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Safety Operations Center leverages L1ght’s best-of-breed content safety AI to reduce internal and external moderation OpEx by up to 92%, while simultaneously dramatically reducing moderator exposure to unsafe content. This is achieved by continuously retraining data models to increase detection accuracy, and reduce resource consumption — whether computational, or human.
A core building block of Safety Operation Center, contextuality is derived by applying multi-dimensional layers of analysis upon every content asset, whether it be an image, a video, or a piece of text. Doing so — in a GDPR and COPPA compliant manner — allows us to apply important decision making granularity that dramatically improves accuracy in determining whether content is benign, or unsafe.
Key to Safety Operations Center is the ability to use this feedback and analysis to allow moderation teams to recalibrate computational and human resources in real-time to more pressing Trust & Safety issues while shifting focus from more benign ones.
Results are fed into the Safety Operation Center dashboard for cubing, and data analysis by regions anonymized information and UGC type.
Safety Operations Center is used by Trust & Safety Teams, Moderators, and Community Managers to identify problematic content access these verticals: