As a fat person, I’ve dealt with a lot of public shaming in my life. I’ve gotten so used to it, I’m more an observer than a victim most of the time. That’s kind of cool because it allows me to think about it abstractly.
I’ve come up with three dimensions for thinking about this issue.
- When is shame useful?
- When is it appropriate?
- When does it help solve a problem?
Note it can be useful even if it doesn’t help solve a problem – one of the characteristics of shame is that the person doing the shaming has broken off all sense of responsibility for whatever the issue is, and sometimes that’s really the only goal. If the shaming campaign is effective, the shamed person or group is exhibited as solely responsible, and the shamer does not display any empathy. It hasn’t solved a problem but at least it’s clear who’s holding the bag.
The lack of empathy which characterizes shaming behavior makes it very easy to spot. And extremely nasty.
Let’s look at some examples of shaming through this lens:
Useful but not appropriate, doesn’t solve a problem
Example 1) it’s both fat kids and their parents who are to blame for childhood obesity:
Example 2) It’s poor mothers that are to blame for poverty:
These campaigns are not going to solve any problems, but they do seem politically useful – a way of doubling down on the people suffering from problems in our society. Not only will they suffer from them, but they will also be blamed for them.
Inappropriate, not useful, possibly solving a short-term discipline problem
Hey parents: shaming your kids might solve your short-term problem of having independent-minded kids, but it doesn’t lead to long-term confidence and fulfillment.
Appropriate, useful, solves a problem
Here’s when shaming is possibly appropriate and useful and solves a problem: when there have been crimes committed that affect other people needlessly or carelessly, and where we don’t want to let it happen again.
For example, the owner of the Bangladeshi factory which collapsed, killing more than 1,000 people got arrested and publicly shamed. This is appropriate, since he knowingly put people at risk in a shoddy building and added three extra floors to improve his profits.
Note shaming that guy isn’t going to bring back those dead people, but it might prevent other people from doing what he did. In that sense it solves the problem of seemingly nonexistent safety codes in Bangladesh, and to some extent the question of how much we Americans care about cheap clothes versus conditions in factories which make our clothes. Not completely, of course. Update: Major Retailers Join Plan for Greater Safety in Bangladesh
Another example of appropriate shame would be some of the villains of the financial crisis. We in Alt Banking did our best in this regard when we made the 52 Shades of Greed card deck. Here’s Robert Rubin:
I’m no expert on this stuff, but I do have a way of looking at it.
One thing about shame is that the people who actually deserve shame are not particularly susceptible to feeling it (I saw that first hand when I saw Ina Drew in person last month, which I wrote about here). Some people are shameless.
That means that shame, whatever its purpose, is not really about making an individual change their behavior. Shame is really more about setting the rules of society straight: notifying people in general about what’s acceptable and what’s not.
From my perspective, we’ve shown ourselves much more willing to shame poor people, fat people, and our own children than to shame the actual villains who walk among us who deserve such treatment.
Shame on us.
I recently read an article off the newsstand called The Rise of Big Data.
It was written by Kenneth Neil Cukier and Viktor Mayer-Schoenberger and it was published in the May/June 2013 edition of Foreign Affairs, which is published by the Council on Foreign Relations (CFR). I mention this because CFR is an influential think tank, filled with powerful insiders, including people like Robert Rubin himself, and for that reason I want to take this view on big data very seriously: it might reflect the policy view before long.
And if I think about it, compared to the uber naive view I came across last week when I went to the congressional hearing about big data and analytics, that would be good news. I’ll write more about it soon, but let’s just say it wasn’t everything I was hoping for.
At least Cukier and Mayer-Schoenberger discuss their reservations regarding “big data” in this article. To contrast this with last week, it seemed like the only background material for the hearing, at least for the congressmen, was the McKinsey report talking about how sexy data science is and how we’ll need to train an army of them to stay competitive.
So I’m glad it’s not all rainbows and sunshine when it comes to big data in this article. Unfortunately, whether because they’re tied to successful business interests, or because they just haven’t thought too deeply about the dark side, their concerns seem almost token, and their examples bizarre.
The article is unfortunately behind the pay wall, but I’ll do my best to explain what they’ve said.
First they discuss the concept of datafication, and their example is how we quantify friendships with “likes”: it’s the way everything we do, online or otherwise, ends up recorded for later examination in someone’s data storage units. Or maybe multiple storage units, and maybe for sale.
They formally define later in the article as a process:
… taking all aspect of life and turning them into data. Google’s augmented-reality glasses datafy the gaze. Twitter datafies stray thoughts. LinkedIn datafies professional networks.
Datafication is an interesting concept, although as far as I can tell they did not coin the word, and it has led me to consider its importance with respect to intentionality of the individual.
Here’s what I mean. We are being datafied, or rather our actions are, and when we “like” someone or something online, we are intending to be datafied, or at least we should expect to be. But when we merely browse the web, we are unintentionally, or at least passively, being datafied through cookies that we might or might not be aware of. And when we walk around in a store, or even on the street, we are being datafied in an completely unintentional way, via sensors or Google glasses.
This spectrum of intentionality ranges from us gleefully taking part in a social media experiment we are proud of to all-out surveillance and stalking. But it’s all datafication. Our intentions may run the gambit but the results don’t.
They follow up their definition in the article, once they get to it, with a line that speaks volumes about their perspective:
Once we datafy things, we can transform their purpose and turn the information into new forms of value
But who is “we” when they write it? What kinds of value do they refer to? As you will see from the examples below, mostly that translates into increased efficiency through automation.
So if at first you assumed they mean we, the American people, you might be forgiven for re-thinking the “we” in that sentence to be the owners of the companies which become more efficient once big data has been introduced, especially if you’ve recently read this article from Jacobin by Gavin Mueller, entitled “The Rise of the Machines” and subtitled “Automation isn’t freeing us from work — it’s keeping us under capitalist control.” From the article (which you should read in its entirety):
In the short term, the new machines benefit capitalists, who can lay off their expensive, unnecessary workers to fend for themselves in the labor market. But, in the longer view, automation also raises the specter of a world without work, or one with a lot less of it, where there isn’t much for human workers to do. If we didn’t have capitalists sucking up surplus value as profit, we could use that surplus on social welfare to meet people’s needs.
The big data revolution and the assumption that N=ALL
According to Cukier and Mayer-Schoenberger, the Big Data revolution consists of three things:
- Collecting and using a lot of data rather than small samples.
- Accepting messiness in your data.
- Giving up on knowing the causes.
They describe these steps in rather grand fashion, by claiming that big data doesn’t need to understand cause because the data is so enormous. It doesn’t need to worry about sampling error because it is literally keeping track of the truth. The way the article frames this is by claiming that the new approach of big data is letting “N = ALL”.
But here’s the thing, it’s never all. And we are almost always missing the very things we should care about most.
So for example, as this InfoWorld post explains, internet surveillance will never really work, because the very clever and tech-savvy criminals that we most want to catch are the very ones we will never be able to catch, since they’re always a step ahead.
Even the example from their own article, election night polls, is itself a great non-example: even if we poll absolutely everyone who leaves the polling stations, we still don’t count people who decided not to vote in the first place. And those might be the very people we’d need to talk to to understand our country’s problems.
Indeed, I’d argue that the assumption we make that N=ALL is one of the biggest problems we face in the age of Big Data. It is, above all, a way of excluding the voices of people who don’t have the time or don’t have the energy or don’t have the access to cast their vote in all sorts of informal, possibly unannounced, elections.
Those people, busy working two jobs and spending time waiting for buses, become invisible when we tally up the votes without them. To you this might just mean that the recommendations you receive on Netflix don’t seem very good because most of the people who bother to rate things are Netflix are young and have different tastes than you, which skews the recommendation engine towards them. But there are plenty of much more insidious consequences stemming from this basic idea.
Another way in which the assumption that N=ALL can matter is that it often gets translated into the idea that data is objective. Indeed the article warns us against not assuming that:
… we need to be particularly on guard to prevent our cognitive biases from deluding us; sometimes, we just need to let the data speak.
And later in the article,
In a world where data shape decisions more and more, what purpose will remain for people, or for intuition, or for going against the facts?
This is a bitch of a problem for people like me who work with models, know exactly how they work, and know exactly how wrong it is to believe that “data speaks”.
I wrote about this misunderstanding here, in the context of Bill Gates, but I was recently reminded of it in a terrifying way by this New York Times article on big data and recruiter hiring practices. From the article:
“Let’s put everything in and let the data speak for itself,” Dr. Ming said of the algorithms she is now building for Gild.
If you read the whole article, you’ll learn that this algorithm tries to find “diamond in the rough” types to hire. A worthy effort, but one that you have to think through.
Why? If you, say, decided to compare women and men with the exact same qualifications that have been hired in the past, but then, looking into what happened next you learn that those women have tended to leave more often, get promoted less often, and give more negative feedback on their environments, compared to the men, your model might be tempted to hire the man over the woman next time the two showed up, rather than looking into the possibility that the company doesn’t treat female employees well.
In other words, ignoring causation can be a flaw, rather than a feature. Models that ignore causation can add to historical problems instead of addressing them. And data doesn’t speak for itself, data is just a quantitative, pale echo of the events of our society.
Some cherry-picked examples
One of the most puzzling things about the Cukier and Mayer-Schoenberger article is how they chose their “big data” examples.
One of them, the ability for big data to spot infection in premature babies, I recognized from the congressional hearing last week. Who doesn’t want to save premature babies? Heartwarming! Big data is da bomb!
But if you’re going to talk about medicalized big data, let’s go there for reals. Specifically, take a look at this New York Times article from last week where a woman traces the big data footprints, such as they are, back in time after receiving a pamphlet on living with Multiple Sclerosis. From the article:
Now she wondered whether one of those companies had erroneously profiled her as an M.S. patient and shared that profile with drug-company marketers. She worried about the potential ramifications: Could she, for instance, someday be denied life insurance on the basis of that profile? She wanted to track down the source of the data, correct her profile and, if possible, prevent further dissemination of the information. But she didn’t know which company had collected and shared the data in the first place, so she didn’t know how to have her entry removed from the original marketing list.
