Archive for the ‘data science’ Category

Sometimes, The World Is Telling You To Polish Up Your LinkedIn Profile

September 27, 2013 9 comments

The above title was stolen verbatim from an excellent essay by Dan Milstein on the Hut 8 Labs blog (hat tip Deane Yang). The actual title of the essay is “No Deadlines For You! Software Dev Without Estimates, Specs or Other Lies”

He wrote the essay about how, as an engineer, you can both make yourself invaluable to your company and avoid meaningless and arbitrary deadlines on your projects. So, he’s an engineer, but the advice he gives is surprisingly close to the advice I was trying to give on Monday night when I spoke at the Columbia Data Science Society (my slides are here, by the way). More on that below.

Milstein is an engaging writer. He wrote a book called Coding, Fast and Slow, which I now feel like reading just because I enjoy his insights and style. Here’s a small excerpt:

Let’s say you’ve started at a new job, leading a small team of engineers. On your first day, an Important Person comes by your desk. After some welcome-to-the-business chit chat, he/she hands you a spec. You look it over—it describes a new report to add to the company’s product. Of course, like all specs, it’s pretty vague, and, worse, it uses some jargon you’ve heard around the office, but haven’t quite figured out yet.

You look up from the spec to discover that the Important Person is staring at you expectantly: “So, <Your Name>, do you think you and your team can get that done in 3 months?”

What do you do?

Here are some possible approaches (all of which I’ve tried… and none of which has ever worked out well):

  • Immediately try to flesh out the spec in more detail

“How are we summing up this number? Is this piece of data required? What does <jargon word> mean, here, exactly?”

  • Stall, and take the spec to your new team

“Hmm. Hmm. Hmmmmmmmm. Do you think, um, Bob (that’s his name, right?) has the best handle on these kinds of things?”

  • Give the spec a quick skim, and then listen to the seductive voice of System I

“Sure, yeah, 3 months sounds reasonable” (OMG, I wish this wasn’t something I’ve doneSO MANY TIMES).

  • Push back aggressively

“I read this incredibly convincing blog post 1 about how it’s impossible to commit to deadlines for software projects, sorry, I just can’t do that.”

He then goes on to write that very blog post. In it, he explains what you should do, which is to learn why the project has been planned in the first place, and what the actual business question is, so you have full context for your job and you know what it means to the company for this to succeed or fail.

The way I say this, regularly, to aspiring data scientists I run into, is that you are often given a data science question that’s been filtered from a business question, through a secondary person who has some idea that they’ve molded that business question into a “mathematical question,” and they want you to do the work of answering that question, under some time constraint and resource constraints that they’ve also picked out of the air.

But often that process has perverted the original aims – often because proxies have magically appeared in the place of the original objects of interest – and it behooves a data scientist who doesn’t want to be working on the wrong problem to go to the original source and verify that their work is answering a vital business question, that they’re optimizing for the right thing, and that they understand the actual constraints (like deadlines but also resources) rather than the artificial constraints made up by whoever is in charge of telling the nerds what to do.

In other words, I suggest that each data scientist “becomes part business person,” and talks to the business owner of the given problem directly until they’re sure they know what needs to get done with data.

Milstein has a bunch of great tips on how to go through with this process, including:

  1. Counting on people’s enjoyment of hearing their own ideas repeated and fears understood,
  2. Using a specific template when talking to Important People, namely a) “I’m going to echo that back, make sure I understand”, b) echo it back, c) “Do I have that right?”.
  3. To always think and discuss your work in terms of risks and information for the business. Things like, you need this information to answer this risk. The point here is it always stays relevant to the business people while you do your technical thing. This means always keeping a finger on the pulse of the business problem.
  4. Framing choices for the Important Person in terms of clear trade-offs of risk, investments, and completion. This engages the business in what your process is in a completely understandable way.
  5. Finally, if your manager doesn’t let you talk directly to the Important People in the business, and you can’t convince your manager to change his or her mind, then you might wanna polish up your LinkedIn profile, because otherwise you are fated to work on failed projects. Great advice.
Categories: data science

A Code of Conduct for data scientists from the Bellagio Fellows

September 25, 2013 3 comments

The 2013 PopTech & Rockefeller Foundation Bellagio Fellows – Kate CrawfordPatrick MeierClaudia PerlichAmy LuersGustavo 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.

