Machine Learning Times
Machine Learning Times
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2 years ago
The Shift to Generalized AI to Better Identify Violating Content

Originally published in Meta AI, Nov 9, 2021.

Addressing content that violates our Community Standards and Guidelines is one of the top priorities at Meta AI. Over the past five years, AI has become one of the most effective tools for reducing the prevalence of violating content, or the amount of violating content that people see on our platforms. AI systems have typically been single-purpose, each designed for a specific content type, language, and problem, like detecting misinformation or flagging hate speech violations, and they require varying amounts of training data and different infrastructure. Groups of bespoke systems result in high compute resources and maintenance complexity, which slows the process of updating systems to quickly address new, evolving challenges. But today, one of the biggest challenges in our integrity work is to build not more bespoke AI systems but fewer, more powerful ones.

AI models that can combine signals across multiple systems help AI make new connections and improve content understanding. This also makes integrity systems more efficient by making better use of compute resources — which, crucially, allows us to respond more rapidly to new issues.

This year, we deployed a new cross-problem system that tackles three different but related violations: hate speech, bullying and harassment, and violence and incitement. It’s clear that these issues overlap — bullying is often connected to violence and incitement, which can involve hate speech. By generalizing the AI across the three violations, our system has developed a broader understanding of all three separate problems, outperforming previous individual classifiers. This consolidation has helped reduce hate speech prevalence over the past six months, as reported in our Community Standards Enforcement Report. We use technology to reduce the prevalence of hate speech in several ways: It helps us proactively detect it, route it to our reviewers, and remove it when it violates our policies. We also saw a direct impact in how quickly we bring classifiers to new languages. While previous systems typically take months to create separate classifiers for each market, we replaced existing classifiers with our cross-problem systems in many markets within weeks, without needing additional hardware to run the new advanced models.

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