Evidence receipt / evaluation
Published · transcript-backedLenny Rachitsky: evaluation
27 Feb 2025 Lenny's Podcast An inside look at X’s Community Notes | Keith Coleman (VP of Product) and Jay Baxter (ML Lead)
“I've never seen a Community Note that is wrong and breaking that promise is a big deal.”
Source trail
Everything needed to verify it.
- Speaker
- Lenny Rachitsky
- Attribution
- Verified speaker
- Claim type
- evaluation
- Recorded
- 27 Feb 2025
- Publisher
- Lenny's Podcast
Transcript context
…Yeah. And it is set right now, by the way, to be really conservative, I think. We just are pretty particular about quality and we really want note quality to be really high. I think Keith and I both believe that we live or die based on the quality of the notes at the end of the day. So we'd rather not show a note that maybe good, but we didn't have enough signal on than the other way around. That makes so much sense. I've never seen a Community Note that is wrong and breaking that promise is a big deal. So I completely get why you guys are super conservative there. Okay. Two more questions [inaudible 00:19:53] because I'm just curious. These weren't on my list of questions to ask, but I feel like people wonder this. How many notes are written versus end up showing up and triggering on a- We probably show about 8% of notes that get proposed. It's been between, let's say, 7% and 10% or 11%, something like that over time. The number can vary a little bit. And as Jay said, there are undoubtedly... And you can see it, there's clearly more good notes than we show, but the goal is to hold a really high bar. We want to show a note when it's going to be helpful, when it's not going to appear biased and undermine trust in the system. We want these to be neutral, informative, helpful. And as Jay was saying, we view the worst possible mistake as showing a bad note because that's going to undermine trust and the trust is why people like the product. So yeah, the bar is there. And like I said, there's clearly some in that remaining, let's call it 90%, that are good. And then, there's a lot that are just not that great and there's some that are bad. And if you write one of these ones that are bad which bad being defined as people who normally disagree find the note not helpful, so it's like the inverse of the ones we show. If you write one that people normally disagree, find not helpful, you actually will ultimately lose your ability to write and have to earn it back. That other 90% is a mix. Sometimes people look at the number, they're like, "Oh, why don't you show more?" It's like, "Well, you probably actually don't really want us showing most of those." The gold here is that the system is able to filter out the good ones.…
Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.