Two speakers discuss how AI systems like Grammarly balance filtering harmful content without over-censoring. They examine how the platform adapted to new biases during Covid-19 and the ethical challenges of deciding what language to flag.
I'm curious about when you were speaking about responsibility and this sensitive text feature. I guess, kind of to play devil's advocate, where for you is the line between filtering out, like potentially triggering speech and like, censorship and these AIs sort of like taking, you know, limiting. Yeah. Access to certain. Access ideas.
Yeah, it's a great question. And it's a question we ask ourselves every single day. I don't know that there's any perfect line etched in stone or in paint or whatever the metaphor is. We have a team of linguists. They are tracking and studying authoritative sources of what constitutes different kinds of bias, hate speech. We try to assemble from a set of these sources sort of rules of the road. What is the thinking around this? What's the research, what's the scholarship? And how can we apply that in a meaningful way in the product? And that changes. So there's not like you do it and you say, okay, check the box, I'm done. There's a constant evaluation. When Covid became prevalent, one of the things that was new was lots of biased ways to refer to Covid, and that became a thing. And so Grammarly's sensitive text classifier caught those things and was able to suggest to users. I mean, at the end of the day, we can't write for you, but we will suggest to you that, hey, the thing you're writing is actually considered pretty biased as a way to refer to this virus. And so maybe you should consider. And here's why, and you should consider this other thing. So this is a thing that keeps changing and adapting, and we are trying to be as close to current as we can.
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