(light music) - [Man] There are a lot of people who are scared of math and engineering, and they're like, well, I can't really participate in data, or data science, if you will. But the point that you're bringing up is think about the ethical implications of data science and participate in that way. - Yeah, so I appreciate people who say they are not good at math, and they're not good at science, and all those things.
Um, but I also disagree with them in a sense that they can't become good at math, or they can't become good at science. In the sense that they can't, I think they can be inspired by it, and they can see it, they can learn it into the level and depth that they want to learn it. But I also fundamentally believe that with data, and the way we present data, the way we explain things, we can help people get a better understanding of how data's going to impact their world.
And this gets you to just a fundamental question of what does it mean to have literacy around data. And we have to help people see that. How do you make the trade-offs of taking one medication versus another, one medical treatment versus another. Those are our profound differences that rely on datasets. When you look at the results around drug medications and side effects and all those things. What of the population of that data, what is that data based on? What's a clinical trial? So who's in a clinical trial? Turns out mostly middle aged white man.
Why is it middle aged white men? Because where are the hospitals that can afford to do these clinical trials, is one. Two, who pushes for someone to be in a clinical trial at that stage? Especially in critical care. It's the spouses, so women. And why aren't women in there? Because they don't have someone necessarily advocating for them. The basis of much of our pharmaceutical medicine is overly biased to one small set, doesn't include half of the population, women.
Let alone other types of minorities. So how do we start getting to a better place about understanding what those kind of implications are. (light music)
Skill Level Appropriate for all
Bracketology Club: Using March Madness to Learn Data Sciencewith Brian Tonsoni12m 7s Appropriate for all
The Data Science of Government and Political Science, with Barton Poulsonwith Barton Poulson1h 2m Appropriate for all
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