Hello everyone, welcome to the Mindscape podcast. I'm your host Sean Carroll. Everyone knows, I think,

that even though words like data and algorithm carry a certain patina of objectivity with them,

in the real world, it's often the case that neither the collection of data, nor the analysis of data,

nor the use of algorithms are completely objective. They have biases built into them because all

of these facts about the world or ideas about how data should be analyzed are created by human

beings and human beings have their foibles, right? And we see this in action in ways both sort of

profound and trivial. There are algorithms that decide who people should hire, who should be

suspected of committing crimes. Something we'll talk about in this podcast is crash test dummies.

When car crashes are done by car companies to test them for safety, it used to be that all of the

crash test dummies were modeled after men. None of them were in the shape or size of women,

and as a result, you could actually figure out exposed facto, the designs of seat belts and

things like that for cars were noticeably less effective for women than for men. So as objective

as we might try to be, we're going to fall a little bit short. Think of it this way. This is

one of the ways I like to think about it. You're standing somewhere right now, you're in a room

or you're outside or in your car, look around and imagine trying to describe your immediate

environment to somebody else in a completely objective way. You can imagine doing that, maybe you

think you can do that, but the fact is you can't. You can say objectively true things, right? There

are true things to say about the world. You're in a car, it's a Toyota, whatever it is, but you're

making choices along the way. There are an infinite number of things you could say that are objectively

true, but it's you who are always going to be a little bit fallible and have your biases, have

your history and your interests and so forth that choose for you what features of the environment

matter, right? How to divide up the environment into the interesting facts, the uninteresting facts,

...