Upcoming talks

September 23, 2013 4 comments

Tomorrow evening I’m meeting with the Columbia Data Science Society and talking to them – who as I understand it are mostly engineers – about “how to think like a data scientist”.

On October 11th I’ll be in D.C. sitting on a panel discussion organized by the Americans for Financial Reform. It’s part of a day-long event on the topic of transparency in financial regulation. The official announcement isn’t out yet but I’ll post it here as soon as I can. I’ll be giving my two cents on what mathematical tools can do and cannot do with respect to this stuff.

On October 16th I’ll again be in D.C. giving a talk in the MAA Distinguished Lecture Series to a mostly high school math teacher audience. My talk is entitled, “Start Your Own Netflix”.

Finally, I’m going to Harvard on October 30th to give a talk in their Applied Statistics Workshop series. I haven’t figured out exactly what I’m talking about but it will be something nerdy and skeptical.

Categories: data science, finance

I’d like you to eventually die

September 22, 2013 24 comments

Google has formally thrown their hat into the “rich people should never die” arena, with an official announcement of their new project called Calico, “a new company that will focus on health and well-being, in particular the challenge of aging and associated diseases”. Their plan is to use big data and genetic research to avoid aging.

I saw this coming when they hired Ray Kurzweil. Here’s an excerpt from my post:

A few days ago I read a New York Times interview of Ray Kurzweil, who thinks he’s going to live forever and also claims he will cure cancer if and when he gets it (his excuse for not doing it in his spare time now: “Well, I mean, I do have to pick my priorities. Nobody can do everything.”). He also just got hired at Google.

Here’s the thing. We need people to die. Our planet cannot sustain all the people currently alive as well as all the people who are going to someday be born. Just not gonna happen. Plus, it would be a ridiculously boring place to live. Think about how boring it is already for young people to be around old people. I bore myself around my kids, and I’m only 30 years older than they are.

And yes, it’s tragic when someone we love actually becomes one of those people whose time has come, especially if they’re young and especially if it seemed preventable. For that matter, I’m all for figuring out how to improve the quality of life for people.

But the idea that we’re going to figure out how to keep alive a bunch of super rich advertising executives just doesn’t seem right – because, let’s face it, there will have to be a way to choose who lives and who dies, and I know who is at the top of that list – and I for one am not on board with the plan. Larry Page, Tim Cook, and Ray Kurzweil: I’d really like it if you eventually died.

On the other hand, I’m not super worried about this plan coming through either. Big data can do a lot but it’s not going to make people live forever. Or let’s say it another way: if they can use big data to make people live forever, they can also use big data to convince me that super special rich white men living in Silicon Valley should take up resources and airtime for the rest of eternity.

Categories: data science, rant

The bursting of the big data bubble

September 20, 2013 42 comments

It’s been a good ride. I’m not gonna lie, it’s been a good time to be a data whiz, a quant-turned-data scientist. I get lots of attention and LinkedIn emails just for my title and my math Ph.D., and it’s flattering. But all of that is going to change, starting now.

You see, there are some serious headwinds. They started a while ago but they’re picking up speed, and the magical wave of hype propelling us forward is giving way. I can tell, I’ve got a nose for sinking ships and sailing metaphors.

First, the hype and why it’s been so strong.

It seems like data and the ability to use data is the secret sauce in so many of the big success stories. Look at Google. They managed to think of the entire web as their data source, and have earned quite a bit of respect and advertising money for their chore of organizing it like a huge-ass free library for our benefit. That took some serious data handling and modeling know-how.

We humans are pretty good at detecting patterns, so after a few companies made it big with the secret data sauce, we inferred that, when you take a normal tech company and sprinkle on data, you get the next Google.

Next, a few reasons it’s unsustainable

Most companies don’t have the data that Google has, and can never hope to cash in on stuff at the scale of the ad traffic that Google sees. Even so, there are lots of smaller but real gains that lots of companies – but not all – could potentially realize if they collected the right kind of data and had good data people helping them.