Two things about this. First, it happens all the time, to everyone, but especially to people who don’t know better than to search online for diseases they actually have. Second, the article seems particularly spooked by the idea that a woman who does not have a disease might be targeted as being sick and have crazy consequences down the road. But what about a woman is actually is sick? Does that person somehow deserve to have their life insurance denied?
The real worries about the intersection of big data and medical records, at least the ones I have, are completely missing from the article. Although they did mention that ”improving and lowering the cost of health care for the world’s poor” inevitable will lead to “necessary to automate some tasks that currently require human judgment.” Increased efficiency once again.
To be fair, they also talked about how Google tried to predict the flu in February 2009 but got it wrong. I’m not sure what they were trying to say except that it’s cool what we can try to do with big data.
Also, they discussed a Tokyo research team that collects data on 360 pressure points with sensors in a car seat, “each on a scale of 0 to 256.” I think that last part about the scale was added just so they’d have more numbers in the sentence – so mathematical!
And what do we get in exchange for all these sensor readings? The ability to distinguish drivers, so I guess you’ll never have to share your car, and the ability to sense if a driver slumps, to either “send an alert or atomatically apply brakes.” I’d call that a questionable return for my investment of total body surveillance.
Big data, business, and the government
Make no mistake: this article is about how to use big data for your business. It goes ahead and suggests that whoever has the biggest big data has the biggest edge in business.
Of course, if you’re interested in treating your government office like a business, that’s gonna give you an edge too. The example of Bloomberg’s big data initiative led to efficiency gain (read: we can do more with less, i.e. we can start firing government workers, or at least never hire more).
As for regulation, it is pseudo-dealt with via the discussion of market dominance. We are meant to understand that the only role government can or should have with respect to data is how to make sure the market is working efficiently. The darkest projected future is that of market domination by Google or Facebook:
But how should governments apply antitrust rules to big data, a market that is hard to define and is constantly changing form?
In particular, no discussion of how we might want to protect privacy.
Big data, big brother
I want to be fair to Cukier and Mayer-Schoenberger, because they do at least bring up the idea of big data as big brother. Their topic is serious. But their examples, once again, are incredibly weak.
Should we find likely-to-drop-out boys or likely-to-get-pregnant girls using big data? Should we intervene? Note the intention of this model would be the welfare of poor children. But how many models currently in production are targeting that demographic with that goal? Is this in any way at all a reasonable example?
Here’s another weird one: they talked about the bad metric used by US Secretary of Defense Robert McNamara in the Viet Nam War, namely the number of casualties. By defining this with the current language of statistics, though, it gives us the impression that we could just be super careful about our metrics in the future and: problem solved. As we experts in data know, however, it’s a political decision, not a statistical one, to choose a metric of success. And it’s the guy in charge who makes that decision, not some quant.
If you end up reading the Cukier and Mayer-Schoenberger article, please also read Julie Cohen’s draft of a soon-to-be published Harvard Law Review article called “What Privacy is For” where she takes on big data in a much more convincing and skeptical light than Cukier and Mayer-Schoenberger were capable of summoning up for their big data business audience.
I’m actually planning a post soon on Cohen’s article, which contains many nuggets of thoughtfulness, but for now I’ll simply juxtapose two ideas surrounding big data and innovation, giving Cohen the last word. First from the Cukier and Mayer-Schoenberger article:
Big data enables us to experiment faster and explore more leads. These advantages should produce more innovation
Second from Cohen, where she uses the term “modulation” to describe, more or less, the effect of datafication on society:
When the predicate conditions for innovation are described in this way, the problem with characterizing privacy as anti-innovation becomes clear: it is modulation, not privacy, that poses the greater threat to innovative practice. Regimes of pervasively distributed surveillance and modulation seek to mold individual preferences and behavior in ways that reduce the serendipity and the freedom to tinker on which innovation thrives. The suggestion that innovative activity will persist unchilled under conditions of pervasively distributed surveillance is simply silly; it derives rhetorical force from the cultural construct of the liberal subject, who can separate the act of creation from the fact of surveillance. As we have seen, though, that is an unsustainable fiction. The real, socially-constructed subject responds to surveillance quite differently—which is, of course, exactly why government and commercial entities engage in it. Clearing the way for innovation requires clearing the way for innovative practice by real people, by preserving spaces within which critical self-determination and self-differentiation can occur and by opening physical spaces within which the everyday practice of tinkering can thrive.
This is a guest post by Josh Snodgrass.
As the Mathbabe noted recently, a lot of companies are collecting a lot of information about you. Thanks to two Firefox add-ons – Collusion (hat tip to Cathy) and NoScript — you can watch the process and even interfere with it to a degree.
Collusion is a beautiful app that creates a network graph of the various companies that have information about your web activity. Here is an example.
On this graph, I can see that nytimes.com has sent info on me to 2mdn.net, linkstorm.net, serving-sys.com, nyt.com and doubleclick.net. Who are these guys? All I know is that they know more about me than I know about them.
Doubleclick is particularly well-informed. They have gotten information on me from nytimes.com, yahoo.com and ft.com. You may not be able to see it on the picture but there are faint links between the nodes. Some (few) of the nodes are sites I have visited. Most of the nodes, especially some of the central ones are data collectors such as doubleclick and googleanalytics. They have gotten info from sites I’ve visited.
This graph is pretty sparse because I cleared all of my cookies recently. If I let it go for a week and the graph will be so crowded it won’t all fit on a screen.
Pretty much everyone is sharing info about me (and presumably you, too). And, I do mean everyone. Mathbabe is a dot near the top. Collusion tells me that mathbabe.org has shared info with google.com, wordpress.com, wp.com, 52shadesofgreed.com, youtube.com and quantserve.com. Google has passed the info on to googleusercontent.com and gstatic.com
I can understand why. WordPress and presumably wp.com are hosting her blog. Google is providing search capabilities. 52shadesofgreed has an ad posted (You can still buy the decks but even better, come to Alt-Banking meetings and get one free). Youtube is providing some content. It is all innocent enough in a way but it means my surfing is being tracked even on non-commercial sites.
These are the conveniences of modern life. Try blocking all cookies and you will find it pretty inconvenient to use the internet. It would be nice to be selective about cookies but that seems very hard. All of this is happening even though I’ve told my browser not to allow third-party cookies. If you look at cookie policies, it seems you have two alternatives:
- Block all cookies and the site won’t work very well
- Allow cookies and we will send your info to whomever we choose (within the law, of course).
So, it would be nice if there were a law that constrained what they do. My impression is that we Americans have virtually no protection. Europe is better from what I understand.
I’m trying to access a site and there are scripts waiting to run from:
- Po.st Scorecard.com
Clearly a lot of those are about tracking me or showing me ads. As with cookies, if you block all the scripts, the site probably won’t function properly. But the great thing about NoScript is that is makes it easy to allow scripts one by one. So, you can allow the ones that look more legitimate until the site works well enough. Also, you can allow them temporarily.
NoScript and Collusion are great. But mostly they are making me more aware of all the tracking that is going on. And they are also making it clear how hard it is to keep your privacy.
This isn’t just on the internet. Years ago, an economist had an idea about having people put boxes on their cars that would track where they went and charge them for driving, particularly in high congestion times and places. The motivation was to reduce travel that causes a lot of pollution while no one is going anywhere. But people ridiculed the idea. Who would let themselves be tracked everywhere they went.
Well, 40 years later, nearly everyone who has a car has an EZ-pass. And, even if you don’t, they will take a picture of your license plate and keep it on file. All in the name of improving traffic flow.
And, if you use credit cards, there are some big companies that have records of your spending.
What to do about this?
I don’t know.
I like conveniences. Keeping your privacy is hard. DuckDuckGo is a search engine that doesn’t track you (another hat tip to Cathy). But their search results are not as good as Google’s.
Google has all these nice tools that are free. Even if you don’t use them, the web sites you visit surely do. And if they do, google is getting information from them, about you.
This experience has made me even more of a fan of Firefox and add-ons available in it. But what else should I use. And, none of these tools is going to be perfect.
What information gets tracked? A lot of privacy policies say they don’t give out identifying information. But how can we tell?
Just keeping on top of what is going on is hard. For example: what are LSOs? They seem to be a kind of “supercookies”. And Better Privacy seems to be an add-on to help with them.
“Our emails may contain a single, campaign-unique “web beacon pixel” to tell us whether our emails are opened and verify any clicks through to links or advertisements within the email”
Who knew that a pixel could do so much?
The truth is, I want to see these sites. So I am enabling scripts (some of them, as few as I can). The question is how to make the tradeoff. Figuring that out is time consuming. I’ve got better things to do with my life.
I’m going to go read a book.
The Alternative Banking group of #OWS is showing up bright and early tomorrow morning to protest at Citigroup’s annual shareholder meeting. Details are: we meet outside the Hilton Hotel, Sixth Avenue between 53rd and 54th Streets, tomorrow, April 24th, from 8-10 am. We’ve already made some signs (see below).
Here are ten reasons for you to join us.
1) The Glass-Steagall Act, which had protected the banking system since 1933, was repealed in order to allow Citibank and Traveler’s Insurance to merge.
In fact they merged before the act was even revoked, giving us a great way to date the moment when politicians started taking orders from bankers – at the time, President Bill Clinton publicly declared that “the Glass–Steagall law is no longer appropriate.”
2) The crimes Citi has committed have not been met with reasonable punishments.
From this Bloomberg article:
In its complaint against Citigroup, the SEC said the bank misled investors in a $1 billion fund that included assets the bank had projected would lose money. At the same time it was selling the fund to investors, Citigroup took a short position in many of the underlying assets, according to the agency.
The SEC only attempted to fine Citi $285 million, even though Citi’s customers lost on the order of $600 million from their fraud. Moreover, they were not required to admit wrongdoing. Judge Rakoff refused to sign off on the deal and it’s still pending. Citi is one of those banks that is simply too big to jail.
