To overcome the barriers of traditional detection methodologies, we propose a new framework: rather than relying on AI to detect at scale and humans to review edge cases, an intelligence-based approach is crucial.

By bringing human-curated, multi-language, off-platform intelligence into learning sets, AI will then be able to detect nuanced, novel abuses at scale, before they reach mainstream platforms. Supplementing this smarter automated detection with human expertise to review edge cases and identify false positives and negatives and then feeding those findings back into training sets will allow us to create AI with human intelligence baked in. This more intelligent AI gets more sophisticated with each moderation decision, eventually allowing near-perfect detection, at scale.

What is this about? What's new?

The way censorship is done these days is that each Internet platform, such as Twitter, has its own moderation team and a decision making engine. Twitter would only look at tweets by any specific twitter user, when deciding on whether to delete any tweets or suspend their authors. Twitter moderators do NOT look at Gettr or other external websites.

So, for example, user @JohnSmith12345 may have a Twitter account and narrowly abide by Twitter rules, but at the same time have a Gettr account where he would publish anti-vaccine messages. Twitter would not be able to suspend @JohnSmith12345's account. That is no longer acceptable to the WEF because they want to silence people and ideas, not individual messages or accounts.