Unfortunately, this process rarely actually happens the right way, often because the business people ask their data people the wrong questions to being with, and since they think of their data people as little more than pieces of software – data in, magic out – they don’t get their data people sufficiently involved with working on something that data can address.

Also, since there are absolutely no standards for what constitutes a data scientist, and anyone who’s taken a machine learning class at college can claim to be one, the data scientists walking around often have no clue how to actually form the right questions to ask anyway. They are lopsided data people, and only know how to answer already well-defined questions like the ones that Kaggle comes up with. That’s less than half of what a good data scientist does, but people have no idea what a good data scientist does.

Plus, it’s super hard to accumulate hard evidence that you have a crappy data science team. If you’ve hired one or more unqualified data scientists, how can you tell? They still might be able to implement crappy models which don’t answer the right question, but in order to see that you’d need to also have a good data scientist who implements a better solution to the right question. But you only have one. It’s a counterfactual problem.

Here’s what I see happening. People have invested some real money in data, and they’ve gotten burned with a lack of medium-term results. Now they’re getting impatient for proof that data is an appropriate place to invest what little money their VC’s have offered them. That means they want really short-term results, which means they’re lowballing data science expertise, which means they only attract people who’ve taken one machine learning class and fancy themselves experts.

In other words, data science expertise has been commodified, and it’s a race to the bottom. Who will solve my business-critical data problem on a short-term consulting basis for less than $5000? Less than $4000?

What’s next?

There really is a difference between A) crude models that someone constructs not really knowing what they’re doing and B) thoughtful models which gain an edge along the margin. It requires someone who actually knows what they’re doing to get the latter kind of model. But most people are unaware of even the theoretical difference between type A and type B models, nor would they recognize which type they’ve got once they get one.

Even so, over time, type B models outperform type A models, and if you care enough about the marginal edge between the two types, say because you’re in a competitive environment, then you will absolutely need type B to make money. And by the way, if you don’t care about that marginal edge, then by all means you should use a type A solution. But you should at least know the difference and make that choice deliberately.

My forecast is that, once the hype wave of big data is dead and gone, there will emerge reasonable standards of what a data scientist should actually be able to do, and moreover a standard of when and how to hire a good one. It’ll be a rubrik, and possibly some tests, of both problem solving and communication.

Personally, I’m looking forward to a more reasonable and realistic vision of how data and data expertise can help with things. I might have to change my job title, but I’m used to it.

Categories: data science

Interactive scoring models: why hasn’t this happened yet?

September 12, 2013 10 comments

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.

Categories: data science, modeling

Working in the NYC Mayor’s Office

September 10, 2013 7 comments

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.

Note the expiration date.

Note the expiration date.

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.

Categories: data science, modeling

The art of definition

Definitions are basic objects in mathematics. Even so, I’ve never seen the art of definition explicitly taught, and I have rarely seen the need for a definition explicitly discussed.

Have you ever noticed how damn hard it is to make a good definition and yet how utterly useful a good definition can be?

The basic definitions inform the research of any field, and a good definition will lead to better theorems than a bad one. If you get them right, if you really nail down the definition, then everything works out much more cleanly than otherwise.

So for example, it doesn’t make sense to work in algebraic geometry without the concepts of affine and projective space, and varieties, and schemes. They are to algebraic geometry like circles and triangles are to elementary geometry. You define your objects, then you see how they act and how they interact.

I saw first hand how a good definition improves clarity of thought back in grad school. I was lucky enough to talk to John Tate (my mathematical hero) about my thesis, and after listening to me go on for some time with a simple object but complicated proofs, he suggested that I add an extra sentence to my basic object, an assumption with a fixed structure.

This gave me a bit more explaining to do up front – but even there added intuition – and greatly simplified the statement and proofs of my theorems. It also improved my talks about my thesis. I could now go in and spend some time motivating the definition, and then state the resulting theorem very cleanly once people were convinced.

