I just finished a neat little book called The Wellness Syndrome by Carl Cederström and André Spicer. They are (business school) professors in Stockholm and London, respectively, so the book has a welcome non-U.S. perspective.
The book defines the wellness syndrome to be an extension and a perversion of the concept of individual well-being. According to Cederström and Spicer, it’s not just that you are expected to care for yourself, it’s that you are blamed if you don’t, and conversely, if there’s anything at all wrong with your life, then it’s because you’ve failed to sufficiently take care of yourself. The result is that people have become utterly unaware of why things happen to them and how much power they actually have to change anything.
The wellness syndrome manifests itself in various ways:
- We are asked to “think positively” to make positive things happen to us. The funniest (read: saddest) section of the book relates to the fact that David Cameron was a big believer in this kind of positive thinking; he focused on good outcomes and ignored the bad ones, believing that somehow his personal willpower would make good things happen.
- We are asked to take care of ourselves in order to stay competitive in the workforce, to productize and commoditize ourselves. This could mean staying slim – because if you’re overweight you’re falling down on the self-optimization regiment – or it could mean engaging in the quantified self movement, keeping track of sleep, exercise, and even pooping schedules, and at the very least it requires us to monitor our attitudes.
- We are asked to enjoy ourselves while we take full personal responsibility for our own wellness, which in the age of the gig economy means we always appear happy to stay lively and infinitely employable under increasingly precarious economic conditions.
- If things don’t go well for us, if we cannot find that job or we cannot seem to lose the extra weight, we are expected to feel guilty and – this is crucial – not to blame the system for an inadequate supply of job, nor the racist, sexist, or otherwise discriminatory environment, but rather our own mindset. God forbid we ever accept any actual limit to our powers of reinvention, because that is equivalent to giving up.
- The authors point to Margaret Thatcher and Tony Blair in the UK and to Reagan and Bill Clinton in the US as creators of this notion of individual responsibility as a shield of governmental responsibility, and they frequently point out that the “positive mindset” self-help gurus thus represent a perfect pairing: a pairing, moreover, which manages to depoliticize itself as its power grows.
- The consequence: we don’t think of ourselves as political victims when we fall prey to a narcissistic worldview in which we are never fit enough, never eating enough organic kale, and never productive enough. Instead we engage in self-criticism, guilt, and renewed promises to try better next time. We internalize the shame and the definition of ourselves as “improperly optimized.”
- In the end, we all walk around with tiny little versions of Reagan’s welfare queens in our heads – or at the very least, the fear of becoming anything like her. In the UK it’s a slightly varied version called the Chav.
There are two rich topics that aren’t addressed in this book which I’d love to hear about, even if it’s just in an informal conversation with the authors. First, what about the online dating scene? How does that play into this and amplify it? From my perspective, online dating has a strong effect on how people create and wield data about themselves, and the extent to which they self-criticize, stemming from (I assume) the question of how they are being seen by potential lovers.
Second, to what extent does this concept of self-perfecting and quantifying encourage the subculture of futurism? Do people like Ray Kurzweil and others who believe they will live forever represent the most extreme version of the wellness syndrome, or do they suffer from some other disease?
I liked the book a lot. There are lots of topics in common with my upcoming book, in fact, including wellness programs and personal data collection, and other ways that employers have increasing control over our bodies and lives. And although we largely agree, it was interesting to read their more historical take on things. Also, it was a super fast read, at only 135 pages. I recommend it.
A decades-long focus on policing minor crimes and activities – a practice called Broken Windows policing – has led to the criminalization and over-policing of communities of color and excessive force in otherwise harmless situations. In 2014, police killed at least 287people who were involved in minor offenses and harmless activities like sleeping in parks, possessing drugs, looking “suspicious” or having a mental health crisis. These activities are often symptoms of underlying issues of drug addiction, homelessness, and mental illness which should be treated by healthcare professionals and social workers rather than the police.
Having studied the effects of uneven policing myself, especially how it pertains to the data byproduct of “police events,” I could not agree more.
There was a recent New York Times article that got people’s attention. It claimed that there was no bias in police shootings of blacks over whites. What it didn’t talk about – crucially – was the chance that a given person would end up in an interaction with the police in the first place.
It’s much more likely for blacks, especially young black men, to end up in an interaction with cops. And that’s due in large part to the broken theory of Broken Windows policing.
