To fight hate speech and harassment, Facebook is using a new programming method that can churn out AI-powered content flagging algorithms at a faster rate.

On Wednesday, Facebook CTO Mike Schroepfer talked up an AI-building approach called "self-supervised learning," which promises to help the social network spot the offensive content in its ever-changing forms.

"The future here is pretty exciting," Schroepfer said while speaking at the company's annual developer conference. "We've seen a number of breakthroughs just in the last two years in the idea of self-supervised learning."

Currently, Facebook can only spot about half of the hate speech circulating over the platform before a user reports it. When it comes to online harassment and bullying, the percentage drops even lower to about 15 percent.

A big reason why is because the content isn't usually posted in plain, easy-to-understand language — at least not for a computer. "When it comes to the various (hate speech) terms, they can take on a very different meaning, depending on the context," Facebook analytics director Veronika Belokhvostova told PCMag.

Complicating the problem is how hate speech can be packaged in clever memes. The wording can also rapidly change based on current events and politics. For instance, "caravan" became a popular term when discussing US-bound migrants from Latin America.

Facebook's existing AI systems can already combat other forms of problematic content, such as spam, nudity and terrorist propaganda. However, the systems have been built with an older programming approach called "supervised learning." Essentially, this has involved taking large sets of data, such as pictures or video, and teaching the AI to recognize the various characteristics inside them, like whether a nipple is present or if graphic violence is depicted.

Although effective, supervised learning has a big drawback: the human programmer has to label all the data the AI is trained on, which can involve tagging every item inside a picture or video. "This is surprisingly manual and slow," Facebook's CTO said. "It can take you days to weeks to months to go through the whole process."

Self-supervised learning, on other hand, can let you essentially skip much of labeling process. Instead, the AI is programmed to first predict what might be present in the raw training data. The AI can then be fine-tuned with a smaller set of labeled training data.

As a result, a programmer may only need to collect and label 80-hours worth of training data as opposed to 12,000 to get their AI working. The same applies to building an AI to fight hate speech. According to Facebook's AI director Manohar Paluri, the approach has helped the company tackle various forms of hate speech using "ten times" less training data.

"This is the power of self-supervision," Paluri said while speaking at Facebook's developer conference. "It allows us to move fast."

Time will tell if the technology makes a difference. But some of Facebook's content moderation problems may have more to do with policy than content detection. For instance, in March, the company began banning the term "white nationalism" as hate speech, a move some critics claim was done far too late. Meanwhile, others have accused the social network of suppressing free speech in its mission to stop online abuse.

Facebook's CTO acknowledged that people might be skeptical of Facebook saying AI can help fix the company's problems. But Schroepfer said the ongoing advancements in artificial intelligence do make him optimistic for the future. "There isn't a simple answer, but there is a lot of work to do," he said. "The solutions are never going to be perfect, but we have to keep going."

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