3) We’d like our pen back, Mr. Weill. Going back to repealing Glass-Steagall. Let’s take an excerpt from this article:
…at the signing ceremony of the Gramm-Leach-Bliley, aka the Glass Steagall repeal act, Clinton presented Weill with one of the pens he used to “fine-tune” Glass-Steagall out of existence, proclaiming, “Today what we are doing is modernizing the financial services industry, tearing down those antiquated laws and granting banks significant new authority.”
Weill has since decided that repealing Glass-Steagall was a mistake.
4) Do you remember the Plutonomy Memos? I wrote about them here. Here’s a tasty excerpt which helps us remember when the class war was started and by whom:
We project that the plutonomies (the U.S., UK, and Canada) will likely see even more income inequality, disproportionately feeding off a further rise in the profit share in their economies, capitalist-friendly governments, more technology-driven productivity, and globalization… Since we think the plutonomy is here, is going to get stronger… It is a good time to switch out of stocks that sell to the masses and back to the plutonomy basket.
5) Robert Rubin – enough said. To say just a wee bit more, let’s look at the Bloomberg Businessweek article, “Rethinking Robert Rubin”:
Rubinomics—his signature economic philosophy, in which the government balances the budget with a mix of tax increases and spending cuts, driving borrowing rates down—was the blueprint for an economy that scraped the sky. When it collapsed, due in part to bank-friendly policies that Rubin advocated, he made more than $100 million while others lost everything.
That $100 million was made at Citigroup, which was later bailed out because of bets Rubin helped them make. He has thus far shown no remorse.
6) The Revolving Door problems Citigroup has. Bill Moyers has a great article on the outrageous revolving door going straight from banks to the Treasury and the White House. What with Rubin and Lew, Citigroup seems pretty much a close second behind Goldman Sachs for this sport.
8) The bailout was actually for Citigroup. If you’ve read Sheila Bair’s book Bull by the Horns, you’ll see the bailout from her inside perspective. And it was this: that Citigroup was really the bank that needed it worst. That in fact, the whole bailout was a cover for funneling money to Citi.
9) The ongoing Fed dole. The bailout is still going on – and Citigroup is currently benefitting from the easy money that the Fed is offering, not to mention the $83 billion taxpayer subsidy. WTF?!
10) Lobbying for yet more favors. Citi spent $62 million from 2001 to 2010 on lobbying in Washington. What’s their return on that investment, do you think?
Join us tomorrow morning! Details here.
Last night I went to an event at Barnard where Ina Drew, ex-CIO head of JP Morgan Chase, who oversaw the London Whale fiasco, was warmly hosted and interviewed by Barnard president Debora Spar.
[Aside: I was going to link to Ina Drew's wikipedia entry in the above paragraph, but it was so sanitized that I couldn't get myself to do it. She must have paid off lots of wiki editors to keep herself this clean. WTF, wikipedia??]
A little background in case you don’t know who this Drew woman is. She was in charge of balance-sheet risk management and somehow managed to not notice losing $6.2 billion dollars in the group she was in charge of, which was meant to hedge risk, at least according to CEO Jamie Dimon. She made $15 million per year for her efforts and recently retired.
In her recent Congressional testimony (see Example 3 in this recent post), she threw the quants with their Ph.D.’s under the bus even though the Senate report of the incident noted multiple risk limits being exceeded and ignored, and then risk models themselves changed to look better, as well as the “whale” trader Bruno Iksil‘s desire to get out of his losing position being resisted by upper management (i.e. Ina Drew).
I’m not going to defend Iksil for that long, but let’s be clear: he fucked up, and then was kept in his ridiculous position by Ina Drew because she didn’t want to look bad. His angst is well-documented in the Senate report, which you should read.
Actually, the whole story is somewhat more complicated but still totally stupid: instead of backing out of certain credit positions the old-fashioned and somewhat expensive way, the CIO office decided to try to reduce its capital requirements via reducing (manipulated) VaR, but ended up increasing their capital requirements in other, non-VaR ways (specifically, the “comprehensive risk measure”, which isn’t as manipulable as VaR). Read more here.
Maybe Ina is going to claim innocence, that she had no idea what was going on. In that case, she had no control over her group and its huge losses. So either she’s heinously greedy or heinously incompetent. My money’s on “incompetent” after seeing and listening to her last night. My live Twitter feed from the event is available here.
We featured Ina Drew on our “52 Shades of Greed” card deck as the Queen of diamonds:
Back to the event.
Why did we cart out Ina Drew in front of an audience of young Barnard women last night? Were we advertising a career in finance to them? Is Drew a role model for these young people?
The best answers I can come up with are terrible:
- She’s a Barnard mom (her daughter was in the audience). Not a trivial consideration, especially considering the potential donor angle.
- President Spar is on the board of Goldman Sachs and there’s a certain loyalty among elites, which includes publicly celebrating colossal failures. Possible, but why now? Is there some kind of perverted female solidarity among women that should be in jail but insist on considering themselves role models? Please count me out of that flavor of feminism.
- President Spar and Ina Drew actually don’t think Drew did anything wrong. This last theory is the weirdest but is the best supported by the tone of the conversation last night. It gives me the creeps. In any case I can no longer imagine supporting Barnard’s mission with that woman as president. It’s sad considering my fond feelings for the place where I was an assistant professor for two years in the math department and which treated me well.
Please suggest other ideas I’ve failed to mention.
Warmup: Automatic Grading Models
Before I get to my main take-down of the morning, let me warm up with an appetizer of sorts: have you been hearing a lot about new models that automatically grade essays?
Does it strike you that’s there’s something wrong with that idea but you don’t know what it is?
Here’s my take. While it’s true that it’s possible to train a model to grade essays similarly to what a professor now does, that doesn’t mean we can introduce automatic grading – at least not if the students in question know that’s what we’re doing.
There’s a feedback loop, whereby if the students know their essays will be automatically graded, then they will change what they’re doing to optimize for good automatic grades rather than, say, a cogent argument.
For example, a student might download a grading app themselves (wouldn’t you?) and run their essay through the machine until it gets a great grade. Not enough long words? Put them in! No need to make sure the sentences make sense, because the machine doesn’t understand grammar!
This is, in fact, a great example where people need to take into account the (obvious when you think about them) feedback loops that their models will enter in actual use.
Job Hiring Models
Now on to the main course.
In this week’s Economist there is an essay about the new widely-used job hiring software and how awesome it is. It’s so efficient! It removes the biases of of those pesky recruiters! Here’s an excerpt from the article:
The problem with human-resource managers is that they are human. They have biases; they make mistakes. But with better tools, they can make better hiring decisions, say advocates of “big data”.
So far “the machine” has made observations such as:
- Good if candidate uses browser you need to download like Chrome.
- Not as bad as one might expect to have a criminal record.
- Neutral on job hopping.
- Great if you live nearby.
- Good if you are on Facebook.
- Bad if you’re on Facebook and every other social networking site as well.
Now, I’m all for learning to fight against our biases and hire people that might not otherwise be given a chance. But I’m not convinced that this will happen that often – the people using the software can always train the model to include their biases and then point to the machine and say “The machine told me to do it”. True.
What I really object to, however, is the accumulating amount of data that is being collected about everyone by models like this.
It’s one thing for an algorithm to take my CV in and note that I misspelled my alma mater, but it’s a different thing altogether to scour the web for my online profile trail (via Acxiom, for example), to look up my credit score, and maybe even to see my persistence score as measured by my past online education activities (soon available for your 7-year-old as well!).
As a modeler, I know how hungry the model can be. It will ask for all of this data and more. And it will mean that nothing you’ve ever done wrong, no fuck-up that you wish to forget, will ever be forgotten. You can no longer reinvent yourself.
Forget mobility, forget the American Dream, you and everyone else will be funneled into whatever job and whatever life the machine has deemed you worthy of. WTF.
This morning I’m being driven crazy by this article in yesterday’s Wall Street Journal entitled “Workers Stuck in Disability Stunt Economic Recovery.”
Even the title makes the underlying goal of the article crystal clear: the lazy disabled workers are to blame for the crap economy. Lest you are unconvinced that anyone could make such an unreasonable claim of causation, here’s a tasty excerpt from the article that spells it out:
Economic growth is driven by the number of workers in an economy and by their productivity. Put simply, fewer workers usually means less growth.
Since the recession, more people have gone on disability, on net, than new workers have joined the labor force. Mr. Feroli estimated the exodus to disability costs 0.6% of national output, equal to about $95 billion a year.
“The greater cost is their long-term dependency on transfers from the federal government,” Mr. Autor said, “placing strain on the soon-to-be exhausted Social Security Disability trust fund.”
The underlying model here, then, is that there’s a bunch of people who have the choice between going on disability or “joining the labor force” and they’ve all chosen to go on disability. I wonder where their evidence is that people really have that choice, considering the unemployment numbers and participation rate numbers we see nowadays.
For example, the unemployment rate for youths is now 22.9%, and the participation rate for them has gone from 59.2% in December 2007, to 54.5% today. This is probably not because so many kids under the age of 25 are disabled, I suspect. If you look at the overall labor participation rate, it’s dropped from 66.0 in December 2007 to 63.3 in March 2013. Most of the people who have left the work force are also not disabled. They’ve been discouraged for some other mysterious reason. I’m gonna go ahead and guess it’s because they can’t find a job.
Here’s another example from the article of a seriously fucked-up understanding of cause and effect:
With overall participation down, the labor force—a measure of people working and people looking for work—is barely growing.
They consistently paint the picture whereby people decide to stop working, and then yucky things happen, in this case the labor force stops growing. Damn those lazy people.
They even bring in a fancy word from physics to describe the problem, namely hysteresis. Now, they didn’t understand or correctly define the term, but it doesn’t really matter, because the point of using a fancy term from physics was not to add to the clarity of the argument but rather to impress.
The goal here is, in fact, that if enough economists use sophisticated language to describe the various effects, we will all be able to blame people with bad backs, making $13.6K per year, on why our economy sucks, rather than the rich assholes in finance who got us into this mess and are currently buying $2 million dollar personal offices instead of going to jail.
Just to be clear, that’s $1,130 a month, which I guess represents so enticing a lifestyle that the people currently enjoying it ‘are “pretty unlikely to want to forfeit economic security for a precarious job market”‘ according to M.I.T. economist David Autor. I’d love to have David Autor spell out, for us, exactly what’s economically secure about that kind of monthly check.