New York City’s version of Broken Windows policing – Stop, Question, and Frisk – was particularly vile, and was eventually declared unconstitutional due to its disparate impact on minorities. The ACLU put some facts together when Stop and Frisk was at its height, including the following unbelievable statistics from 2011:
- The number of stops of young black men exceeded the entire city population of young black men (168,126 as compared to 158,406).
- In 70 out of 76 precincts, blacks and Latinos accounted for more than 50 percent of stops, and in 33 precincts they accounted for more than 90 percent of stops. In the 10 precincts with black and Latino populations of 14 percent or less (such as the 6th Precinct in Greenwich Village), black and Latino New Yorkers accounted for more than 70 percent of stops in six of those precincts.
What happens when this kind of uneven policing goes on? Lots of stupid arrests for petty crimes, for “resisting arrest,” and generally for being poor or having untreated mental health problems. About 1 in 1000 such stops are directly linked to a violent crime.
And again, since those stopped are overwhelmingly minority, it means that when City Hall decides to use predictive policing based on this data, they end up over policing the same neighborhoods, creating even more uneven and biased data. That continuing stream of data even ends up in sentencing and paroling algorithms, making it more likely for those same over-policed populations to stay in jail longer.
It’s high time we get rid of the root cause, the theory of Broken Windows, which was never proven in the first place, which optimizes on the wrong definition of success, and which further undermines community trust in the police.
I was invited last week to an event co-sponsored by the White House,Microsoft, and NYU called AI Now: The social and economic implications of artificial intelligence technologies in the near term. Many of the discussions were under “Chatham House Rule,” which means I get to talk about the ideas without attributing any given idea to any person.
Before I talk about some of the ideas that came up, I want to mention that the definition of “AI” was never discussed. After a while I took it to mean anything that was technological that had an embedded flow chart inside it. So, anything vaguely computerized that made decisions. Even a microwave that automatically detected whether your food was sufficiently hot – and kept heating if it wasn’t – would qualify as AI under these rules.
In particular, all of the algorithms I studied for my book certainly qualified. And some of them, like predictive policing and recidivism risk models, google search and resume filtering algorithms, were absolutely talked about and referred to as AI.
One of the questions we posed was, when is AI appropriate? Is there a class of questions that AI should not be used for, and why? More interestingly, is there AI working and making decisions right now, in some context, that should be outlawed? Or at least put on temporary suspension?
[Aside: I’m so glad we’re actually finally discussing this. Up until now it seems like wherever I go it’s taken as a given that algorithms would be an improvement over human decision-making. People still automatically assume algorithms are more fair and objective than humans, and sometimes they are, but they are by no means perfect.]
We didn’t actually have time to thoroughly discuss this question, but I’m going to throw down the gauntlet anyway.
Take recidivism risk models. Julia Angwin and her team at ProPublica recently demonstrated that the COMPAS model, which was being used in Broward County Florida (as well as many other places around the country), is racist. In particular, it has very different errors for blacks and for whites, with high “false positive” rates for blacks and high “false negative” rates for whites. This ends up meaning that blacks go to jail for longer, since that’s how recidivism rates are being used.
So, do we throw out recidivism modeling altogether? After all, judges by themselves are also racist; a models such as the COMPAS model might actually be improving the situation. Then again, it might be making it worse. We simply don’t know without a monitor in place. (So, let’s get some monitors in place, people! Let’s see some academic work in this area!)
I’ve heard people call for removing recidivism models altogether, but honestly I think that’s too simple. I think we should instead have a discussion on what they show, why they’re used the way they are, and how they can be improved to help people.
So, if we’re seeing way more black (men) with high recidivism risk scores, we need to ask ourselves: why are black men deemed so much more likely to return to jail? Is it because they’re generally poorer and don’t have the start-up funds necessary to start a new life? Or don’t have job opportunities when they get out of prison? Or because their families and friends don’t have a place for them to stay? Or because the cops are more likely to re-arrest them because they live in poor neighborhoods or are homeless? Or because the model’s design itself is flawed? In short, what are we measuring when we build recidivism scores?
Second, why are recidivism risk models used to further punish people who are already so disadvantaged? What is it about our punitive and vengeful justice system that makes us punish people in advance for crimes they have not yet committed? It keeps them away from society even longer and further casting them into a cycle of crime and poverty. If our goal were to permanently brand and isolate a criminal class, we couldn’t look for a better tool. We need to do better.
