Truth and Prediction in the Dataclysm

The Deluge by Francis Danby. 1837-1839

Last time I looked at the state of online dating. Among the figures was mentioned was Christian Rudder, one of the founders of the dating site OkCupid and the author of a book on big data called Dataclysm: Who We Are When We Think No One’s Looking that somehow manages to be both laugh-out-loud funny and deeply disturbing at the same time.

Rudder is famous, or infamous depending on your view of the matter, for having written a piece about his site with the provocative title: We experiment on human beings!. There he wrote: 

We noticed recently that people didn’t like it when Facebook “experimented” with their news feed. Even the FTC is getting involved. But guess what, everybody: if you use the Internet, you’re the subject of hundreds of experiments at any given time, on every site. That’s how websites work.

That statement might set the blood of some boiling, but my own negative reaction to it is somewhat tempered by the fact that Rudder’s willingness to run his experiments on his sites users originates, it seems, not in any conscious effort to be more successful at manipulating them, but as a way to quantify our ignorance. Or, as he puts it in the piece linked to above:

I’m the first to admit it: we might be popular, we might create a lot of great relationships, we might blah blah blah. But OkCupid doesn’t really know what it’s doing. Neither does any other website. It’s not like people have been building these things for very long, or you can go look up a blueprint or something. Most ideas are bad. Even good ideas could be better. Experiments are how you sort all this out.

Rudder eventually turned his experiments on the data of OkCupid’s users into his book Dataclysm which displays the same kind of brutal honesty and acknowledgement of the limits of our knowledge. What he is trying to do is make sense of the deluge of data now inundating us. The only way we have found to do this is to create sophisticated algorithms that allow us to discern patterns in the flood.  The problem with using algorithms to try and organize human interactions (which have themselves now become points of data) is that their users are often reduced into the version of what being a human beings is that have been embedded by the algorithm’s programmers. Rudder, is well aware and completely upfront about these limitations and refuses to make any special claims about algorithmic wisdom compared to the normal human sort. As he puts it in Dataclysm:

That said, all websites, and indeed all data scientists objectify. Algorithms don’t work well with things that aren’t numbers, so when you want a computer to understand an idea, you have to convert as much of it as you can into digits. The challenge facing sites and apps is thus to chop and jam the continuum of the of human experience into little buckets 1, 2, 3, without anyone noticing: to divide some vast, ineffable process- for Facebook, friendship, for Reddit, community, for dating sites, love- into a pieces a server can handle. (13)

At the same time, Rudder appears to see the data collected on sites such as OkCupid as a sort of mirror, reflecting back to us in ways we have never had available before the real truth about ourselves laid bare of the social conventions and politeness that tend to obscure the way we truly feel. And what Rudder finds in this data is not a reflection of the inner beauty of humanity one might hope for, but something more like the mirror out of A Picture of Dorian Grey.

As an example take what Rudder calls” Wooderson’s Law” after the character from Dazed and Confused who said in the film “That’s what I love about these high school girl, I get older while they stay the same age”. What Rudder has found is that heterosexual male attraction to females peaks when those women are in their early 20’s and thereafter precipitously falls. On OkCupid at least, women in their 30’s and 40’s are effectively invisible when competing against women in their 20’s for male sexual attraction. Fortunately for heterosexual men, women are more realistic in their expectations and tend to report the strongest attraction to men roughly their own age, until sometime in men’s 40’s where males attractiveness also falls off a cliff… gulp.

Another finding from Rudder’s work is not just that looks rule, but just how absolutely they rule. In his aforementioned piece, Rudder lays out that the vast majority of users essentially equate personality with looks. A particularly stunning women can find herself with a 99% personality rating even if she has not one word in her profile.

These are perhaps somewhat banal and even obvious discoveries about human nature Rudder has been able to mine from OkCupid’s data, and to my mind at least, are less disturbing than the deep seated racial bias he finds there as well. Again, at least among OkCupid’s users, dating preferences are heavily skewed against black men and women. Not just whites it seems, but all other racial groups- Asians, Hispanics would apparently prefer to date someone from a race other than African- disheartening for the 21st century.

Rudder looks at other dark manifestations of our collective self than those found in OkCupid data as well. Try using Google search as one would play the game Taboo. The search suggestions that pop up in the Google search bar, after all, are compiled on the basis of Google user’s most popular searches and thus provide a kind of gauge on what 1.17 billion human beings are thinking. Try these some of which Rudder plays himself:

“why do women?”

“why do men?”

“why do white people?”

“why do black people?”

“why do Asians?”

“why do Muslims?”

The exercise gives a whole new meaning to Nietzsche’s observation that “When you stare into the abyss, the abyss stares back”.

Rudder also looks at the ability of social media to engender mobs. Take this case from Twitter in 2014. On New Years Eve of that year a young woman tweeted:

“This beautiful earth is now 2014 years old, amazing.”

