Hey there, I'm Kimberly Adams. Welcome to Make Me Smart, where none of us is as smart

as all of us.

I'm Karah Rizdolat's Tuesday, which usually means deep dive into a single topic with a single

expert. And we all get made smart about something that is super relevant in that moment

or just kind of a cool thing to know about. We bounce back and forth. But something a

little bit different today because scheduling. So we couldn't actually be here here to do

the thing that we usually do on Tuesdays. But we still want to do what we usually do

on Tuesdays, which is make it smart, even with a little break in format.

Right. So regular listeners of the show might remember the deep dive we did a while back

on credit scores and how using algorithms to determine someone's credit worthiness was

originally meant to remove like chances of bias and discrimination from that decision,

because you should just have to walk into a bank and stare at somebody. But using these

algorithms and this big data method doesn't always work quite that way. So I was not quite

done thinking about that after our deep dive. And so the folks over at the Marketplace

Tech team, we all did a whole series about credit scores and the algorithms behind

them and how those credit scores shape our financial reality today. And the first thing

you need to know is that the algorithm that's being used to build your credit score is itself

built to make assumptions about broader financial trends and your place in them.

Many times credit scores are built on history of all kinds of other aggregate data. So people

who look like you, Safian Noble is a professor of gender studies and African American studies

at UCLA. She writes about how algorithms can perpetuate racism and gender bias.

And this is where we start to get in trouble because if you are part of a group that has

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