Jay Baxter, founding ML engineer for Community Notes at X, discusses the measurable efficacy of fact-checking notes with Lenny Rachitsky.
Insight
Baxter: Bridging agreement between polarized users ensures accuracy and neutrality
“I think the key thing really that we do is we actually look for agreement from people who have disagreed in the past. And what we see is when people actually have that sort of surprising agreement, that's what makes the notes so neutral and accurate and well w…”
Assertion Not checkable as stated
Baxter: Bridging algorithms enable crowdsourced moderation without external fact-checker labels
“If you said I think like back in 2020, before we started building anything here, whether this could work at all, I think a room of ML engineers would say, oh, you have to keep it closed source. You know, people are going to be manipulating this all the time. Y…”
Assertion Supported
Baxter: Research shows crowdsourced lay fact-checkers match professional fact-checkers
“There was, you know, original research you know, before, before birdwatch even started or community notes even started from external researchers showing that, you know, crowdsource fact checkers can do, you know, lay people can do about as well as Fact checker…”
Assertion Supported
Baxter: X core ranking algorithm does not demote noted posts
“Although we don't actually use the fact that a post was noted in the core ranking algorithm”
Insight
Baxter: Corporate OKR planning took longer than completing entire goals
“The whole OKR determination and planning process took longer than it would take us to pick a goal and then execute it on it and finish it.”
Assertion Contradicted
Baxter: Community Notes' initial PageRank algorithm amplified majority bias
“The actual first algorithm that I put into production was very focused on anti manipulation. It was this kind of page rank variant but it didn't solve the problem of, you know, bias basically. So if there are some, if there are more users on one side, the, a p…”