The rest of the article is in large part a description of how people get onto SSDI, insinuating that the people currently on it are not really all that disabled or worthy of living high on the hog, and are in any case never ever leaving.
How’s this for a slightly different take on the situation: there are of course some people who are faking something, that’s always the case. But in general, the people on SSDI need to be there, and before the recession might have had the kind of employers who kept them on even though they often called in sick, out of loyalty and kindness, because they didn’t want to fire them. But when the recession struck those employers had to cut them off, or they went out of business completely. Now those people can’t find work and don’t have many options. In other words, the recession caused the SSDI program to grow. That doesn’t mean it caused a bunch of people to get sick, but it does mean that sick people are more dependent on SSDI because there are fewer options.
By the way, read the comments of this article, there are some really good ones (“What were people with injuries and no high-value job skills to do? Is the number of people in the social security disability program the problem or the symptom?”) as well as some really outrageous ones (‘The current situation makes the picture of the “Welfare Queen” of the 1980s look like an honest citizen’).
The financial crisis has given rise to a series of catastrophes related to mathematical modeling.
Time after time you hear people speaking in baffled terms about mathematical models that somehow didn’t warn us in time, that were too complicated to understand, and so on. If you have somehow missed such public displays of throwing the model (and quants) under the bus, stay tuned below for examples.
A common response to these problems is to call for those models to be revamped, to add features that will cover previously unforeseen issues, and generally speaking, to make them more complex.
For a person like myself, who gets paid to “fix the model,” it’s tempting to do just that, to assume the role of the hero who is going to set everything right with a few brilliant ideas and some excellent training data.
Unfortunately, reality is staring me in the face, and it’s telling me that we don’t need more complicated models.
If I go to the trouble of fixing up a model, say by adding counterparty risk considerations, then I’m implicitly assuming the problem with the existing models is that they’re being used honestly but aren’t mathematically up to the task.
But this is far from the case – most of the really enormous failures of models are explained by people lying. Before I give three examples of “big models failing because someone is lying” phenomenon, let me add one more important thing.
Namely, if we replace okay models with more complicated models, as many people are suggesting we do, without first addressing the lying problem, it will only allow people to lie even more. This is because the complexity of a model itself is an obstacle to understanding its results, and more complex models allow more manipulation.
Example 1: Municipal Debt Models
Many municipalities are in shit tons of problems with their muni debt. This is in part because of the big banks taking advantage of them, but it’s also in part because they often lie with models.
Specifically, they know what their obligations for pensions and school systems will be in the next few years, and in order to pay for all that, they use a model which estimates how well their savings will pay off in the market, or however they’ve invested their money. But they use vastly over-exaggerated numbers in these models, because that way they can minimize the amount of money to put into the pool each year. The result is that pension pools are being systematically and vastly under-funded.
Example 2: Wealth Management
I used to work at Riskmetrics, where I saw first-hand how people lie with risk models. But that’s not the only thing I worked on. I also helped out building an analytical wealth management product. This software was sold to banks, and was used by professional “wealth managers” to help people (usually rich people, but not mega-rich people) plan for retirement.
We had a bunch of bells and whistles in the software to impress the clients – Monte Carlo simulations, fancy optimization tools, and more. But in the end, the banks and their wealth managers put in their own market assumptions when they used it. Specifically, they put in the forecast market growth for stocks, bonds, alternative investing, etc., as well as the assumed volatility of those categories and indeed the entire covariance matrix representing how correlated the market constituents are to each other.
The result is this: no matter how honest I would try to be with my modeling, I had no way of preventing the model from being misused and misleading to the clients. And it was indeed misused: wealth managers put in absolutely ridiculous assumptions of fantastic returns with vanishingly small risk.
Example 3: JP Morgan’s Whale Trade
I saved the best for last. JP Morgan’s actions around their $6.2 billion trading loss, the so-called “Whale Loss” was investigated recently by a Senate Subcommittee. This is an excerpt (page 14) from the resulting report, which is well worth reading in full:
While the bank claimed that the whale trade losses were due, in part, to a failure to have the right risk limits in place, the Subcommittee investigation showed that the five risk limits already in effect were all breached for sustained periods of time during the first quarter of 2012. Bank managers knew about the breaches, but allowed them to continue, lifted the limits, or altered the risk measures after being told that the risk results were “too conservative,” not “sensible,” or “garbage.” Previously undisclosed evidence also showed that CIO personnel deliberately tried to lower the CIO’s risk results and, as a result, lower its capital requirements, not by reducing its risky assets, but by manipulating the mathematical models used to calculate its VaR, CRM, and RWA results. Equally disturbing is evidence that the OCC was regularly informed of the risk limit breaches and was notified in advance of the CIO VaR model change projected to drop the CIO’s VaR results by 44%, yet raised no concerns at the time.
I don’t think there could be a better argument explaining why new risk limits and better VaR models won’t help JPM or any other large bank. The manipulation of existing models is what’s really going on.
Just to be clear on the models and modelers as scapegoats, even in the face of the above report, please take a look at minute 1:35:00 of the C-SPAN coverage of former CIO head Ina Drew’s testimony when she’s being grilled by Senator Carl Levin (hat tip Alan Lawhon, who also wrote about this issue here).
Ina Drew firmly shoves the quants under the bus, pretending to be surprised by the failures of the models even though, considering she’d been at JP Morgan for 30 years, she might know just a thing or two about how VaR can be manipulated. Why hasn’t Sarbanes-Oxley been used to put that woman in jail? She’s not even at JP Morgan anymore.
Stick around for a few minutes in the testimony after Levin’s done with Drew, because he’s on a roll and it’s awesome to watch.
I don’t know if you read this article (h/t Radhika Sainath) on a hyperactive professor and Organizational Psychology researcher, Adam Grant, who always helps people when they ask and has a theory about giving. He claims that generous giving is the answer to getting ahead and feeling and being successful.
Well, as a “strategic giver” myself, let me tell you that giving isn’t the way to get ahead. Not as expressed by Grant, anyway*.
If you look carefully at the story, it reveals a bunch of things. Here are a few of them:
- Grant has a stay-at-home wife who deals with the kids all the time. Even so, she doesn’t seem all that psyched about how much time he devotes to helping other people (“Sometimes I tell him, ‘Adam — just say no,’ ”).
- He works all the time and misses sleep to get stuff done.
- He engages in high-profile strategic helping – he helps colleagues and students.
- Moreover, he does it in exaggerated and dramatic ways, leading to people talking about him and thanking him profusely, generally giving him attention.
- Considering that his area of research is how to get people to work hard and be more efficient through helping each other, this attention directly in line with his goal of gaining status.
- Just to be clear, he isn’t researching how to get other people to have high status like him, but rather how to get people to work harder in boring-ass jobs.
Put it all together, and you’ve got this disconnect between the way he applies “helping” to himself and to the subjects in his research.
He researches people in call centers, for example, and figures out how to get them to really believe in their work by seeing someone who benefitted from the associated scholarship program. But working harder doesn’t get them more status, it just makes them tired. The other examples in the article are similar. Actually some of them get grosser. Here’s a tasty excerpt from the article:
Jerry Davis, a management professor who taught Grant at the University of Michigan and is generally a fan of [Adam Grant]‘s work, couldn’t help making a pointed critique about its inherent limits when they were on a panel together: “So you think those workers at the Apple factory in China would stop committing suicide if only we showed them someone who was incredibly happy with their iPhone?”
So what does he means by “giving” when he’s considering other people? Working really hard in a dead-end job? Kinda reminds me of this review of Sheryl Sandberg’s “Lean In” book, written by ex-Facebook disgruntled speech writer Kate Losse. Here’s my favorite line from that bitter essay:
For Sandberg, pregnancy must be converted into a corporate opportunity: a moment to convince a woman to commit further to her job. Human life as a competitor to work is the threat here, and it must be captured for corporate use, much in the way that Facebook treats users’ personal activities as a series of opportunities to fill out the Facebook-owned social graph.
In other words, Grant, like Sandberg, is selling us a message of working really hard with the underlying promise that it will make us successful, especially if we do it because we just love working really hard.
First, it really matters what you work on and who you are helping. If you are not a strategic helper, you end up wasting your time for no good reason. How many times have we seen people who end up doing their job plus someone else’s job, without any thanks or extra money?
If you work really hard on a project which nobody cares about, nobody appreciates it. True.
And if you aren’t a political animal, able to smell out the projects and people that are worth working on extra hard and helping, then you’re pretty much out of luck.
But let’s take one step back from the terrible advice being given by Grant and Sandberg. What are their actual goals? Is it possible that they really think just by working extra hard at whatever shit corporate job we have will leave us successful and fulfilled? Are they that blind to other people’s options? Do they really know nobody in their private lives who found fulfillment by quitting their dead-end corporate job and became a poor but happy poet?
Here’s what Kate Losse says, and I think she hit the nail on the head:
Sandberg is betting that for some women, as for herself, the pursuit of corporate power is desirable, and that many women will ramp up their labor ever further in hopes that one day they, too, will be “in.” And whether or not those women make it, the companies they work for will profit by their unceasing labor.
Similarly, Grant’s personal academic success comes from getting people to work harder. His incentive is to get you to work harder, not be fulfilled. Just to be clear.
* I actually do think giving is a wonderful thing, but certainly not exclusively at work, and it’s not a secret.
There’ve been a couple of articles in the past few days about teacher Value-Added Testing that have enraged me.
If you haven’t been paying attention, the Value-Added Model (VAM) is now being used in a majority of the states (source: the Economist):
But it gives out nearly random numbers, as gleaned from looking at the same teachers with two scores (see this previous post). There’s a 24% correlation between the two numbers. Note that some people are awesome with respect to one score and complete shit on the other score:
Final thing you need to know about the model: nobody really understands how it works. It relies on error terms of an error-riddled model. It’s opaque, and no teacher can have their score explained to them in Plain English.
Now, with that background, let’s look into these articles.
