A YouTube chat about chess got flagged for hate speech
The algorithms flagged around 1 percent of transcripts or comments as hate speech. Detecting hate speech or abuse is about more than just catching foul words and phrases. Choi says additional progress in detecting hate speech will require big investments and new approaches. No one has collected and annotated a high-quality data set of hate speech or abuse that includes lots of "Edge cases" with ambiguous language. Such as "At 1:43, if white king simply moves to G1, it's the end of black's attack and white is only down a knight, right?" were judged 90 percent likely not hate speech. The statement "White's attack on black is brutal. White is stomping all over black's defenses. The black king is gonna fall" was judged more than 60 percent likely to be hate speech. It remains unclear how often content may be mistakenly flagged as hate speech on YouTube and other platforms.