mathbabe

Nerds, you’re in luck!

We’ve designed Stacks Project Hoodies and they’re for sale. Please tell all your nerd friends to sign up by June 16th so we’ll have them printed in time for the Stack Project Workshop taking place in Michigan at the end of July.

Here’s the Google form, have at it!

Thanks to Wei Ho and Pieter Belmans for their help in organizing!

I ranted against Silicon Valley “entrepreneurs” in my latest Bloomberg View column:

Don’t Expect Tech to Care About Your Problems:

Interplanetary travel is way more fun than accountability.

See all my Bloomberg View columns here.

My newest Bloomberg View column is out:

What If Robots Did the Hiring at Fox News?

See all my Bloomberg View columns here.

I’ve gone and done it, folks: I’ve designed a “Period Equity (tampon) Hat” for my friend Laura Strausfeld, who is speaking later today at a cool rally in D.C.:

Rally for Safe Feminine Care Products in Washington, DC!

Anyway, here’s the hat, tell me what you think:

I had to learn a new technique called “intarsia in the round” in order to knit this hat. Also, I plan to put the design up on ravelry soon, so look for me there if you’re interested in knitting your own Period Equity (tampon) Hat! My Ravelry username is cathyoneil.

Also, if you’re wondering why I’m interested in this particular issue, and why Laura is speaking there, please read this post, as well as this one, about how I was a plaintiff on the New York State tampon tax case, which we won, and Laura was the legal brain behind it.

Laura has recently started an organization called Period Equity to further the cause. And if you look at their site, you’ll see my hat design was pretty much a total rip-off of their website design.

This is a guest post by Eugene Stern, originally posted on his blog sensemadehere.wordpress.com.

“Why am I surrounded by statistical illiterates?” — Roger Mexico in Gravity’s Rainbow

Oops, they did it again. This weekend, the New York Times put out this profile of William Sanders, the originator of evaluating teachers using value-added models based on student standardized test results. It is statistically illiterate, uses math to mislead and intimidate, and is utterly infuriating.

Here’s the worst part:

When he began calculating value-added scores en masse, he immediately saw that the ratings fell into a “normal” distribution, or bell curve. A small number of teachers had unusually bad results, a small number had unusually good results, and most were somewhere in the middle.

And later:

Up until his death, Mr. Sanders never tired of pointing out that none of the critiques refuted the central insight of the value-added bell curve: Some teachers are much better than others, for reasons that conventional measures can’t explain.

The implication here is that value added models have scientific credibility because they look like math — they give you a bell curve, you know. That sounds sort of impressive until you remember that the bell curve is also the world’s most common model of random noise. Which is what value added models happen to be.

Just to replace the Times’s name dropping with some actual math, bell curves are ubiquitous because of the Central Limit Theorem, which says that any variable that depends on many similar-looking but independent factors looks like a bell curve, no matter what the unrelated factors are. For example, the number of heads you get in 100 coin flips. Each single flip is binary, but when you flip a coin over and over, one flip doesn’t affect the next, and out comes a bell curve. Or how about height? It depends on lots of factors: heredity, diet, environment, and so on, and you get a bell curve again. The central limit theorem is wonderful because it helps explain the world: it tells you why you see bell curves everywhere. It also tells you that random fluctuations that don’t mean anything tend to look like bell curves too.

So, just to take another example, if I decided to rate teachers by the size of the turds that come out of their ass, I could wave around a lovely bell-shaped distribution of teacher ratings, sit back, and wait for the Times article about how statistically insightful this is. Because back in the bad old days, we didn’t know how to distinguish between good and bad teachers, but the Turd Size Model™ produces a shiny, mathy-looking distribution — so it must be correct! — and shows us that teacher quality varies for reasons that conventional measures can’t explain.

Or maybe we should just rate news articles based on turd size, so this one could get a Pulitzer.

My newest Bloomberg View Column:

Donald Trump’s Path-Independent Theory of Mind: How the U.S. president is like a Google ad test

You can see all of my Bloomberg View columns here.

