are two kinds of heat maps. Here’s one that defines distance as the maximum (directed) distance from the root node. In other words, how far down in the proof is this result needed? In this case the main result 01WC is bright red with a black dotted border, and any result that 01WC depends on is represented as a node. The edges are directed, although the arrows aren’t drawn, but you can figure out the direction by how the color changes. The dark blue colors are the leaf nodes that are farthest away from the root.
Another way of saying this is that the redder results are the results that are closer to it in meaning and sophistication level.
Note if we had defined the distance as the minimum distance from the root node (to come soon hopefully), then we’d have a slightly different and also meaningful way of thinking about “redness” as “relevance” to the root node.
This is a screenshot but feel free to play with it directly here. For all of the graphs, hovering over a result will cause the statement of the result to appear, which is awesome.
Next, let’s look at another kind of heat map where the color is defined as maximum distance from some leaf note in the overall graph. So dark blue nodes are basic results in algebra, sheaves, sites, cohomology, simplicial methods, and other chapters. The link is the same, you can just toggle between the different metric.
Next we delved further into how results depend on those different topics. Here, again for the same result, we can see the extent to which that result depends on the different on results from the various chapters. If you scroll over the nodes you can see more details. This is just a screenshot but you can play with it yourself here and you can collapse it in various ways corresponding to the internal hierarchy of the project.
Finally, we have a way of looking at the logical dependency graph directly, where result node is labeled with a tag and colored by “type”: whether it’s a lemma, proposition, theorem, or something else, and it also annotates the results which have separate names. Again a screenshot but play with it here, it rotates!
Check out the whole project here, and feel free to leave comments using the comment feature!
Not much time because I’m giving a keynote talk at the PyData 2013 conference in Cambridge today, which is being held at the Microsoft NERD conference center.
It’s gonna be videotaped so I’ll link to that when it’s ready.
My title is “Storytelling With Data” but for whatever reason on the schedule handed out yesterday the name had been changed to “Scalable Storytelling With Data”. I’m thinking of addressing this name change in my talk – one of the points of the talk, in fact, is that with great tools, we don’t need to worry too much about the scale.
Plus since it’s Sunday morning I’m going to make an effort to tie my talk into an old testament story, which is totally bizarre since I’m not at all religious but for some reason it feels right. Please wish me luck.
I’ve blogged before about how I find it outrageous that the credit scoring models are proprietary, considering the impact they have on so many lives.
The argument given for keeping them secret is that otherwise people would game the models, but that really doesn’t make sense.
After all, the models that the big banks have to deal with through regulation aren’t secret, and they game those models all the time. It’s one of the main functions of the banks, in fact, to figure out how to game the models. So either we don’t mind gaming or we don’t hold up our banks to the same standards as our citizens.
Plus, let’s say the models were open and people started gaming the credit score models – what would that look like? A bunch of people paying their electricity bill on time?
Let’s face it: the real reason the models are secret is that the companies who set them up make more money that way, pretending to have some kind of secret sauce. What they really have, of course, is a pretty simple model and access to an amazing network of up-to-date personal financial data, as well as lots of clients.
Their fear is that, if their model gets out, anyone could start a credit scoring agency, but actually it wouldn’t be so easy – if I wanted to do it, I’d have to get all that personal data on everyone. In fact, if I could get all that personal data on everyone, including the historical data, I could easily build a credit scoring model.
So anyhoo, it’s all about money, that and the fact that we’re living under the assumption that it’s appropriate for credit scoring companies to wield all this power over people’s lives, including their love lives.
It’s like we have a secondary system of secret laws where we don’t actually get to see the rules, nor do we get to point out mistakes or reasonably refute them. And if you’re thinking “free credit report,” let’s be clear that that only tells you what data goes in to the model, it doesn’t tell you how it’s used.
As it turns out, though, it’s now more than like a secondary system of laws – it’s become embedded in our actual laws. Somehow the proprietary credit scoring company Equifax is now explicitly part of our healthcare laws. From this New York Times article (hat tip Matt Stoller):
Federal officials said they would rely on Equifax — a company widely used by mortgage lenders, social service agencies and others — to verify income and employment and could extend the initial 12-month contract, bringing its potential value to $329.4 million over five years.
Contract documents show that Equifax must provide income information “in real time,” usually within a second of receiving a query from the federal government. Equifax says much of its information comes from data that is provided by employers and updated each payroll period.
Under the contract, Equifax can use sources like credit card applications but must develop a plan to indicate the accuracy of data and to reduce the risk of fraud.
Thanks Equifax, I guess we’ll just trust you on all of this.