Facebook Trains AI on ‘Hateful Memes’

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Facebook unveiled an initiative Tuesday to take on “hateful memes” by utilizing synthetic intelligence, backed by crowd sourcing, to establish maliciously motivated posts.

The main social community mentioned it had already created a database of 10,000 memes – photos typically blended with textual content to ship a selected message – as a part of a ramped-up effort in opposition to hate speech.

Facebook mentioned it was releasing the database to researchers as a part of a “Hateful Memes Challenge” to develop improved algorithms to detect hate-driven visible messages, with a prize pool of $100,000 (roughly Rs. 75.Four lakh).

“These efforts will spur the broader AI research community to test new methods, compare their work, and benchmark their results in order to accelerate work on detecting multimodal hate speech,” Facebook mentioned in a weblog submit.

Facebook’s effort comes because it leans extra closely on AI to filter out objectionable content material in the course of the coronavirus pandemic that has sidelined most of its human moderators.

Its quarterly transparency report mentioned Facebook eliminated some 9.6 million posts for violating “hate speech” insurance policies within the first three months of this yr, together with 4.7 million items of content material “connected to organised hate.”

Facebook mentioned AI has change into higher tuned at filtering because the social community turns extra to machines because of the lockdowns.

Guy Rosen, Facebook vp for integrity, mentioned that with AI, “we are able to find more content and can now detect almost 90 percent of the content we remove before anyone reports it to us.”

Facebook mentioned it made a dedication to “disrupt” organised hateful conduct a yr in the past following the lethal mosque assaults in New Zealand which prompted a “call to action” by governments to curb the unfold of on-line extremism.

Automated methods and synthetic intelligence may be helpful, Facebook mentioned, for detecting extremist content material in varied languages and analysing textual content embedded in photos and movies to grasp its full context.

Mike Schroepfer, Facebook’s chief expertise officer, instructed journalists on a convention name that one of many methods serving to this effort was a system to establish “near identical” photos, to handle the reposting of malicious photos and movies with minor modifications to evade detection.

“This technology can detect near perfect matches,” Schroepfer mentioned.

Heather Woods, a Kansas State University professor who research memes and extremist content material, welcomed Facebook’s initiative and inclusion of outdoor researchers.

“Memes are notoriously complex, not only because they are multimodal, incorporating both image and text, as Facebook notes, but because they are contextual,” Woods mentioned.

“I imagine memes’ nuance and contextual specificity will remain a challenge for Facebook and other platforms looking to weed out hate speech.”

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