My newest Bloomberg Column about politically driven data finagling:

Unreliable Data Can Threaten Democracy

Also, you can see all my Bloomberg columns here.

I’m a bit behind with posting my latest gargantuan knitting project. I call it the 100 Day Blanket because I bought the yarn on the day after the election in an effort to counterbalance my wildly unbalanced thoughts and emotions, and I finished it 100 days after the inauguration. It was a very successful coping mechanism for anxiety.

Given that it has 144 squares in it, and that there were about 10 weeks in between the election and inauguration, that means I knitted nearly one square on average. Actually it took me a couple of weeks to gather the courage to put it all together so I’d say I really did just continuously knit for a while there.

Because, dude, that’s a lot of nervous energy. I should also mention that I knitted numerous pussy hats and other smaller projects during that same period. Serious question, what do non-knitters do to deal with their anxiety?

Without further ado, the 100 Day Blanket:

Please don’t look too carefully at our messy side tables.

Here’s a glamour shot:

And a couple of shots of putting it together:

This took place at our friends’ ‘Happy House’ upstate.

One quarter at a time!

My latest Bloomberg View column, probably my favorite so far:

Don’t Grade Teachers With a Bad Algorithm

My newest column in Bloomberg View, in which I argue that Yuval Harari is putting us all on:

I’d Rather Not Merge With Robots, Thank You

This is an anonymous guest post.

Mentorship is important in any field. In the tech industry, it is essential. In tech, one’s network is key for learning about the existence of smaller startups, where the financial upside is often higher than at big companies due to stock grants. For a culture that emphasizes meritocracy so heavily, tech is much more of a who-is-who than I ever realized before moving out to the Bay Area as an engineer last year. Not only that, but it is especially difficult to access this network as a woman.   I believe that the informal culture of tech, in which professional and social mix to an extent that it is unclear whether an interaction is professional or romantic, harms women in finding mentorship. Ultimately, those with real power and influence in Silicon Valley are in a network of their own.

I learned this firsthand when I met with my first Very Important Person (VIP). This VIP invited me to meet at The Battery, described on its website as a “unique sort of social destination” featuring “an eager, inquisitive bunch, always curious, always on the hunt for new ideas and problems to solve…Here is where they came to refill their cups. To tell stories. To swap ideas. To eschew status but enjoy the company of those they respected. Here is where they came to feel at home on an evening out.” For an easy annual payment of $2400.

I was initially surprised when this VIP decided to meet with me, given how difficult I had found it to get face time with anyone. I was even more surprised when he talked at me for nearly an hour (ignoring my pre-prepared questions), until his next meeting – a tall blonde girl – arrived. Being just out of college and naive, I thought nothing of it, though he did reference how he “just wanted to get laid in college” during our meeting – until the emails and texts started coming. Over the course of the next month, I received email after email from this person, to all three of my email addresses which he somehow got, and later to my cell phone, saying “wanted to see me again” among other things. I will never be 100 percent sure about his intent. At the same time, why on earth would a VIP be so interested in seeing me again?

Whatever his intent, I am confident that it wasn’t mentorship. Despite my having prepared specific questions for our meeting that I wanted advice on, he instead talked at me for the full hour. I think that was the most upsetting piece of it for me. I wanted mentorship, and instead ended up getting weird emails and texts.

I am not the only one of my friends with a Battery story. I’ve been told that there is a secret bar behind the regular bar, which is where things get really weird.

This VIP is certainly an outlier. Only a small fraction of men have creepy intent. And yet, I am sure that plenty of white men aged 35 to 50 (the “older generation” by tech standards that I am trying to access) probably don’t want to talk to me for precisely that reason. Getting coffee with a young woman can look like a date even if it is not, and men in positions of power are especially wary of sexual harassment allegations.