Another example from my husband’s grad seminar this semester: he’s starting out with the concept of triangulated categories coming from Verdier’s thesis. One mysterious part of the definition involves the so-called “octahedral axiom,” which mathematicians have been grappling with ever since it was invented. As far as Johan tells it, people struggle with why it’s necessary but not that it’s necessary, or at least something very much like it. What’s amazing is that Verdier managed to get it right when he was so young.

Why? Because definition building is naturally iterative, and it can take years to get it right. It’s not an obvious process. I have no doubt that many arguments were once fought over whether the most basic definitions, although I’m no historian. There’s a whole evolutionary struggle that I can imagine could take place as well – people could make the wrong definition, and the community would not be able to prove good stuff about that, so it would eventually give way to stronger, more robust definitions. Better to start out carefully.

Going back to that. I think it’s strange that the building up of definitions is not explicitly taught. I think it’s a result of the way math is taught as if it’s already known, so the mystery of how people came up with the theorems is almost hidden, never mind the original objects and questions about them. For that matter, it’s not often discussed why we care whether a given theorem is important, just whether it’s true. Somehow the “importance” conversations happen in quiet voices over wine at the seminar dinners.

Personally, I got just as much out of Tate’s help with my thesis as anything else about my thesis. The crystalline focus that he helped me achieve with the correct choice of the “basic object of study” has made me want to do that every single time I embark on a project, in data science or elsewhere.

Simons Center for Data Analysis

Has anyone heard of the new Simons Center for Data Analysis?

Neither had I until just now. But some guy named Leslie Greengard, who is a distinguished mathematician and computer scientist, just got named its director (hat tip Peter Woit).

Please inform me if you know more about this center. I got nothing except this tiny description:

As SCDA’s director, Greengard will build and lead a team of scientists committed to analyzing large-scale, rich data sets and to developing innovative mathematical methods to examine such data.

Categories: data science, news

Experimentation in education – still a long way to go

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.


Short your kids, go long your neighbor: betting on people is coming soon

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:

  1. You get to see your own profile, here, starting in 2 days, but only your own.
  2. 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.
  3. And they’re doing it so that people have clarity on their business.
  4. 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.
  5. 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.
  6. 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.

Summers’ Lending Club makes money by bypassing the Equal Credit Opportunity Act

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.

Categories: data science, finance, modeling rips off poor people; let’s take control of our online personas

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 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, 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 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 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.

Categories: data science, modeling, rant

When big data goes bad in a totally predictable way

Three quick examples this morning in the I-told-you-so category. I’d love to hear Kenneth Neil Cukier explain how “objective” data science is when confronted with this stuff.

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 had added a “diversity questionnaire” to the site.  This gives an applicant the opportunity to identify their sex and race to potential employers. 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.

Categories: data science, modeling

What’s the difference between big data and business analytics?

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.

Categories: data science, modeling

Larry Summers and the Lending Club

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.

Categories: data science, finance, modeling

Should lawmakers use algorithms?

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.

Categories: data science, finance, modeling

PyData talk today

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.

The Stop and Frisk sleight of hand

I’m finishing up an essay called “On Being a Data Skeptic” in which I catalog different standard mistakes people make with data – sometimes unintentionally, sometimes intentionally.

It occurred to me, as I wrote it, and as I read the various press conferences with departing mayor Bloomberg and Police Commissioner Raymond Kelly when they addressed the Stop and Frisk policy, that they are guilty of making one of these standard mistakes. Namely, they use a sleight of hand with respect to the evaluation metric of the policy.

Recall that an evaluation metric for a model is the way you decide whether the model works. So if you’re predicting whether someone would like a movie, you should go back and check whether your recommendations were good, and revise your model if not. It’s a crucial part of the model, and a poor choice for it can have dire consequences – you could end up optimizing to the wrong thing.

[Aside: as I've complained about before, the Value Added Model for teachers doesn't have an evaluation method of record, which is a very bad sign indeed about the model. And that's a Bloomberg brainchild as well.]

So what am I talking about?

Here’s the model: stopping and frisking suspicious-looking people in high-crime areas will improve the safety and well-being of the city as a whole.

