Tuesday, April 19, 2016

On Bias

cartoon; panel one, person one says: "Define bias." Person two says, "A careful review of the facts..." panel two, person two leaning into person one angrily, says, "...which results in any viewpoint other than my own!"

When I teach undergraduates (or anyone, really) about assessing websites for academic use, I start by asking them how they judge websites for themselves. "Pretend this has nothing to do with a class. Tell me how you decide to trust, or not trust, the information you get from the web." Inevitably, the first thing I hear is something about looking at the URL. Every class always talks about the upper-level domain name. Of course, they don't put it in such technological terms, but they talk about .com and .edu and such. I love this for a lot of reasons, but best of all it makes a perfect segue to the point I want to make: that all websites are biased.

Let me say that again: All. Websites. Are. Biased. Websites are created by people, and all people are biased. I repeat: All. People. Are. Biased. We all think we can escape our biases, that somehow *we're* the one who can be truly and completely objective. But that's not how human brains work. Let me lay some science on you... Depending on where and who you are, estimates say that your are receiving millions of distinct pieces of information, and your unconscious brain makes decisions about which ones are important enough to merit attention. If you're really smart and really aware, the things that merit attention can be measured in dozens. The average bear can pay attention to far fewer. And what do our brains do with all the other millions and millions of bits of info? That 3 pound chunk of meat in your skull looks for patterns so it can maps new things onto prior experiences. That lady ahead of you at the grocery store who aggravates you and you don't know why may remind you of that horrible teacher's aide from kindergarten who embarrassed you in front of your friends. Your new coworker about whom you have warm feelings before even getting to know him could bear a resemblance to your childhood best friend's uncle who bought you a second ice cream cone after you dropped your first. You'll dismiss the lady at the grocery store as "rude" and embrace your new coworker as "kind" without even realizing that something about them maps onto your childhood image of "rude" or "kind."

All this came back to mind when I read about a big company that is trying to do to control for unintentional biases in their efforts. It's a short read, but here are the highlights that felt pertinent to libraries:

  • "Focus on skills and eliminate distractions." I've done this by creating interview scripts and listing required skills and knowledge before starting an interview process. There are other ways this could work as well. 
  • "Acknowledge microaggression." Microaggressions are real. Microaggressions happen in libraries all the time. I know I've perpetrated some and I know I've been subject to others. I work hard, every single day, to eliminate them from my spoken and written lexicon and from my actions.
  • "Talk about it." To some extent, I talk with my staff and colleagues about bias. I have also worked to beef up our materials about Islam. We created a display about protest culture and civil rights leaders when #BlackLivesMatters first started to make national headlines. These aren't comfortable conversations to have, but they are important so we work on it.

That last point is so important. To bring this back to how I started today's post: when working with undergraduates, or anyone really, about assessing information sources, I talk to them about how they really need to do what they've already been doing - looking at authority, content, origins, timeliness, and bias (or who, what, where, when, and why) - but doing it intentionally. That's how I think about bias in libraries. I want to keep learning and improving. Intentionally. So should we all, don't you think?

And, by way of parting, here's one of my favorite jokes about acknowledging bias and privilege:

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