Next, how can we retool recidivism models to help people rather than harm them? We could use the scores to figure out who needs resources the most in order to stay out of trouble after release, to build evidence that we need to help people who leave jail rebuild their lives. How do investments in education inside help people once they get out land a job? Do states that make it hard for employers to discriminate based on prior convictions – or for that matter on race – see better results for recently released prisoners? To what extent does “broken windows policing” in a neighborhood affect the recidivism rates for its inhabitants? These are all questions we need to answer, but we cannot answer without data. So let’s collect the data.
Back to the question: when is AI appropriate? I’d argue that building AI is almost never inappropriate in itself, but interpreting results of AI decision-making is incredibly complicated and can be destructive or constructive, depending on how well it is carried out.
And, as was discussed at the meeting, most data scientists/ engineers have little or no training in thinking about this stuff beyond optimization techniques and when to use linear versus logistic regression. That’s a huge problem, because part of AI – a big part – is the assumption that AI can solve every problem in essentially the same way. AI teams are, generally speaking, homogenous in gender, class, and often race, and that monoculture gives rise to massive misunderstandings and narrow ways of thinking.
The short version of my answer is, AI can be made appropriate if it’s thoughtfully done, but most AI shops are not set up to be at all thoughtful about how it’s done. So maybe, at the end of the day, AI really is inappropriate, at least for now, and until we figure out how to involve more people and have a more principled discussion about what it is we’re really measuring with AI.
What the fuck is wrong with the NSF? Why isn’t it supporting the arXiv?
I have been offended and enraged recently to receive pleading emails from members of the hard-working Cornell University Library arXiv Team for money. As in, please give us $5.
This is a ridiculous state of affairs.
Right now arXiv, which hosts preprints from the fields of mathematics, computer science, physics, quantitative biology, quantitative finance, and statistics, plays an absolutely pivotal role in basic research in this country, especially given the expense and time-consuming journal publishing process.
It has an operating budget of less that $1 million per year, and is somehow left begging for personal donations, supplemented by small grants from the Simons Foundation.
If you look at the mission of the National Science Foundation, it’s first part is “to promote the progress of science.” Moreover, it has an annual budget of $7.5 billion. I cannot think of a better way for it to fulfill its mission than to support the maintenance and expansion of the arXiv.
Am I wrong about this? WTF??
Today and yesterday I’m recording the audiobook version of my upcoming book, Weapons of Math Destruction, in a studio in the Random House building near Columbus Circle.
It’s hard work! I’m constantly having to retake sentences, either because I thought my tone was too flat (I hate flat audiobook readers!), or wasn’t emphasizing the right words, or because the words are just hard to say.
Speaking of which, I promise to never, ever write the phrase “assist statistics” in anything that might someday be read out loud, ever, anywhere. And also, you are hereby prohibited from reading this blogpost out loud.
I was pretty worried that the actual content would be bothersome to me – that I’d find tons of typos, or that things would have changed so much that the content is no longer relevant. So far, so good, though, at least to my eyes.
I’m happy with the book! Is that ok to say (not out loud!!)? I’m holding on to this delicious feeling until the nasty reviews come out. After that I’ll just cry inside at all times.
In the meantime, I’ve started a website for the book, including early reviews (i.e. blurbs, including from my buddy Jordan Ellenberg) and one actual review from Publisher’s Weekly, which I’m super happy with.
There was an amazing This American Life episode that aired earlier this month called Tell Me I’m Fat, centering around 4 stories about how people have dealt with being fat and the obesity epidemic more generally (hat tip Becky Jaffe).
And I plan to respond to all of them in turn, but let me mention right off the top that I didn’t think I had much to learn about this topic, but I learned a lot about this topic from listening to this episode, which was both empathetic and deep.
The first story could have been about me, almost. In short, it was about a woman who spent a bunch of wasted time in her youth worrying about being fat, then eventually she realized she was always going to be fat, that she was sick of apologizing for it and going on diets that didn’t work, and she came to terms with being fat. She owns it. Good for her.
What especially made me nod along was her talking about how she’d prefer the descriptor “fat” than the alternative, “overweight,” which is both a useless euphemism and a judgment, that it was somehow a temporary problem that would soon be fixed. Fuck that.
Oh, and also, she works with Dan Savage, and she called him on his fat shaming. I have always wanted someone to do that.
The second story was super sad, about a woman who was fat at some point but lost a bunch of weight by taking diet pills – basically speed – and found love and a good job by slimming down. She is now married to a man who admitted on tape that he wouldn’t love her if she were fat. She has a job which she claims she needs to be skinny to keep. She’s still taking (black market) diet pills. I am absolutely terrified for her.