First, there’s this New York Times article from yesterday, entitled “Curious Grade for Teachers: Nearly All Pass”. In this article, it describes how teachers are nowadays being judged using a (usually) 50/50 combination of classroom observations and VAM scores. This is different from the past, which was only based on classroom observations.
What they’ve found is that the percentage of teachers found “effective or better” has stayed high in spite of the new system – the numbers are all over the place but typically between 90 and 99 percent of teachers. In other words, the number of teachers that are fingered as truly terrible hasn’t gone up too much. What a fucking disaster, at least according to the NYTimes, which seems to go out of its way to make its readers understand how very much high school teachers suck.
A few things to say about this.
- Given that the VAM is nearly a random number generator, this is good news – it means they are not trusting the VAM scores blindly. Of course, it still doesn’t mean that the right teachers are getting fired, since half of the score is random.
- Another point the article mentions is that failing teachers are leaving before the reports come out. We don’t actually know how many teachers are affected by these scores.
- Anyway, what is the right number of teachers to fire each year, New York Times? And how did you choose that number? Oh wait, you quoted someone from the Brookings Institute: “It would be an unusual profession that at least 5 percent are not deemed ineffective.” Way to explain things so scientifically! It’s refreshing to know exactly how the army of McKinsey alums approach education reform.
- The overall article gives us the impression that if we were really going to do our job and “be tough on bad teachers,” then we’d weight the Value-Added Model way more. But instead we’re being pussies. Wonder what would happen if we weren’t pussies?
The second article explained just that. It also came from the New York Times (h/t Suresh Naidu), and it was a the story of a School Chief in Atlanta who took the VAM scores very very seriously.
What happened next? The teachers cheated wildly, changing the answers on their students’ tests. There was a big cover-up, lots of nasty political pressure, and a lot of good people feeling really bad, blah blah blah. But maybe we can take a step back and think about why this might have happened. Can we do that, New York Times? Maybe it had to do with the $500,000 in “performance bonuses” that the School Chief got for such awesome scores?
Let’s face it, this cheating scandal, and others like it (which may never come to light), was not hard to predict (as I explain in this post). In fact, as a predictive modeler, I’d argue that this cheating problem is the easiest thing to predict about the VAM, considering how it’s being used as an opaque mathematical weapon.
This is a review of Part I of The Occupy Handbook. Part I consists of twelve pieces ranging in quality from excellent to awful. But enough from me, in Janet Byrne’s own words:
Part 1, “How We Got Here,” takes a look at events that may be considered precursors of OWS: the stories of a brakeman in 1877 who went up against the railroads; of the four men from an all-black college in North Carolina who staged the first lunch counter sit-in of the 1960s; of the out-of-work doctor whose nationwide, bizarrely personal Townsend Club movement led to the passage of Social Security. We go back to the 1930s and the New Deal and, in Carmen M. Reinhart and Kenneth S. Rogoff‘s “nutshell” version of their book This Time Is Different: Eight Centuries of Financial Folly, even further.
Ms. Byrne did a bang-up job getting one Nobel Prize Winner in economics (Paul Krugman), two future Economics Nobel Prize winners (Robert Shiller, Daron Acemoglu) and two maybes (sorry Raghuram Rajan and Kenneth Rogoff) to contribute excellent essays to this section alone. Powerhouse financial journalists Gillian Tett, Michael Hilztik, John Cassidy, Bethany McLean and the prolific Michael Lewis all drop important and poignant pieces into this section. Arrogant yet angry anthropologist Arjun Appadurai writes one of the worst essays I’ve ever had the misfortune of reading and the ubiquitous Brandon Adams make his first of many mediocre appearances interviewing Robert Shiller. Clocking in at 135 pages, this is the shortest section of the book yet varies the most in quality. You can skip Professor Appadurai and Cassidy’s essays, but the rest are worth reading.
Advice from the 1 Percent: Lever Up, Drop Out by Michael Lewis
Framed as a strategy memo circulated among one-percenters, Lewis’ satirical piece written after the clearing of Zucotti Park begins with a bang.
The rabble has been driven from the public parks. Our adversaries, now defined by the freaks and criminals among them, have demonstrated only that they have no idea what they are doing. They have failed to identify a single achievable goal.
Indeed, the absurd fixation on holding Zuccotti Park and refusal to issue demands because doing so “would validate the system” crippled Occupy Wall Street (OWS). So far OWS has had a single, but massive success: it shifted the conversation back to the United States’ out of control wealth inequality managed to do so in time for the election, sealing the deal on Romney. In this manner, OWS functioned as a holding action by the 99% in the interests of the 99%.
We have identified two looming threats: the first is the shifting relationship between ambitious young people and money. There’s a reason the Lower 99 currently lack leadership: anyone with the ability to organize large numbers of unsuccessful people has been diverted into Wall Street jobs, mainly in the analyst programs at Morgan Stanley and Goldman Sachs. Those jobs no longer exist, at least not in the quantities sufficient to distract an entire generation from examining the meaning of their lives. Our Wall Street friends, wounded and weakened, can no longer pick up the tab for sucking the idealism out of America’s youth.We on the committee are resigned to all elite universities becoming breeding grounds for insurrection, with the possible exception of Princeton.
Michael Lewis speaks from experience; he is a Princeton alum and a 1 percenter himself. More than that however, he is also a Wall Street alum from Salomon Brothers during the 1980s snafu and wrote about it in the original guide to Wall Street, Liar’s Poker. Perhaps because of his atypicality (and dash of solipsism), he does not have a strong handle on human(s) nature(s). By the time of his next column in Bloomberg, protests had broken out at Princeton.
Ultimately ineffectual, but still better than…
Lewis was right in the end, but more than anyone sympathetic to the movement might like. OccupyPrinceton now consists of only two bloggers, one of which has graduated and deleted all his work from an already quiet site and another who is a senior this year. OccupyHarvard contains a single poorly written essay on the front page. Although OccupyNewHaven outlasted the original Occupation, Occupy Yale no longer exists. Occupy Dartmouth hasn’t been active for over a year, although it has a rather pathetic Twitter feed here. Occupy Cornell, Brown, Caltech, MIT and Columbia don’t exist, but some have active facebook pages. Occupy Michigan State, Rutgers and NYU appear to have had active branches as recently as eight months ago, but have gone silent since. Functionally, Occupy Berkeley and its equivalents at UCBerkeley predate the Occupy movement and continue but Occupy Stanford hasn’t been active for over a year. Anecdotally, I recall my friends expressing some skepticism that any cells of the Occupy movement still existed.
As for Lewis’ other points, I’m extremely skeptical about “examined lives” being undermined by Wall Street. As someone who started in math and slowly worked his way into finance, I can safely say that I’ve been excited by many of the computing, economic, and theoretical problems quants face in their day-to-day work and I’m typical. I, and everyone who has lived long-enough, knows a handful of geniuses who have thought long and hard about the kinds of lives they want to lead and realized that A. there is no point to life unless you make one and B. making money is as good a point as any. I know one individual, after working as a professional chemist prior to college,who decided to in his words, “fuck it and be an iBanker.” He’s an associate at DB. At elite schools, my friend’s decision is the rule rather than the exception, roughly half of Harvard will take jobs in finance and consulting (for finance) this year. Another friend, an exception, quit a promising career in operations research to travel the world as a pick-up artist. Could one really say that either the operations researcher or the chemist failed to examine their lives or that with further examinations they would have come up with something more “meaningful”?
One of the social hacks to give lie to Lewis-style idealism-emerging-from-an attempt-to-examine-ones-life is to ask freshpeople at Ivy League schools what they’d like to do when they graduate and observe their choices four years later. The optimal solution for a sociopath just admitted to a top school might be to claim they’d like to do something in the peace corp, science or volunteering for the social status. Then go on to work in academia, finance, law or tech or marriage and household formation with someone who works in the former. This path is functionally similar to what many “average” elite college students will do, sociopathic or not. Lewis appears to be sincere in his misunderstanding of human(s) nature(s). In another book he reveals that he was surprised at the reaction to Liar’s Poker – most students who had read the book “treated it as a how-to manual” and cynically asked him for tips on how to land analyst jobs in the bulge bracket. It’s true that there might be some things money can’t buy, but an immensely pleasurable, meaningful life do not seem to be one of them. Today for the vast majority of humans in the Western world, expectations of sufficient levels of cold hard cash are necessary conditions for happiness.
In short and contra Lewis, little has changed. As of this moment, Occupy has proven so harmless to existing institutions that during her opening address Princeton University’s president Shirley Tilghman called on the freshmen in the class of 2016 to “Occupy” Princeton. No freshpeople have taken up her injunction. (Most?) parts of Occupy’s failure to make a lasting impact on college campuses appear to be structural; Occupy might not have succeeded even with better strategy. As the Ivy League became more and more meritocratic and better at discovering talent, many of the brilliant minds that would have fallen into the 99% and become its most effective advocates have been extracted and reached their so-called career potential, typically defined by income or status level. More meritocratic systems undermine instability by making the most talented individuals part of the class-to-be-overthrown, rather than the over throwers of that system. In an even somewhat meritocratic system, minor injustices can be tolerated: Asians and poor rural whites are classes where there is obvious evidence of discrimination relative to “merit and the decision to apply” in elite gatekeeper college admissions (and thus, life outcomes generally) and neither group expresses revolutionary sentiment on a system-threatening scale, even as the latter group’s life expectancy has begun to decline from its already low levels. In the contemporary United States it appears that even as people’s expectations of material security evaporate, the mere possibility of wealth bolsters and helps to secure inequities in existing institutions.
Hence our committee’s conclusion: we must be able to quit American society altogether, and they must know it.The modern Greeks offer the example in the world today that is, the committee has determined, best in class. Ordinary Greeks seldom harass their rich, for the simple reason that they have no idea where to find them. To a member of the Greek Lower 99 a Greek Upper One is as good as invisible.
He pays no taxes, lives no place and bears no relationship to his fellow citizens. As the public expects nothing of him, he always meets, and sometimes even exceeds, their expectations. As a result, the chief concern of the ordinary Greek about the rich Greek is that he will cease to pay the occasional visit.
Michael Lewis is a wise man.
