Absurd: China relies on mind-reading helmets for the "porn police"


AI makes too many mistakes when scouring the Chinese network for pornographic content, human controllers tire too quickly. Now researchers on behalf of the state are devising a solution: humans and AI are working together on the hunt for porn.

Humans recognize porn, AI knows humans

The seemingly limitless mass surveillance in China shows the limits of automated systems. The country has strict laws regarding pornography, compliance with which has so far been monitored on two levels: artificial intelligence scans the heavily shielded Chinese network for such content, and a task force known as the “porn police” carries out censorship with eyes and hands, so to speak. As the South China Morning Post (Smp via hot) now writes, a technical aid was developed here on behalf of the state, which brings man and machine together here in a curious way.
Sample for training a ‘porn police’ helmet

The initial problem: The AI ​​used in China still makes too many mistakes when recognizing pornographic content, the human experts of the “porn police” can check and correct them, but because of the sheer mass of content and the monotony of the task, they also come these quickly to their limits. The approach of a team from Beijing Jiaotong University: A helmet that analyzes brain waves and recognizes when the wearer is looking at pornographic images. For the first tests with the device, fifteen male university students between the ages of 20 and 25 were seated in front of a computer screen. Each time a “sensitive photo” appeared, the helmet could trigger a corresponding alarm. “The prototype proved that human-machine cooperation in detecting faulty information is possible,” said Xu Jianjun, director of the experimental center.

A few problems

A few small problems remain with the ambitious project: As the Scmp writes, the research team had great difficulties in finding sufficient training material. Apparently, this was only provided in a limited and heavily censored manner by the government. The team was also unable to win female test subjects. The human-machine team currently has a hit rate of 80 percent.

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