I believe that the informal culture of hoodies and happy hours makes it more difficult for women to access mentorship. A college classmate who works in politics remarked that senior people in politics are more willing to chat with her, sometimes for hours. The informal culture of tech, in which men frequently grab a drink with a male mentor but often do not feel comfortable doing the same with a woman, means that it is difficult for women to get access. At least in politics, it is more clear whether a mentor is inappropriately hitting on you in a professional setting, because that setting is clearly professional.

What about senior female mentors? I have pursued this strategy as well with some limited success, but feel that there are simply not enough senior women to go around for this to be a viable solution. Attrition rates, coupled with the fact that this industry was far more hostile to women 10+ years ago, means that senior women are few and far between, as well as stretched thin. It is essential to connect with mentors of all genders.

I am enormously grateful to those who have provided me with mentorship, including peers just a year or two above me who have helped fill in some of the gaps. That being said, I feel that as long as succeeding in tech involves being well-connected in a way that women and minorities in tech are not, diversity in the industry will stall. The past year has felt more like The Social Network than I ever could have imagined – creepy but well-connected mentors, hiring decisions made over drinks, and all.

Here’s my newest Bloomberg View column, about female engineers at Facebook and Simpson’s Paradox:

Is Facebook Tough on Women? Let’s Check the Data

This is a guest post by Phil Goff, the inaugural Franklin A. Thomas Professor in Policing Equity at John Jay College of Criminal Justice. He is the co-founder and president of the Center for Policing Equity, and an expert in contemporary forms of racial bias and discrimination, as well as the intersections of race and gender.

On November 9, 2016, the world of police accountability shifted dramatically. Though the Movement for Black Lives and local engagement in police reform did not end, the drastic change to the political landscape left many who supported those fights in shock. And as the first 100 days of the new Administration are now behind us, the ways in which this Administration has already changed the trajectory of criminal justice reform and other aligned civil liberties is discouraging. From the appointment of Jeff Sessions to Attorney General to his order to review existing DOJ grants, investigations, and consent decrees, many are expecting both an evaporating role of federal government in police accountability and an expanding role for it in immigration and surveillance activities that run antithetical to public safety and fairness.

The retreat from principles of safety and justice hurts me, too. But I don’t despair. That’s at least in part because I’m a professional JusticeNerd™ (in addition to being one in my spare time). My job is to build more and better protections for civil rights through science. So, while the picture at the federal level is not inspiring, the rest of the landscape is. Specifically, recent commitments from philanthropy and tech giants like Google bring the promise of accountability through data metrics closer than it was before the election. Here’s what I mean:

When the crisis of public trust in police began, we had no national data on police behavior. Nothing on stops. Nothing on use of force. Nothing on policies. Nothing on officer psychological profiles. Nothing. And without metrics, the job of holding police accountable is nigh impossible.

But unbeknownst to many, police chiefs were already organizing to fix that. Yes. Police chiefs. At a conference my organization, the Center for Policing Equity (CPE), co-hosted with DOJ, representatives from 36 of the largest police departments called for a national database where stops and use of force could be standardized, compared, and mined for insights about racial disparities. They wanted answers to their questions about how “bad” their disparities were—and what they could do to fix them. As a result, CPE won a million-dollar grant from the National Science Foundation, and we began constructing the National Justice Database (NJD), the first and still the largest standardized database of police officer behavior. The NJD also collects data on officer psychological orientations (including implicit bias), providing a unique opportunity to study the roots of racial disparities in policing.

But the database was not ready when the crisis hit. We were still putting together the infrastructure. Still adding departments. So, when the crisis hit, first in Ferguson, then in Baltimore, New York, Minneapolis, Baton Rouge, and Charlotte, we had fewer answers than questions. Still, like the JusticeNerds™ we are, we persisted.

The past 5 years have been labor intensive, with data extraction, cleaning, standardization, and analysis taking months for each department. With those months of effort came valuable insights about the right metrics to use for identifying racial disparities that were rooted in broader racial inequality (e.g., employment, education, or housing disparities) as opposed to police policies, psychologies, or behaviors. The metrics represent the ability to hold police accountable to the values of equality we should all share. And, with voluntary commitments from departments that cover around one-third of the United States by population—with so much enthusiasm among police and communities—I was already optimistic about where things were headed.