I can recall a conversation with one of my Professors; an expert on Democratic Kampuchea (American: Khmer Rouge), she explained that for a long time the identity of the oligarchy ruling the country was kept secret from its citizens. She identified this obvious subversion of republican principles (how can you have control over your future when you don’t even know who runs your region?) as a weakness of the regime. Au contraire, I suggested, once you realize your masters are not gods, but merely humans with human characteristics, that they: eat, sleep, think, dream, have sex, recreate, poop and die – all their mystique, their claims to superior knowledge divine or earthly are instantly undermined. De facto segregation has made upper classes in the nation more secure by allowing them to hide their day-to-day opulence from people who have lost their homes, job and medical care because of that opulence. Neuroscience will eventually reveal that being mysterious makes you appear more sexy, socially dominant, and powerful, thus making your claims to power and dominance more secure (Kautsky et. al. 2018).*
If the majority of Americans manage to recognize that our two tiered legal system has created a class whose actual claim to the US immense wealth stems from, for the most part, a toxic combination of Congressional pork, regulatory and enforcement agency capture and inheritance rather than merit, there will be hell to pay. Meanwhile, resentment continues to grow. Even on the extreme right one can now regularly read things like:
Now, I think I’d be downright happy to vote for the first politician to run on a policy of sending killer drones after every single banker who has received a post-2007 bonus from a bank that received bailout money. And I’m a freaking libertarian; imagine how those who support bombing Iraqi children because they hate us for our freedoms are going to react once they finally begin to grasp how badly they’ve been screwed over by the bankers. The irony is that a banker-assassination policy would be entirely constitutional according to the current administration; it is very easy to prove that the bankers are much more serious enemies of the state than al Qaeda. They’ve certainly done considerably more damage.
The rest of part I reviewed tomorrow. Hang in there people.
Addendum 1: If your comment amounts to something like “the Nobel Prize in Economics is actually called the The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel” and thus “not a real Nobel Prize” you are correct, yet I will still delete your comment and ban your IP.
*Addendum 2: More on this will come when we talk about the Saez-Delong discussion in part III.
I’ve been enjoying my new job at Johnson Research Labs, where I spend a majority of the time editing my book with my co-author Rachel Schutt. It’s called Doing Data Science (now available for pre-purchase at Amazon), and it’s based on these notes I took last semester at Rachel’s Columbia class.
Recently I’ve been working on Brian Dalessandro‘s chapter on logistic regression. Before getting into the brass tacks of that algorithm, which is especially useful when you are trying to predict a binary outcome (i.e. a 0 or 1 outcome like “will click on this ad”), Brian discusses some common constraints to models.
The one that’s particularly interesting to me is what he calls “interpretability”. His example of an interpretability constraint is really good: it turns out that credit card companies have to be able to explain to people why they’ve been rejected. Brain and I tracked down the rule to this FTC website, which explains the rights of consumers who own credit cards. Here’s an excerpt where I’ve emphasized the key sentences:
You Also Have The Right To…
- Have credit in your birth name (Mary Smith), your first and your spouse’s last name (Mary Jones), or your first name and a combined last name (Mary Smith Jones).
- Get credit without a cosigner, if you meet the creditor’s standards.
- Have a cosigner other than your spouse, if one is necessary.
- Keep your own accounts after you change your name, marital status, reach a certain age, or retire, unless the creditor has evidence that you’re not willing or able to pay.
- Know whether your application was accepted or rejected within 30 days of filing a complete application.
- Know why your application was rejected. The creditor must tell you the specific reason for the rejection or that you are entitled to learn the reason if you ask within 60 days. An acceptable reason might be: “your income was too low” or “you haven’t been employed long enough.” An unacceptable reason might be “you didn’t meet our minimum standards.” That information isn’t specific enough.
- Learn the specific reason you were offered less favorable terms than you applied for, but only if you reject these terms. For example, if the lender offers you a smaller loan or a higher interest rate, and you don’t accept the offer, you have the right to know why those terms were offered.
- Find out why your account was closed or why the terms of the account were made less favorable, unless the account was inactive or you failed to make payments as agreed.
The result of this rule is that credit card companies must use simple models, probably decision trees, to make their rejection decisions.
It’s a new way to think about modeling choice, to be sure. It doesn’t necessarily make for “better” decisions from the point of view of the credit card company: random forests, a generalization of decision trees, are known to be more accurate, but are arbitrarily more complicated to explain.
So it matters what you’re optimizing for, and in this case the regulators have decided we’re optimizing for interpretability rather than accuracy. I think this is appropriate, given that consumers are at the mercy of these decisions and relatively powerless to act against them (although the FTC site above gives plenty of advice to people who have been rejected, mostly about how to raise their credit scores).
Three points to make about this. First, I’m reading the Bankers New Clothes, written by Anat Admati and Martin Hellwig (h/t Josh Snodgrass), which is absolutely excellent – I’m planning to write up a review soon. One thing they explain very clearly is the cost of regulation (specifically, higher capital requirements) from the bank’s perspective versus from the taxpayer’s perspective, and how it genuinely seems “expensive” to a bank but is actually cost-saving to the general public. I think the same thing could be said above for the credit card interpretability rule.
Second, it makes me wonder what else one could regulate in terms of plain english modeling. For example, what would happen if we added that requirement to, say, the teacher value-added model? Would we get much-needed feedback to teachers like, “You don’t have enough student participation”? Oh wait, no. The model only looks at student test scores, so would only be able to give the following kind of feedback: “You didn’t raise scores enough. Teach to the test more.”
In other words, what I like about the “Modeling in Plain English” idea is that you have to be able to first express and second back up your reasons for making decisions. It may not lead to ideal accuracy on the part of the modeler but it will lead to much greater clarity on the part of the modeled. And we could do with a bit more clarity.
Finally, what about online loans? Do they have any such interpretability rule? I doubt it. In fact, if I’m not wrong, they can use any information they can scrounge up about someone to decide on who gets a loan, and they don’t have to reveal their decision-making process to anyone. That seems unreasonable to me.
There are good things about TED talks. It’s nice to have a thoughtful articulate person saying something a little bit new and a little bit different. OK I’m done.
Then there are annoying things about TED talks. People are so ridiculously polished. No idea is that perfect! Rumor has it that, after getting professionally trained for their TED performances, the producers then remove all the “umms” and awkward silences to make it even more perfect. Yuck.
Here’s one way to think about it: TED talks aren’t as good as blogs because they’re not interactive – the audience is expected to receive and not talk back. That’s why I prefer to blog in my underwear and bathrobe, imagining my friends on their living room sofas, also wearing pajamas, and objecting to my stupidity. And that’s why I like the feedback and the comments. It makes my ideas better.
At the same time, TED talks are not as deep as books, where you have enough time and space to actually think through an argument. How could you really develop a deep thought in 20 minutes? You just can’t.
Instead, you have a manipulation of the past which often result in simulated emotional responses, much like how the soundtrack to Amy Tan’s “The Joy Luck Club” makes me cry every time I hear it, no matter what emotional state I’m actually in.
The essence of what’s annoying about TED talks is perfectly parodied by Onion Talks, especially this one:
But what I really hate about TED talks is the curating of ideas that it represents. I realize that any gatekeeper will do this, but I’m particularly concerned about the TED byline, “Ideas Worth Spreading”. According to whom?
Who gets invited to those things? Whose ideas are interesting but non-threatening enough for the TED audience?
And how often do other, rawer ideas get ignored? How appealing do I have to make my idea to rich people in order to be an insider in this mini self-congratulatory universe?
Here’s an example of what I’m talking about written by a woman who was uninvited to give a TED talk under suspicious circumstances (with a follow-up here). Granted, it’s a TEDx situation, but it’s the same problem. The paragraph I worry about most:
Looking back, I must admit that upon learning of this invitation some of my colleagues and I questioned TEDx Manhattan’s commitment to serving as a platform for looking at our food system from a non-privileged perspective. Changing the Way We Eat is not a venue for the common person. The website makes no mention of available scholarships to enable low-income people or students to attend the pricey one day conference. Not only must attendees pay $135 for the privilege of sitting and listening, they also have to apply, explaining why they deserve to be part of the audience and then hope to be selected! Unless the Glynwood Institute does real serious targeted outreach to communities of color (which I haven’t seen and was the primary purpose of my screening party), their set up is going to result in the exclusion of low-income and people of color, regardless of whether it is intentional. I received feedback from a past attendee that presenters referenced poor people and people of color only as being the recipients of charity or service. I think Changing the Way We Eat needed to hear my voice in order to change the way the mainstream food movement thinks about poverty, food access, hunger, and food system change.
I’ve noticed a recent trend in coverage of data science. Namely, there’s backlash against the hype and the over-promising, intentional or not, of data science and data scientists. People are beginning to develop smell tests for big data and raise incredulous eyebrows at certain claims.
This is a good thing. We data scientists should welcome the backlash, first because it’s inevitable, and second because it allows us to have a much-needed conversation about how to behave and what is reasonable to claim or even hope for with respect to big data. There is a poseur problem in big data, after all.
But, fellow data nerds, let’s take this as a cue to start an internal discussion about data science skepticism. Let’s make sure that it’s coming from our community, or at least the surrounding technical community, rather than from yet another set of poseurs who don’t actually know what data is and would only serve to lampoon and discredit our emerging field rather than improve it. We should be the ones leading the charge and admitting when we’re full of shit. We need to own the backlash.
Let me give you an example. A serious data scientist friend of mine recently got asked to be interviewed as part of a conversation on data science skepticism. After thinking hard about what her contribution could be, she wrote back to accept the offer, but was then told she was “off the hook” because they’d found someone else who was “perfect for the assignment.” It turned out to be a journalist who had previously interviewed her. That was his credential for this conversation.
But how can you actually have informed skepticism if you are not yourself an expert?
Another example. David Brooks recently wrote a column wherein he declared himself a data science skeptic and then followed that up by referring to no fewer than eight random statistical studies that made no coherent sense and had no overall point. My conclusion: this is the wrong man to lead the charge against poseurs in data science.
If we are going to rebel against big data soundbites, let’s not do it in soundbites. Instead, let’s talk to people on the inside, who see specific problems in the field and are willing to talk openly about them.
