CyberWell released new guidance for social media companies earlier this month on improving artificial intelligence content moderation to better target antisemitism, while protecting free expression.

The guidance was submitted as a public comment this month in response to a call for input from Meta’s Oversight Board on the use of large language models (LLMs) in content moderation, addressing both the potential efficiencies of automated review and the persistent challenges of interpreting nuanced content at scale.

“AI gives platforms an unprecedented opportunity to improve the enforcement of their own rules at scale, moving beyond slur lists toward context-based decisions that can help reduce human error. However, expert input is crucial to ensuring these systems can effectively address specific forms of online hate,” CyberWell founder and CEO Tal-Or Cohen Montemayor said.

“Antisemitism constantly evolves through coded language, symbols, cultural references, and forms of ‘algospeak’ designed to evade automated detection. AI moderation systems must draw on contextual, linguistic, and subject-matter expertise to recognize the antisemitic message, not just the words, while protecting legitimate expression. Social media platforms have also struggled to draw the line between anti-Jewish scapegoating – a classic form of antisemitism– and misinformation and disinformation campaigns that receive different moderation treatment,” Cohen Montemayor added.

In its submission, Cyberwell detailed several technical challenges that automated systems frequently fail to catch.

The report noted that online bad actors use modern AI tools to create tricky new versions of hate speech, meaning computer programs have to understand hidden meanings instead of just searching for banned words.

For example, automated safety filters often miss posts that combine harmless-looking images with hidden insults, such as an AI-generated video showing Adolf Hitler dancing in front of the Auschwitz concentration camp alongside text celebrating the mass murder of Jewish people.

Posts like these also frequently slip past automated checks by using innocent symbols in place of words, such as using a juice box emoji because it sounds similar to the word Jews when spoken.

The research revealed that internet users often try to hide hateful content by pretending they are just jokes, memes, or educational material.

For instance, automated filters often struggle to catch posts that mock the Holocaust by wrapping denialist ideas in absurd scenarios, such as a video calculating how difficult it would be to bake six million pizzas in five years.