SINGAPORE — Before a TikTok video reaches the recommendation feed, machines get the first look, scanning its images, sounds and other signals for potential violations of the platform’s community guidelines. In the first quarter of this year, automated systems detected and removed 178 million, or 96.7 percent, of the 184 million videos taken down globally — before they were reviewed by human moderators. Overall, 99.3 percent of removed videos were taken down before users reported them, according to the platform. At TikTok’s Transparency and Accountability Center in Singapore, a demonstration shows how machine-learning systems scan uploaded videos for signals associated with potential violations, such as smoking, drinking, physical violence and extremist symbols, even before human moderators step in to review cases requiring greater context or judgment. The systems use more than visual recognition to also analyze audio, keywords and hyperlinks. The technology can also recognize body posture and actions, allowing it to combine multiple signals — such as a cigarette and a person
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