Contributed talk
in
Workshop: Challenges in Deploying and Monitoring Machine Learning Systems

Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms

Vashist Avadhanula · Omar Abdul Baki · Hamsa Bastani · Osbert Bastani · Caner Gocmen · Daniel Haimovich · Darren Hwang · Dmytro Karamshuk · Thomas Leeper · Jiayuan Ma · Gregory macnamara · Jake Mullet · Christopher Palow · Sung Park · Varun S Rajagopal · Kevin Schaeffer · Parikshit Shah · Deeksha Sinha · Nicolas Stier-Moses · Ben Xu


Abstract:

We describe the current content moderation strategy employed by Meta to remove policy-violating content from its platforms. Meta relies on both handcrafted and learned risk models to flag potentially violating content for human review. Our approach aggregates these risk models into a single ranking score, calibrating them to prioritize more reliable risk models. A key challenge is that violation trends change over time, affecting which risk models are most reliable. Our system additionally handles production challenges such as changing risk models and novel risk models. We use a contextual bandit to update the calibration in response to such trends. Our approach increases Meta's top-line metric for measuring the effectiveness of its content moderation strategy by 13%.

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