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YouTube AI misinterprets chess chat with “black” and “white” pieces, racist flags




YouTube AI may have accidentally blocked the chess streamer channel due to racist remarks. (George Becker of Pexels)

It may be unbelievable at first that the YouTube algorithm detected the chess discussion as “racist” and flagged it as punishment. In the case of chess YouTuber, he was blocked by a video streaming company on suspicion of a delicate issue.

YouTube AI may have mistakenly identified chess chat as a “racist”

The incident occurred in June 2020, and I have no idea why the algorithm blocked streamers from creating content videos about chess. At the very least, you may have known that videos are flagged for “harmful” and “dangerous” content. If so, it may have recognized a large amount of hate speech in the discussion forums.

According to the Daily Mail report, Croatian chess enthusiast Antonio Radick, also known as YouTube’s name “Agadometer”, was confused as to why activities within the video sharing platform were banned. Two researchers at Carnegie Mellon University (CMU) made exaggerated speculations about the mystery behind the turmoil.

What was intriguing about this incident was that YouTube didn’t explain why the Radic channel was shut down so quickly. But 24 hours later, he came back as if nothing had happened. To solve a mind-boggling scenario, project scientists said it happened because Radik’s viral interview with GM Hikaru Nakamura detected the words that caused racism.

Ashique Khuda Bukhsh of CMU’s Language Technologies Institute acknowledged in the discussion that there was no clue as to which tool YouTube used to detect racist slurs. However, the video mentioned “black” and “white,” which were believed to be racist terms.

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He added that if the incident hit a popular YouTuber like Radic, what if the AI ​​was doing the same to other people streaming just for fun? Khuda Bukhsh and his fellow research engineer Rupak Sarkar ran two AI software that could detect hate speech and test its feasibility. In addition to testing, they found over 680,000 comments, all from five channels on chess.

In addition, a simple random test was performed on 1,000 sample comments out of about 700,000 comments. We found that 82% of the comments did not mention anything about malicious language. However, they have seen that racist-related words such as white, threat, black, and aggression may have lost the key to AI’s sudden actions.

Rest assured that the AI ​​can have another way to filter messages through a large sample, and the accuracy will vary based on the example.

Comparison of past situations and what happened to radicals

According to a CMU News report, Khuda Bukhsh has previously encountered sample issues. His aim is to recognize “active dogs” and “lazy dogs” in the group of photos. Most of the pictures of “active dogs” contained grass where the dog ran. However, the program may consider a grassy photo as a sample of an “active dog”, and in some cases the dog may not be detected in the photo.

What happened to Radic was just a resemblance to that. The main topic was chess, but the training dataset was very small. As a result, those misclassifications occurred.

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