I liked the recent Strata talk by Kate Crawford entitled “Untangling Algorithmic Illusions from Reality in Big Data” (h/t Alan Fekete) which discusses bias in data using very concrete examples, and asks us to examine the objectivity of our “facts”.
For example, she talked about a smart phone app that finds potholes in Boston and report them to the City, and how on the one hand it was cool but on the other it would mean that, if naively applied, richer neighborhoods like Lincoln would get better services than Roxbury. She explained an important point: data analysis is not objective, which most people know. But often the data itself is not either – it was collected in a certain way with particular selection biases.
We need more conversations like this or else we will be leaving a hole which will be filled with loud, uninformed skeptics who would be right to raise the alarm.
One last thing. I’m aware that tons of people, especially serious academic statisticians and computer scientists, criticize data scientists for a totally different reason, namely that we are overly self-promoting (although academics have their own status plays).
But I don’t apologize for that. The truth is, a data scientist is a hybrid between a business person and a researcher. And this is a good thing, not a bad thing: it means the world gets direct access to the modeler, and can challenge any hyperbolic claims by asking for details, rather than having to go through a marketing person who acts (usually quite poorly) as a nerd interpreter. I for one would rather represent my work directly to the world (and be called a self-promoter) then to be kept in the back room.
I usually don’t talk about feminism per se, because honestly I usually don’t think about it. Thanks to role models like my mom, who was an MIT co-ed in the ’60′s and an original nerd, helping develop the internet at Bolt Beranek and Newman and teaching computer science at UMass Boston, I’ve never for one second doubted my personal right to be a thoughtful, argumentative, and ambitious woman. I learned from my mom, and from other trailblazers, that I can pursue my personal interests and trust that the world will welcome my contributions.
Two events in the past week have made me think about how confusing this message has become for today’s growing girls, however.
First, the Sheryl Sandberg thing. To be honest, I haven’t read the book. But I have read this Washington Post article describing the book, and here’s my take on it: a corporate branding campaign loosely tied to women, but mostly pushing forward the agenda of how to be a company drone. From the article:
Sandberg’s understanding of leadership so perfectly internalizes the power structures of institutions created and dominated by men that it cannot conceive of women’s leadership outside of those narrow spaces. Does this also explain why, for Sandberg, the biggest threat to our ability to occupy a position of leadership is a woman’s desire to have a child? This is what men have been telling us for years.
Sandberg may miss so many women in her movement simply because her brand of gender equity is almost entirely privatized, doled out from employer to employee. Women, she advises, will find their way to the top through telling employers upfront about their childbearing plans, through learning how to negotiate pay raises (say “we” instead of “I,” Sandberg cautions, though the collective here is the corporation), through comportment exercises, as taught through Lean In’s web videos.
Like I said in this post, wouldn’t an actual feminist agenda include saying “The hell with this!” to a corporation that is so stifling that all our imaginations could bring us is better maternity leave negotiation tactics with the Borg? Resistance is futile, man!
Answer? As it turns out, anything where you have an opinion and they feel intimidated by you. Solution? Dumb it down, sex it up, and act like a toy. That way, in her words, you’ll be empowered, because they’re all calling you back, and the choice is yours. The choice, I’d add, from a long list of wimps. No thank you.
Can we do better than this, people??
A couple of nights I ago I attended this event at Columbia on the topic of ”Rent-Seeking, Instability and Fraud: Challenges for Financial Reform”.
The event was great, albeit depressing – I particularly loved Bill Black‘s concept of control fraud, which I’ll talk more about in a moment, as well as Lynn Turner‘s polite description of the devastation caused by the financial crisis.
To be honest, our conclusion wasn’t a surprise: there is a lack of political will in Congress or elsewhere to fix the problems, even the low-hanging obvious criminal frauds. There aren’t enough actual police to take on the job of dealing with the number of criminals that currently hide in the system (I believe the statistic was that there are about 1,000,000 people in law enforcement in this country, and 2,500 are devoted to white-collar crime), and the people at the top of the regulatory agencies have been carefully chosen to not actually do anything (or let their underlings do anything).
Even so, it was interesting to hear about this stuff through the eyes of a criminologist who has been around the block (Black was the guy who put away a bunch of fraudulent bankers after the S&L crisis) and knows a thing or two about prosecuting crimes. He talked about the concept of control fraud, and how pervasive control fraud is in the current financial system.
Control fraud, as I understood him to describe it, is the process by which a seemingly legitimate institution or process is corrupted by a fraudulent institution to maintain the patina of legitimacy.
Once you say it that way, you recognize it everywhere, and you realize how dirty it is, since outsiders to the system can’t tell what’s going on – hey, didn’t you have overseers? Didn’t they say everything was checking out ok? What the hell happened?
So for example, financial firms like Bank of America used control fraud in the heart of the housing bubble via their ridiculous accounting methods. As one of the speakers mentioned, the accounting firm in charge of vetting BofA’s books issued the same exact accounting description for many years in the row (literally copy and paste) even as BofA was accumulating massive quantities of risky mortgage-backed securities (update: I’ve been told it’s called an “Auditors Report” and it has required language. But surely not all the words are required? Otherwise how could it be called a report?). In other words, the accounting firm had been corrupted in order to aid and abet the fraud.
To get an idea of the repetitive nature and near-inevitability of control fraud, read this essay by Black, which is very much along the lines of his presentation on Tuesday. My favorite passage is this, when he addresses how our regulatory system “forgot about” control fraud during the deregulation boom of the 1990′s:
On January 17, 1996, OTS’ Notice of Proposed Rulemaking proposed to eliminate its rule requiring effective underwriting on the grounds that such rules were peripheral to bank safety.
“The OTS believes that regulations should be reserved for core safety and soundness requirements. Details on prudent operating practices should be relegated to guidance.
Otherwise, regulated entities can find themselves unable to respond to market innovations because they are trapped in a rigid regulatory framework developed in accordance with conditions prevailing at an earlier time.”
This passage is delusional. Underwriting is the core function of a mortgage lender. Not underwriting mortgage loans is not an “innovation” – it is a “marker” of accounting control fraud. The OTS press release dismissed the agency’s most important and useful rule as an archaic relic of a failed philosophy.
Here’s where I bring mathematics into the mix. My experience in finance, first as a quant at D.E. Shaw, and then as a quantitative risk modeler at Riskmetrics, convinced me that mathematics itself is a vehicle for control fraud, albeit in two totally different ways.
In the context of hedge funds and/or hard-core trading algorithms, here’s how it works. New-fangled complex derivatives, starting with credit default swaps and moving on to CDO’s, MBS’s, and CDO+’s, got fronted as “innovation” by a bunch of economists who didn’t really know how markets work but worked at fancy places and claimed to have mathematical models which proved their point. They pushed for deregulation based on the theory that the derivatives represented “a better way to spread risk.”
Then the Ph.D.’s who were clever enough to understand how to actually price these instruments swooped in and made asstons of money. Those are the hedge funds, which I see as kind of amoral scavengers on the financial system.
At the same time, wanting a piece of the action, academics invented associated useless but impressive mathematical theories which culminated in mathematics classes throughout the country that teach “theory of finance”. These classes, which seemed scientific, and the associated economists described above, formed the “legitimacy” of this particular control fraud: it’s math, you wouldn’t understand it. But don’t you trust math? You do? Then allow us to move on with rocking our particular corner of the financial world, thanks.
I also worked in quantitative risk, which as I see it is a major conduit of mathematical control fraud.
First, we have people putting forward “risk estimates” that have larger errorbars then the underlying values. In other words, if we were honest about how much we can actually anticipate price changes in mortgage backed securities in times of panic, then we’d say something like, “search me! I got nothing.” However, as we know, it’s hard to say “I don’t know” and it’s even harder to accept that answer when there’s money on the line. And I don’t apologize for caring about “times of panic” because, after all, that’s why we care about risk in the first place. It’s easy to predict risk in quiet times, I don’t give anyone credit for that.
Never mind errorbars, though- the truth is, I saw worse than ignorance in my time in risk. What I actually saw was a rubberstamping of “third part risk assessment” reports. I saw the risk industry for what it is, namely a poor beggar at the feet of their macho big-boys-of-finance clients. It wasn’t just my firm either. I’ve recently heard of clients bullying their third party risk companies into allowing them to replace whatever their risk numbers were by their own. And that’s even assuming that they care what the risk reports say.
Overall, I’m thinking this time is a bit different, but only in the details, not in the process. We’ve had control fraud for a long long time, but now we have an added tool in the arsenal in the form of mathematics (and complexity). And I realize it’s not a standard example, because I’m claiming that the institution that perpetuated this particular control fraud wasn’t a specific institution like Bank of America, but rather then entire financial system. So far it’s just an idea I’m playing with, what do you think?
For the past few months at Occupy we’ve been focusing more and more on having a single message and goal. That has been to break up the big banks.
What’s great about this goal is that it’s a non-partisan issue; there is growing consensus (among non-bankers) from the left and the right that the current situation is outrageous and untenable. What’s not great, of course, is that the situation is so easy to spot because it’s so heinous.
Yesterday another voice joined the Break-Up-The-Big-Banks chorus in the form of an editorial at Bloomberg (hat tip Hannah Appel). They wrote a persuasive piece on breaking up the big banks based on simple arithmetic involving bank profits and taxpayer subsidy. Even the title fits that description: “Why Should Taxpayers Give Big Banks $83 Billion a Year?”. Here’s an excerpt from the editorial (emphasis mine):
…Banks have a powerful incentive to get big and unwieldy. The larger they are, the more disastrous their failure would be and the more certain they can be of a government bailout in an emergency. The result is an implicit subsidy: The banks that are potentially the most dangerous can borrow at lower rates, because creditors perceive them as too big to fail.
Lately, economists have tried to pin down exactly how much the subsidy lowers big banks’ borrowing costs. In one relatively thorough effort, two researchers — Kenichi Ueda of the International Monetary Fund and Beatrice Weder di Mauro of the University of Mainz — put the number at about 0.8 percentage point. The discount applies to all their liabilities, including bonds and customer deposits.
