Dataclysm: Who We AreDataclysm: Who We Are by Christian Rudder

My rating: 5 of 5 stars

I’m a sucker for small, data-driven descriptions.

As a writer, I’ve come to distrust anecdotal arguments meant to paint a broad picture about”the way we are” are people. As a professor who works in the digital humanities, I’ve seen too many arguments that use individual experiences as way to paint large portraits of a world that seems to be describing a particular political idea.

As such, I prefer the world of big data, which is how I came across Dataclysm by Christian Rudder, one of the founders of the OKCupid dating site.

Rudder delves into the big data of its members without adding unwarranted political descriptors. Instead, he digs into the data — both the self-reported data and the data of action — to draw pictures of who we are in terms of race, gender, and sexuality.

In many ways, the data isn’t surprising.

For instance, men tend to prefer younger women (20-24) regardless of age, and women tend to prefer slightly older men until they turn 30 when they prefer slightly younger men. For many who make arguments about the dysfunctional nature of society, this is proof of the thesis that women are held to a ridiculous standard of unattainable beauty. (And as Rudder points out, the accompanying data about who receives messages on the site paints a pictures that mirrors that.)

But the data shows something deeper than just how we view profile pictures.

When OKCupid launched a blind dating app, which paired people together for spontaneous dates without providing profile pictures, a different set of data emerged. It turns out that when people are pushed into action in real life, the expected societal forces didn’t necessarily translate on an individual level.

No matter which person was better-looking or by how much—even in cases where one blind-dater was a knockout and the other rather homely—the percent of people giving the dates a positive rating was constant. Attractiveness didn’t matter. This data, from real dates, turned everything I’d seen in ten years of running a dating site on its head.

So underneath the social pressures, we may be better people than we think.

Unfortunately, those outside forces may be stronger than we willingly admit, as Rudder’s deep dive into race showed.

Women, for instance, were less likely to date outside their own race. (Or to put it another way: They were more likely to date somebody of their own race.) I was not expecting that particular finding, in part because I assumed that women would be more likely to consider factors outside of appearance. (Truthfully, maybe they do. Rudder and the data are silent on that, which means my assumption on this matter was my own.)

But a more insidious finding came soon after. All non-white races ranked white as its second preference, and all races illustrated a “black beauty penalty.”

Does this illustrate a white bias in America? Again, Rudder is silent on that issue, but it’s not hard to argue that this data quantifies in a very specific way that white beauty is valued. (It’s probably not difficult to make the leap that this white beauty is valued because of privileged social standing.)

What the data does show is that this pervasive, hidden (to many) bias isn’t relegated just to the Deep South. This is baked into the fabric of our world.

When I show here that black women and later, black men, get short shrift, and that adding whiteness to a user’s identity makes him or her more attractive, I’m not describing some Ozark fever dream. I’m describing our world, mine and yours.

Another surprising find: Youth may not be the answer to the race problem. This “black penalty” is equally pervasive in today’s youth culture, in which the “data shows non-blacks discount African American profiles.” (In other words: While the children may be the future, our future may look decidedly like our present.)

As he continually reminded the reader, Rudder is simply presenting quantified data, which gives a 10,000-foot view of the complex interactions we have by smoothing off the edges to give us a generalized view of the world. He’s not diagnosing problems, or arguing that these data sets give a complete and accurate assessment of the world. Instead, he’s offering up a snapshot of the data that shows how we live now.

As I read, though, it was hard not to see patterns. I found myself (dangerously) bringing my own conjecture to the book.

I couldn’t help but wonder if Rudder’s data didn’t help explain why people reject social arguments such as “Women face unrealistic beauty expectations” or “White culture is privileged in America.”

If we see, for instance, that all men tend to list as a beauty preference 20-24 year old women, we can reasonably conclude that a woman who sees that data may try to alter her appearance to stay in that sweet spot, no matter how unattainable that is. We might also reasonably conclude that a man who went on an enjoyable blind date matched with somebody based upon different criteria, might reject the notion of the “beauty myth” based upon his own experience.

Which is then more real: The experience of the “hidden forces” of beauty the woman faces, or the “real” experience of the matched blind date?

Rudder’s book helped articulate that duality that is sometimes lacking in our social science. (As my wife likes to tell me: “We can both be right.”)

Yet even with the duality, I was floored by the data that showed how gender and race are filtered by LinkedIn, a professional business site. In a place where looks and race should be irrelevant, both had more than a negligible impact on who received job offers. It’s not that I discounted the “hidden forces,” I was simply not prepared for how pervasive those factors were.

Dataclysm is worth the read just for this section.

This is what makes Rudder’s book so fascinating and important.

Our “hidden” movements can now be gathered, quantified, and used to help us understand how the forces that surround us shape the ways in which we live now. And for us to be okay with the duality of these two ideas — hidden forces and real experiences — we need to see what the data is telling us.

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