The third story was what hit me. It was the story of a very fat woman of color, talking about just how hard it is to be that large. I really do get a lot of what she’s saying, but the more I think about it the more I realize I don’t get it, actually. I mean, I’ve been to restaurants where the chairs have arms and define a butt size that is simply smaller than mine. I have needed to ask for another chair. I have been extremely uncomfortable in an airplane seat.
But I’ve never been unable to fly, nor have I worried about chairs breaking beneath me. This woman does worry about this, and researches restaurants before she goes in case she cannot be accommodated. It’s a different level of humiliation and isolation. Where I feel annoyed that subway seats are too small, she is truly removed from the realm of normal.
She has a name for people like me: Lane Bryant Fat. I’m the woman who, increasingly, can find cute clothes to wear, who can talk about being fit and fat, and who can find company in a larger and larger adult population of women of size 22 or thereabouts.
She’s right, I don’t feel like a freak anymore. When I go to Brooklyn, I actually feel very normal. Even when I was in Paris I didn’t stick out very much, which was certainly very different 20 years ago.
And she’s also right that Lane Bryant Fat women don’t really get here or care about her. When I pass by people as large as she is, I do not regularly relate to them. On a normal day, some little voice inside me, some mean part of me, says, at least I haven’t let myself go that much.
Considering how hard I know I’ve tried in the past to change, you’d think I would be more enlightened about this issue, but until I heard this radio segment, I had never examined my own, internal version of fat shaming. Shame on me.
The last segment was about the Oral Roberts University effort in the 1970’s, I believe, to make its students lose weight as a graduation requirement. This resonated with me deeply, because it was a large scale version of what went on within my home as a child. For a time as a tweenager, I wasn’t given my allowance unless I’d lost enough weight each week. It was cruel, humiliating, and it imbued me with a shame that lasted longer than I’d care to admit.
This was a breakthrough, this radio program. I am so very glad this conversation has begun, and I’m so very glad it included these multiple voices, but it’s really just the beginning.
For example, here’s the thing I’m grappling with right now. I’m living in fear of becoming (type II) diabetic. I’m absolutely high risk for it: my age, my genetics, and my weight all point to it. The only thing I have going for myself is that I exercise regularly, which reduces the risk, but not entirely. So I’m on the lookout, and I’d like to think I’m prepared.
But part of that preparation includes being willing to have gastric bypass surgery, which has become much safer and is an almost miracle cure for type II diabetes. It is, in fact, the treatment of choice according to some international experts.
But at the same time, it’s a diet surgery, and if I underwent the procedure, I could expect to lose a lot of weight. For someone who has spent 20 years establishing a (Lane Bryant) fat identity, it’s actually really confusing to imagine opting for the knife. I’d feel like a turncoat.
Which isn’t to say I’d refuse it. I’ve already checked that my insurance covers the surgery. For BMI up to 40, it covers it if diabetes is present. But given that my BMI is actually above that, I could get the surgery now, without needing to “be sick.” I’m confused by this, and I don’t think I’m alone.
So what about it, This American Life? More episodes, please!
The Brexit vote was a huge deal, both politically and economically. Tons of polls have been telling us for weeks that’s it’d be a close contest, but since the murder of Jo Cox’s, they had mostly been pointing one way: namely, to a Remain win.
To be clear, lots of people said it was too close to call, but the bulk of yesterday’s evidence said that Remain would win by 52% to 48%, with a margin of error of around 2%. The actual results were the opposite, Remain lost by 48% to 52%.
Stock markets can also embed beliefs, and in this case they definitely seemed to think Britain would vote to remain in the EU. For that matter, there were plenty of betting markets that allowed people to bet directly on the vote, and as of yesterday the odds were steeply in favor of Remain. Even the early exit polls pointed to Remain.
So, why did all the polls get it so wrong? I have no more information that anyone else, but I have some purely unsubstantiated, backwards-looking guesses:
- Older people are much more likely to vote, and they also tended to vote Leave.
- People who voted to Leave cared more about the issue.
- People lie in polls, and given that the Leave campaign was being accused of racism, it’s maybe easier to lie towards Remain than the other way around. Also could be a reason that more “undecided” voters were secretly planning to vote Leave but didn’t want to say it out loud.
- People might have actually put money in the betting markets, including the financial markets, that have nothing to do with their belief of the outcome but rather represents a hedge for another position.
- As for the exit polls, they are easier to take in cities, where there are a lot of people, but where there also tend to be more “Remain” voters.
What do you think? Here’s some demographic info from the Guardian that may or may not help: