Incident 144: YouTube's AI Mistakenly Banned Chess Channel over Chess Language Misinterpretation
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Computer scientists at Carnegie Mellon University began investigating the AI glitch after a popular chess channel on YouTube was blocked for “harmful and dangerous” content last June.
Croatian chess player Antonio Radic, who goes by the online alias Agadmator, hosts the world’s most popular YouTube chess channel, with more than 1 million subscribers.
On 28 June, 2020, Radic was blocked from YouTube while presenting a chess show with Grandmaster Hikaru Nakamura, though no specific reason was given by the Google-owned video platform.
Radic’s channel was reinstated after 24 hours, leading the chess champion to speculate that he had been temporarily banned for a referral to “black against white”, even though he was talking about chess at the time.
YouTube’s moderation system relies on both humans and AI algorithms, meaning any AI system could misinterpret the comments if not trained correctly to understand context.
“If they rely on artificial intelligence to detect racist language, this kind of accident can happen,” said Ashiqur KhudaBukhsh, a project scientist at CMU’s Language Technologies Institute.
KhudaBukhsh tested this theory by using a state-of-the-art speech classifier to screen more than 680,000 comments gathered from five popular chess-focussed YouTube channels.
After manually reviewing a selection of 1,000 comments that had been classed by the AI as hate speech, they found that 82 per cent of them had been misclassified due to the use of words like “black”, “white”, “attack” and “threat” – all of which commonly used in chess parlance.
The paper was presented this month at the Association for the Advancement of AI annual conference.
YouTube's overeager AI might have misinterpreted a conversation about chess as racist language.
Last summer, a YouTuber who produces popular chess videos saw his channel blocked for including what the site called 'harmful and dangerous' content.
YouTube didn't explain why it had blocked Croatian chess player Antonio Radic, also known as 'Agadmator,' but service was restored 24 hours later.
Computer scientists at Carnegie Mellon suspect Radic's discussion of 'black vs. white' with a grandmaster accidentally triggered YouTube's AI filters.
Running simulations with software trained to detect hate speech, they found more than 80 percent of chess videos flagged for hate speech lacked any—but did include terms like 'black,' 'white,' 'attack' and 'threat.'
The researchers suggest social-media platforms incorporate chess language into their algorithms to prevent further confusion.
Popular chess YouTuber Antonio Radic had his channel blocked last summer for 'harmful and dangerous' content. He believes the platform's AI mistakenly flagged him for discussing 'black versus white' in a chess conversation
With more than a million subscribers, Agadmator is considered the most popular chess vertical on YouTube.
But on June 28, Radic's channel was blocked after he posted a segment with Grandmaster Hikaru Nakamura, a five-time champion and the youngest American to earn the title of Grandmaster.
YouTube didn't provide him with a reason for blocking the channel.
In addition to human moderators, YouTube uses AI algorithms to ferret out prohibited content—but if they're not fed the right examples to provide context, those algorithms can flag benign videos.
Researchers at Carnegie Mellon tested two top speech classifiers, AI software that can be trained to detect hate speech. More than 80 percent of the comments the programs flagged lacked any racist language, but they did include chess terms like 'black,' 'white,' 'attack' and 'threat'
Radic's channel was reinstated after 24 hours, leading him to speculate his use of the phrase 'black against white' in the Nakamura was the culprit.
At the time, he was talking about the two opposing sides in a chess game.
Ashiqur R. KhudaBukhsh, a computer scientist at Carnegie Melon's Language Technologies Institute, suspected Radic was right.
'We don't know what tools YouTube uses, but if they rely on artificial intelligence to detect racist language, this kind of accident can happen,' KhudaBukhsh said.
To test his theory, KhudaBukhsh and fellow researcher Rupak Sarkar ran tests on two cutting-edge speech classifiers, AI software that can be trained to detect hate speech.
Radic's channel was blocked for 24 hours after he posted this video, featuring a conversation with Grandmaster Hikaru Nakamura
Using the software on over 680,000 comments taken from five popular YouTube chess channels, they found 82 percent of the comments flagged in a sample set didn't include any obvious racist language or hate speech.
Words such as 'black,' 'white,' 'attack' and 'threat' seemed to have set off the filters, KhudaBukhsh and Sarkar said in a presentation this month at the annual Association for the Advancement of AI conference.
The software's accuracy depends on the examples its given, KhudaBukhsh said, and the training data sets for YouTube's classifiers 'likely include few examples of chess talk, leading to misclassification.'
Radić, 33, started his YouTube channel in 2017 and has more than a millions subscribers. His most popular video, a review of a 1962 match, has garnered more than 5.5 million views
If someone as well-known as Radic is being erroneously blocked, he added, 'it may well be happening quietly to lots of other people who are not so well known.'
YouTube declined to indicate what caused Radic's video to be flagged, but told Mail Online, 'When it’s brought to our attention that a video has been removed mistakenly, we act quickly to reinstate it.'
'We also offer uploaders the ability to appeal removals and will re-review the content,' a representative said. 'Agadmator appealed the removal, and we quickly reinstated the video.'