Then, recently, Google came into the picture, and my optimism soared. Along with a $5 million commitment, Google did what we most needed: pledged to help us automate the processes of data extraction, cleaning, standardization, and analysis. This means that the months-long slog of putting a report together for each department is likely to turn into a matter of hours or days. And that means that there will no longer be any reasonable excuse for a department who collects data on any of these factors to say they don’t know what they mean.

What CPE—and the field—needs now are analysts. Lots and lots of analysts. And we, at least, are hiring DataNerds who want to be JusticeNerds™. With departments now coming in by the state-load, we are inundated with confidential data that needs to be interrogated so that we can answer some of the most fundamental questions in policing like: what economic conditions predict racial disparities in police stops? When does housing segregation most influence police activity? And, how do race and gender intersect in predicting police use of force?

Doing this work is exciting. It’s energizing. And, most importantly, it staves off the temptation to despair when it seems that progress is in retreat. So, even if policing isn’t your thing, my goal in writing this is to encourage folks to consider being a professional JusticeNerd™, regardless (in addition to being a nerd in your spare time). Because my hope is that the JusticeNerds™ who have not yet connected to this work professionally have also not given up on it. I hope that as more folks who feel passionately about social justice—and geek out over data architecture and social science—think about how they want to make their money, that they will work to find employment at the intersections of tech and justice. Or that they will create those opportunities.

Our country needs professional JusticeNerds™, and we are in short supply. My colleagues at the Vera Institute, Measures for Justice, the PICO Network, the National Network for Safe Communities, PolicyLink, Urban Institute, the Police Foundation, as well as at CPE are almost constantly in need of a diverse array of the most creative and committed folks. Folks who may have felt dispirited, but who are willing to fight despair as a vocation. So, if you feel like I’ve been talking directly to you (or to someone you know), you’re probably right. And if you are looking to make your passions your fulltime gig, please do. The whole field is hiring. And the whole country needs us to.

People, I need to tell you all about the TED experience from last week. I promise it will happen soon.

In the meantime check out my latest Bloomberg View column:

How the Robot Apocalypse Will Go Down

My latest post on Bloomberg View:

United Airlines Exposes Our Twisted Idea of Dignity

Also last Friday I wrote an old-fashioned philosophical essay:

What If We Could Upload Books to Our Brains?

In today’s Bloomberg View column, I debate Noah Smith over one of his previous columns in which he claims that capitalism shrinks inequality. I don’t think the facts are on his side:

Debating Whether Capitalism Shrinks Inequality

I’m super happy to say that, according to the New York Times, Bannon was demoted yesterday in part because the President Bannon/ #PostcardToBannon campaign – which I wrote about back in February – really got under Trump’s skin. From the article:

Obviously Bannon hasn’t been kicked out of the White House, but I think we can all agree this is a step in the right direction.

Also, my newest Bloomberg View column is out, where I describe the idea behind Big Data jury selection and decide against it:

Big Data Won’t Make Juries Better

My buddy Brian Conrad alerted me yesterday to a very welcome three-way intersection:

Graphics courtesy of meta-chart.com

So, anyone surprised? I’m not. But I am excited. Here’s what we’ve got on Ravelry:

You can get the pattern for free here, and you can read and article about the concept here.

But, there’s more! Because knitting nerd activists are endlessly creative, we have the following generalizations on the above idea:

Combination pussy hat and resistor hat

Chemistry hat

DNA helix hat

Water molecule hat

March for Trees hat

Amazing!

All patterns available here.

This is a guest post by Angela Grammatas, a digital analytics consultant specializing in worldwide implementations of online analytics tools.  She loves powerful data, but doesn’t love having artificial intelligence use it in creepy ways. She also has synesthesia and paints numbers (Instgram @angelagrammatas).