Here’s Bloomberg/Kelly’s evaluation method: the death rate by murder has gone down in New York during the policy. However, that rate is highly variable and depends just as much on whether there’s a crack epidemic going on as anything else. Or maybe it’s improved medical care. Truth is people don’t really know. In any case ascribing credit for the plunging death rate to Stop and Frisk is a tenuous causal argument. Plus since Stop and Frisk events have decreased drastically recently, we haven’t seen the murder rate shoot up.

Here’s another possible evaluation method: trust in the police. And considering that 400,000 innocent black and Latino New Yorkers were stopped last year under this policy (here are more stats), versus less than 50,000 whites, and most of them were young men, it stands to reason that the average young minority male feels less trust towards police than the average young white male. In fact, this is an amazing statistic put together by the NYCLU from 2011:

The number of stops of young black men exceeded the entire city population of young black men (168,126 as compared to 158,406).

If I’m a black guy I have an expectation of getting stopped and frisked at least once per year. How does that make me trust cops?

Let’s choose an evaluation method closer to what we can actually control, and let’s optimize to it.

Update: a guest columnist fills in for David Brooks, hopefully not for the last time, and gives us his take on Kelly, Obama, and racial profiling.

Categories: data science, modeling, rant

The creepy mindset of online credit scoring

Usually I like to think through abstract ideas – thought experiments, if you will – and not get too personal. I take exceptions for certain macroeconomists who are already public figures but most of the time that’s it.

Here’s a new category of people I’ll call out by name: CEO’s who defend creepy models using the phrase “People will trade their private information for economic value.”

That’s a quote of Douglas Merrill, CEO of Zest Finance, taken from this video taken at a recent data conference in Berkeley (hat tip Rachel Schutt). It was a panel discussion, the putative topic of which was something like “Attacking the structure of everything”, whatever that’s supposed to mean (I’m guessing it has something to do with being proud of “disrupting shit”).

Do you know the feeling you get when you’re with someone who’s smart, articulate, who probably buys organic eggs from a nice farmer’s market, but who doesn’t expose an ounce of sympathy for people who aren’t successful entrepreneurs? When you’re with someone who has benefitted so entirely and so consistently from the system that they have an almost religious belief that the system is perfect and they’ve succeeded through merit alone?

It’s something in between the feeling that, maybe you’re just naive because you’ve led such a blessed life, or maybe you’re actually incapable of human empathy, I don’t know which because it’s never been tested.

That’s the creepy feeling I get when I hear Douglas Merrill speak, but it actually started earlier, when I got the following email almost exactly one year ago via LinkedIn:

Hi Catherine,

Your profile looked interesting to me.

I’m seeking stellar, creative thinkers like you, for our team in Hollywood, CA. If you would consider relocating for the right opportunity, please read on.

You will use your math wizardry to develop radically new methods for data access, manipulation, and modeling. The outcome of your work will result in game-changing software and tools that will disrupt the credit industry and better serve millions of Americans.

You would be working alongside people like Douglas Merrill – the former CIO of Google – along with a handful of other ex-Googlers and Capital One folks. More info can be found on our LinkedIn company profile or at

At ZestFinance we’re bringing social responsibility to the consumer loan industry.

Do you have a few moments to talk about this? If you are not interested, but know someone else who might be a fit, please send them my way!

I hope to hear from you soon. Thank you for your time.


Wow, let’s “better serve millions of Americans” through manipulation of their private data, and then let’s call it being socially responsible! And let’s work with Capital One which is known to be practically a charity.


Message to ZestFinance: “getting rich with predatory lending” doesn’t mean “being socially responsible” unless you have a really weird definition of that term.

Going back to the video, I have a few more tasty quotes from Merrill:

  1. First when he’s describing how he uses personal individual information scraped from the web: “All data is credit data.”
  2. Second, when he’s comparing ZestFinance to FICO credit scoring: “Context is developed by knowing thousands of things about you. I know you as a person, not just you via five or six variables.”

I’d like to remind people that, in spite of the creepiness here, and the fact that his business plan is a death spiral of modeling, everything this guy is talking about is totally legal. And as I said in this post, I’d like to see some pushback to guys like Merrill as well as to the NSA.

Categories: data science, rant

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