When AI dialogue gets lost in translation from American public media, this is marketplace

tech. I'm Megan McCarty Karino. We hear words like safety and transparency thrown around a lot in the AI industry, but they don't always

mean the same things to tech insiders as they do to the rest of us. Take for

instance a 2016 paper by a roster of prominent AI researchers with

open AI Google and anthropic on their resumes. It's called concrete problems in AI safety, but as tech journalist Karen Howe points out,

the safety problems described might not be the ones consumers or policy makers are most worried about, but rather the risk that machine learning systems

could behave in unintended ways.

She wrote a whole glossary of terms like this a few years ago for MIT Technology Review.

When we talk about safety in the public domain,

you would automatically think of,

oh, this system isn't going to harm me.

It's not going to violate my privacy, it's not going to make judgments

on me that could greatly negatively impact my life. But in the paper, it explicitly says that none of these things are what they mean by safety.

It just refers to, for them, essentially preventing AI from becoming rogue and misaligned.

Of course, because of this confusion, companies can say we are investing a lot in AI safety we care a lot about keeping AI safe

and that does a lot of work for them in the public assuming that all of these

things are taken care of.

Right, what are some of the harms that the public might think they would like to be

protected from?

Yeah, one of the things that we've seen a lot of research done recently, both in academia and in journalism,

is how image generators can create really racist stereotypical images. These kind of like embedded

biases have a really big impact on the content that we are going to continue seeing on the internet because these systems are being used to generate enormous volumes of images and text.

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