This week, the US Congress voted to allow ISPs (Internet Service Providers) to collect and sell your internet data without your consent.  Erasing your web data – or not allowing any to be collected in the first place – is getting more difficult, and less effective.  Hiding from data collection isn’t working.

It’s time for a completely different approach.  Instead of restricting our data, it’s time to create more – a lot more.  A flood of meaningless data could create a noisy cover that makes our true behavior hard to understand.  This could be a path to bursting the filter bubble, one person at a time.  And if enough people participate, we could collectively render some datasets completely meaningless.

Why should we care about where our data goes?

Organizations rely on data to “target” users online and serve them relevant (read, “more likely to be clicked”) advertisements.  Plenty of targeting is innocuous and can be genuinely helpful.  For example, getting a sale offer on a recently-viewed product can be a win-win; the company makes a sale, and the customer is happy about the discount.  Targeting (and re-targeting) makes that possible.

But when the pool of data gets larger and more integrated, the implications change.  For example: let’s imagine that “Jane Internet” loves cats, and visits cats.com daily.  One day, she’s considering how to vote on a local proposition, and she does some research by visiting two political news sites at opposite ends of the spectrum.  She reads a relevant article on each site, getting a balanced view of the issue.  Let’s imagine that the “Yes on Prop A” campaign has access to retargeting capabilities that utilize that large, blended dataset.  Soon, Jane starts to see “Vote Yes on Prop A” advertisements on many unrelated websites, with the message that Prop A will be great for local wildlife.

Jane has no way of knowing this, but that pro-wildlife message has been chosen specifically for her, because of her past visits to cats.com.  The ads are everywhere online (for Jane), so Jane believes that this message is a primary “Yes on A” talking point, and she’s encouraged to vote in agreement.  The “No on A” campaign never has any opportunity to discuss or debate the point.  They may not even know that the cats-related topic has been raised, because they’ve never even been exposed to it – that message is reserved for retargeting campaigns directed at people like Jane.  Jane’s attempt to be a well-informed voter has been usurped by retargeting.  And, perhaps most importantly, Jane doesn’t even know this has happened.

How could meaningless data help?

Jane was targeted because of her visits to cats.com, and the (reasonable) assumption that cats.com visitors are animal lovers.  What if she’d spent just as much time visiting sites related to other topics – desserts.com, running.com, and supportthelibrary.com?  Many organizations want to access potential customers who are interested in desserts, running, and libraries.  If Jane was visiting all of those sites, she’d be seeing a variety of targeted messages, exposing her to different points of view while also decreasing the impact of any single message.  Jane would start to break out of the “filter bubble” created by targeted ads.  In that case, Jane may not see any ads related to Prop A – or she might see ads that address the issue from a variety of perspectives.  For Jane, the playing field would be leveled again.

But if Janes all over the country also began to visit a much wider variety of sites, they could level the playing field for everyone.  Targeting algorithms that identify “people like Jane” look for similarities in web browsing behavior, and assume that these people will have similar ad-clicking behavior.  If the dataset becomes more randomized, those correlations will be weaker, and even when similar groups are identified, they won’t result in as many clicks – driving the cost of the ads up, and reducing the incentive to retarget.

The reality, of course, is that Jane doesn’t have the time or inclination to spend hours clicking random links online just to create her own personal meaningless dataset.  That’s why I created Noiszy (http://noiszy.com), a free browser plugin that runs in the background on Jane’s computer (or yours!) and creates real-but-meaningless web data – digital “noise.”  It visits and navigates around websites from within the user’s browser, leaving your misleading digital footprints wherever it goes.

When organizations lose the ability to “figure us out” from our browsing data, they’ll have to work harder to build products and content that people willingly engage and share data with, rather than simply chasing clicks and impressions.  Could “fake data” lead to the end of “fake news”?  Targeting algorithms are happily churning away on our data, pushing whatever messages the highest bidder wants us to see, and we have no obvious way to feed back into the cycle.  Meaningless data can help us hack this system, and bring about a conversation we deeply need to have: how should algorithms be (re)built for the greatest good?

Please join us if you can!