Welcome to Public Health On Call, a podcast from the Johns Hopkins Bloomberg School of Public Health,

where we bring evidence, experience, and perspective to make sense of today's leading health challenges.

If you have questions or ideas for us, please send an email to public health question at jh.h.edu.

That's public health question at jh.u.edu for future podcast episodes.

This is Lindsay Smith Rogers.

I'm not sure if you've ever thought about algorithms, but after listening to this podcast,

you'll never think of them the same way again. Dr. Kadeja Ferryman is an assistant professor

and core faculty at the Berman Institute of Bioethics at Johns Hopkins. She speaks with Dr. Josh

Jarsstein about the algorithms all around us and how the risk of bias in these algorithms poses

a risk to our health. Let's listen. Dr. Kedesha Ferreiman, thanks for joining me on public health

on call. Now, usually when we talk about potentially dangerous, unseen challenges to public health, we're talking about microorganisms or viruses.

But today we're going to talk about algorithms.

Let's start with basic question.

What's an algorithm?

I think a great way to think about what an algorithm is, is to think about it as a recipe.

A recipe is a set of instructions, right? So if you're

going to make dinner, you know, you look at all of your different ingredients that you need. So there's

a list of ingredients and then there's the steps of sort of what you need to do. And that in a way is sort of,

you know, akin to what

algorithms are. So there are sets of ingredients, which we think of as data that we need to make

the algorithm run. And then there are a set of instructions, right? So there are a set of

instructions that tell the computer in this case, not the person making dinner, but tell

...