Small as it might sound, 0.8 percentage point makes a big difference. Multiplied by the total liabilities of the 10 largest U.S. banks by assets, it amounts to a taxpayer subsidy of $83 billion a year. To put the figure in perspective, it’s tantamount to the government giving the banks about 3 cents of every tax dollar collected.
The top five banks — JPMorgan, Bank of America Corp., Citigroup Inc., Wells Fargo & Co. and Goldman Sachs Group Inc. – - account for $64 billion of the total subsidy, an amount roughly equal to their typical annual profits (see tables for data on individual banks). In other words, the banks occupying the commanding heights of the U.S. financial industry — with almost $9 trillion in assets, more than half the size of the U.S. economy – would just about break even in the absence of corporate welfare. In large part, the profits they report are essentially transfers from taxpayers to their shareholders.
Next time someone tells me I want to take money out of rich people’s pockets (and that makes me a free market hater), I’m going to remind them that every time I pay taxes, 3 cents out of every dollar (that I know of) goes directly to the banks for no good reason whatsoever except the fact that they have the lobbyists to support this system. They’re bullies, and I hate bullies.
So no, I’m not suggesting we take honestly earned money out of the pockets of those who deserve it, I’m suggesting we stop stuffing insiders’ pockets with our money. Big difference.
But it’s not just money I object to – it’s future liability. There’s now an established track record of discovered criminal acts that don’t get anyone at the big banks in trouble. We are setting ourselves up for an even bigger bailout of some form soon, one that we taxpayers really may not be able to afford.
I think of the too-big-to-fail problem as like having an alcoholic brother-in-law who not only sleeps on your couch every night but also knows the PIN code on your ATM card. The money is irksome, no doubt, but what if that guy fell asleep smoking a cigarette and me and my kids die in the resulting fiery inferno? And it’s not that I think all addicts could be magically cured, but I don’t want them to have access to my personal stuff. Get them out of my house.
So can we break up the megabanks already? I’d really like to stop worrying about them because I have better things to do.
1. The U.S. has a progressive tax code
2. The U.S. is a land of opportunity
Actually, the mobility of the U.S. is worse than Canada’s or anywhere in Western Europe. From the NY Times article:
Despite frequent references to the United States as a classless society, about 62 percent of Americans (male and female) raised in the top fifth of incomes stay in the top two-fifths, according to research by the Economic Mobility Project of the Pew Charitable Trusts. Similarly, 65 percent born in the bottom fifth stay in the bottom two-fifths.
3. The bailout worked
Actually, the bailout is still happening, as we see from monthly discoveries such as this recent back-door bailout, and it hasn’t worked for the majority of the people it was intended for, namely people stuck with unreasonable mortgages (people forget this sometimes, but the first half of TARP was for the banks, the second half was for mortgage holders). From a NY Times Op-ed by Elizabeth Lynch (emphasis mine):
So a lender can forgive a second mortgage — which in the event of foreclosure would be worthless anyway — and under the settlement claim credits for “modifying” the mortgage, while at the same time it or another bank forecloses on the first loan. The upshot, of course, is that the people the settlement was designed to protect keep losing their homes.
4. Our private data is protected by our government
Although on the one hand the CIA recently admitted to full monitoring of Facebook using fake personas (h/t Chris Wiggins), the U.S. government does not in fact take great pains to protect the data they collect about its citizens. Moreover, government workers who complain about the porous data protection are punished instead of protected, as is explained in this Times piece. My favorite quote is this bit of common sense:
Susan Landau, a Guggenheim fellow in cyber security, privacy and public policy, says companies and agencies are unlikely to improve data security without the threat of penalty.
“What are the personal consequences for employees who allow data breaches to happen?” Ms. Landau asks. “Until people lose their jobs, nothing is going to change.”
5. We are recovering from the great recession
From 2009-2011, the top 1% captured 121% of all income gains (h/t Matt Stoller).
Who says you can’t perform at 121%? Turns out you can if other people are actually losing income while you’re getting increasingly rich.
Don’t get me wrong, corporate profits have done even better – a 171% gains since we’ve had Obama. But I’d go by things that matter to the 99%, so payrolls and jobs. Payrolls are flat and we still have 5 million fewer jobs, so I’d say it’s not much of a recovery.
The other day I was chatting with a data scientist (who didn’t know me), and I asked him what he does. He said that he used social media graphs to see how we might influence people to lose weight.
Whaaaa? That doesn’t pass the smell test.
If I can imagine it happening in real life, between people, then I can imagine it happening in a social medium. If it doesn’t happen in real life, it doesn’t magically appear on the internet.
So if I have a huge crush on LeBron James (true), and if he tweets that I should go out and watch “Life of Pi” because it’s a great movie (true), then I’d do it, because I’d imagine he is here with me in my living room suggesting that I see that movie, and I’d do anything that man says if he’s in my living room, especially if he’s jamming with me.
But if LeBron James tells me to lose weight while we’re hanging, then I just feel bad and weird. Because nobody can influence someone else to lose weight in person*.
Bottomline: there’s a smell test, and it states that real influence happening inside a social graph isn’t magical just because it’s mathematically formulated. It is at best an echo of the actual influence exerted in real life. I have yet to see a counter-example to that. If you have one, please challenge me on this.
Any data scientist going around claiming they’re going to surpass this smell test should stop right now, because it adds to the hype and adds to the noise around big data without adding to the conversation.
* I’ll make an exception if they’re a doctor wielding a surgical knife about to remove my stomach or something, which doesn’t translate well into social media, and might not always work long-term. And to be fair, you (or LeBron) can influence me to not eat a given thing on a given day, or even to go on a diet, but by now we should know that doesn’t have long term effects. There’s a reason Weight Watchers either doesn’t publish their results or relies on survivorship bias for fake results.
Protest with #OWS Alternative Banking Group
I’m writing to invite you to a protest against mega-bank HSBC at noon on Valentine’s Day (Thursday) starting on the steps of the New York Public Library at 42nd and 5th. Details are here but it’s the big green box on the map on the Fifth Avenue side:
Why are we protesting?
Like you, I’m sure, I’d like nothing more than to stop worrying about shit that goes on in our country’s banks.
We have better things to do with out time than to get annoyed over enormous bonuses being given to idiots for their repeated failures. We’re frankly exhausted from the outrage.
I mean, the average person doesn’t have a job where they get an $11 million bonus instead of a $22 million dollar bonus when they royally screw up. Outside the surreal realm of international banking, the normal response to screw-ups on that level is to get fired.
You might expect a company that has been caught criminally screwing minorities out of fair contracts might be at risk of being closed down, but in this day and age you’d know that big banks, or TIBACO (too interconnected, big, and complex to oversee) institutions, as we in Alt Banking like to call them, are immune to such action.
There’s a clear evolving standard of treatment in the banking sector when it comes to criminal activity:
- the powers that be (SEC, DOJ, etc.) make a huge production over the severity of the fine,
- which is large in dollar amounts but
- usually represents about 10% of the overall profit the given banks made during their exploit.
- Nobody ever goes to jail, and
- the shareholders pay the fine, not the perpetrators.
- The perps get somewhat diminished bonuses. At worst.
The bottomline: we have an entire class of citizens that are immune to the laws because they are considered too important to our financial stability.
But why HSBC?
HSBC is a perfect example of this. An outrageous example.
HSBC didn’t get a bailout in 2008 like many other banks, even though they were ranked #2 in subprime mortgage lending. But that’s not because they didn’t lose money – in fact they lost $6 billion but somehow kept afloat.
And now we know why.
Namely, they were money-laundering, earning asstons by facilitating drugs and terrorism. This was blood money, make no mistake, and it went directly into the pockets of HSBC bankers in the form of bonuses.
When this years-long criminal mafia activity was discovered, nothing much happened beyond a fine, as per usual. Well, to be honest, they were fined $1.9 billion dollars, which is a lot of money, but is only 5 weeks of earnings for the mammoth institution – depending on the way you look at it, HSBC is the 2nd largest bank in the world.
Too big to jail
And that’s when “Too big to fail” became “Too big to jail.” Even the New York Times was outraged. From their editorial page:
Federal and state authorities have chosen not to indict HSBC, the London-based bank, on charges of vast and prolonged money laundering, for fear that criminal prosecution would topple the bank and, in the process, endanger the financial system. They also have not charged any top HSBC banker in the case, though it boggles the mind that a bank could launder money as HSBC did without anyone in a position of authority making culpable decisions.
Clearly, the government has bought into the notion that too big to fail is too big to jail. When prosecutors choose not to prosecute to the full extent of the law in a case as egregious as this, the law itself is diminished. The deterrence that comes from the threat of criminal prosecution is weakened, if not lost.
You may recall that there was an extensive FBI investigation of OWS before Zuccotti Park was even occupied.
Ironic? As the Village Voice said, “apparently non-violent demonstration against corrupt banking is subject to more criminal scrutiny than actual corrupt banking.”
Question for you: which is the bigger national security threat, OWS or HSBC?
HSBC needs its license revoked, and there need to be prosecutions. Those who are guilty need to be punished or else we have an official invitation to criminal acts by bankers. We simply can’t live in a country which rewards this kind of behavior.
Mind you, this isn’t just about HSBC. This is about all the megabanks. Citi or BoA are exempt from prosecution, too. Our message needs to be “break up the megabanks”.
I’ll end with what Matt Taibbi had to say about the HSBC settlement:
On the other hand, if you are an important person, and you work for a big international bank, you won’t be prosecuted even if you launder nine billion dollars. Even if you actively collude with the people at the very top of the international narcotics trade, your punishment will be far smaller than that of the person at the very bottom of the world drug pyramid. You will be treated with more deference and sympathy than a junkie passing out on a subway car in Manhattan (using two seats of a subway car is a common prosecutable offense in this city). An international drug trafficker is a criminal and usually a murderer; the drug addict walking the street is one of his victims. But thanks to Breuer, we’re now in the business, officially, of jailing the victims and enabling the criminals.
Join us on Valentine’s Day at noon on the steps of the New York Public Library and help us Occupy HSBC. Please redistribute widely!