Her strength obviously wasn’t science in school, but what should have just led to collective giggles, or perhaps a polite correction regarding terrestrial chronology, ballooned into a storm of tweets like this:

“Kill yourself”

And:

“Kill yourself you stupid motherfucker”. (139)

As a recent study has pointed out the emotion second most likely to go viral is rage, we can count ourselves very lucky the emotion most likely to go viral is awe.

Then there’s the question of the structure of the whole thing. Like Jaron Lanier, Rudder is struck by the degree to which the seemingly democratized architecture of the Internet appears to consistently manifest the opposite and reveal itself as following Zipf’s Law, which Rudder concisely reduces to:

rank x number = constant (160)

Both the economy and the society in the Internet age are dominated by “superstars”, companies (such as Google and FaceBook that so far outstrip their rivals in search or social media that they might be called monopolies), along with celebrities, musical artist, authors. Zipf’s Law also seems to apply to dating sites where a few profiles dominate the class of those viewed by potential partners. In the environment of a networked society where invisibility is the common fate of almost all of us and success often hinges on increasing our own visibility we are forced to turn ourselves towards “personal branding” and obsession over “Klout scores”. It’s not a new problem, but I wonder how much all this effort at garnering attention is stealing time from the effort at actual work that makes that attention worthwhile and long lasting.

Rudder is uncomfortable with all this algorithmization while at the same time accepting its inevitability. He writes of the project:

Reduction is inescapable. Algorithms are crude. Computers are machines. Data science is trying to make sense of an analog world. It’s a by-product of the basic physical nature of the micro-chip: a chip is just a sequence of tiny gates.

From that microscopic reality an absolutism propagates up through the whole enterprise, until at the highest level you have the definitions, data types and classes essential to programming languages like C and JavaScript.  (217-218)

Thing is, for all his humility at the effectiveness of big data so far, or his admittedly limited ability to draw solid conclusions from the data of OkCupid, he seems to place undue trust in the ability of large corporations and the security state to succeed at the same project. Much deeper data mining and superior analytics, he thinks, separate his efforts from those of the really big boys. Rudder writes:

Analytics has in many ways surpassed the information itself as the real lever to pry. Cookies in your web browser and guys hacking for your credit card numbers get most of the press and our certainly the most acutely annoying of the data collectors. But they’ve taken hold of a small fraction of your life and for that they’ve had to put in all kinds of work. (227)

He compares them to Mike Myer’s Dr. Evil holding the world hostage “for one million dollars”

… while the billions fly to the real masterminds, like Axicom. These corporate data marketers, with reach into bank and credit card records, retail histories, and government fillings like tax accounts, know stuff about human behavior that no academic researcher searching for patterns on some website ever could. Meanwhile the resources and expertise the national security apparatus brings to bear makes enterprise-level data mining look like Minesweeper (227)

Yet do we really know this faith in big data isn’t an illusion? What discernable effects that are clearly traceable to the juggernauts of big data ,such as Axicom, on the overall economy or even consumer behavior? For us to believe in the power of data shouldn’t someone have to show us the data that it works and not just the promise that it will transform the economy once it has achieved maximum penetration?

On that same score, what degree of faith should we put in the powers of big data when it comes to security? As far as I am aware no evidence has been produced that mass surveillance has prevented attacks- it didn’t stop the Charlie Hebo killers. Just as importantly, it seemingly hasn’t prevented our public officials from being caught flat footed and flabbergasted in the face of international events such as the revolution in Egypt or the war in Ukraine. And these later big events would seem to be precisely the kinds of predictions big data should find relatively easy- monitoring broad public sentiment as expressed through social media and across telecommunications networks and marrying that with inside knowledge of the machinations of the major political players at the storm center of events.

On this point of not yet mastering the art of being able to anticipate the future despite the mountains of data it was collecting,  Anne Neuberger, Special Assistant to the NSA Director, gave a fascinating talk over at the Long Now Foundation in August last year. During a sometimes intense q&a she had this exchange with one of the moderators, Stanford professor, Paul Saffo:

 Saffo: With big data as a friend likes to say “perhaps the data haystack that the intelligence community has created has grown too big to ever find the needle in.”

Neuberger : I think one of the reasons we talked about our desire to work with big data peers on analytics is because we certainly feel that we can glean far more value from the data that we have and potentially collect less data if we have a deeper understanding of how to better bring that together to develop more insights.

It’s a strange admission from a spokesperson from the nation’s premier cyber-intelligence agency that for their surveillance model to work they have to learn from the analytics of private sector big data companies whose models themselves are far from having proven their effectiveness.

Perhaps then, Rudder should have extended his skepticism beyond the world of dating websites. For me, I’ll only know big data in the security sphere works when our politicians, Noah like, seem unusually well prepared for a major crisis that the rest of us data poor chumps didn’t also see a mile away, and coming.

 

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2 comments on “Truth and Prediction in the Dataclysm

  1. […] Pulling the Dark Net out of the bin. Intelligence outsourcing. What Snowden did. Hackers and the military don’t get along. Spook girls. Dataclysm. […]

  2. […] however, sees the kinds of dark rhetoric that is so often found on the internet, as less a reflection of human darkness than something that arose out of the anarchic spirit of the […]

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