Practical Lessons Learned From Detecting, Preventing and Mitigating Harmful Experiences on Facebook

Abstract

Social media's explosive growth brings with it a variety of societal risks ranging from severely harmful issues such as dangerous organizations and child sexual exploitation to moderately harmful content like displays of aggression, borderline nudity to benign or distasteful contents like gross videos and baity content. In recent times, the multitude and magnitude of these harms is being further exacerbated with the advent of generative AI [5]. Meta is committed to ensuring that Facebook is a place where people feel empowered to communicate and we take our role seriously in keeping abuse off the platform [7]. In this talk, I will describe practical challenges and lessons learned from tackling bad experiences for users on Facebook, particularly in the subjective, borderline and low quality spectrum of harms using state of the art, scalable machine learning approaches to content understanding, user behavior understanding and personalized ranking.