Radić, 33, started his YouTube channel in 2017 and, within a year, its revenue exceeded his day job as a wedding videographer.
'I always loved chess but I live in a small town and there weren't too many people I could talk to about [it],' he told ESPN last year. 'So, starting a YouTube channel kind of made sense.'
His most popular video, a review of a 1962 match between Rashid Nezhmetdinov and Oleg Chernikov, has garnered more than 5.5 million views to date.
COVID lockdowns have sparked a renewed interest in chess: Since March 2020, the server and social network Chess.com has added roughly 2 million new members a month since the pandemic began, Annenberg Media reported.
The game of kings has also benefited from the popularity of 'The Queen's Gambit,' an acclaimed mini-series about a troubled female chess master that dropped on Netflix in October.
"The Queen's Gambit," the recent TV mini-series about a chess master, may have stirred increased interest in chess, but a word to the wise: social media talk about game-piece colors could lead to misunderstandings, at least for hate-speech detection software.That's what a pair of Carnegie Mellon University researchers suspect happened to Antonio Radić, or "agadmator," a Croatian chess player who hosts a popular YouTube channel. Last June, his account was blocked for "harmful and dangerous" content.
YouTube never provided an explanation and reinstated the channel within 24 hours, said Ashique R. KhudaBukhsh a project scientist in CMU's Language Technologies Institute (LTI). It's nevertheless possible that "black vs. white" talk during Radić's interview with Grandmaster Hikaru Nakamura triggered software that automatically detects racist language, he suggested.
"We don't know what tools YouTube uses, but if they rely on artificial intelligence to detect racist language, this kind of accident can happen," KhudaBukhsh said. And if it happened publicly to someone as high-profile as Radić, it may well be happening quietly to lots of other people who are not so well known.
To see if this was feasible, KhudaBukhsh and Rupak Sarkar, an LTI course research engineer, tested two state-of-the-art speech classifiers — a type of AI software that can be trained to detect indications of hate speech. They used the classifiers to screen more than 680,000 comments gathered from five popular chess-focused YouTube channels.They then randomly sampled 1,000 comments that at least one of the classifiers had flagged as hate speech. When they manually reviewed those comments, they found that the vast majority — 82% — did not include hate speech. Words such as black, white, attack and threat seemed to be triggers, they said.As with other AI programs that depend on machine learning, these classifiers are trained with large numbers of examples and their accuracy can vary depending on the set of examples used.For instance, KhudaBukhsh recalled an exercise he encountered as a student, in which the goal was to identify "lazy dogs" and "active dogs" in a set of photos. Many of the training photos of active dogs showed broad expanses of grass because running dogs often were in the distance. As a result, the program sometimes identified photos containing large amounts of grass as examples of active dogs, even if the photos didn't include any dogs.In the case of chess, many of the training data sets likely include few examples of chess talk, leading to misclassification, he noted.
The research paper by KhudaBukhsh and Sarkar, a recent graduate of Kalyani Government Engineering College in India, won the Best Student Abstract Three-Minute Presentation this month at the Association for the Advancement of AI annual conference.
It might be unbelievable at first that a YouTube algorithm has detected a chess discussion as 'racist' and flagged it for punishment. In the case of the chess YouTuber, he was blocked by the video-streaming company for the alleged, sensitive issue.
YouTube AI Could Have Mistakenly Perceived The Chess Chat As 'Racist'
The incident happened in June 2020 and people have entirely no idea why the algorithm blocked the streamer from making his content videos about chess. At the very least, it could have known that the video was flagged as 'harmful' and 'dangerous' content. Maybe, it recognized an amount of hate speech in the discussion forum if that was the case.
In a report by Daily Mail, the Croatian chess enthusiast, Antonio Radic, who is also known by his YouTube name "agadmator" was puzzled why he was barred from any activity within the video-sharing platform. Two researchers from the Carnegie Mellon University (CMU) had a wild guess about the mystery behind the confusion.
What's intriguing regarding the case was YouTube did not even explain why Radic's channel was shut down immediately. However, after 24 hours, it returned as if nothing happened. To solve the mind-boggling scenario, a project scientist said that it happened because Radic's viral interview with GM Hikaru Nakamura had detected words that sparked racism.
Ashique KhudaBukhsh of the Language Technologies Institute in CMU admitted that they have no clue what tool did YouTube use to detect a racist slur in the discussion. The video, however, mentioned "black" and "white" which were believed to be racist language.
Furthermore, he added that if the incident struck popular YouTubers like Radic, what more if the AI has been doing the same thing to other people who just stream for fun. KhudaBukhsh and his companion Rupak Sarkar, a research engineer ran two AI software that could detect hate speech to test its feasibility. Along with the test, they have discovered over 680,000 comments which came from five channels that were all about chess.
Moreover, out of nearly 700,000 comments, a simple random test was carried out for 1,000 sample comments. They found out that 82% of the comments did not tackle anything about hate speech. But, they have seen that words related to racism like white, threat, black, and attack could be the missing key to the AI's sudden action.
The AI could have a different way of filtering the messages through large samples and rest assured that the accuracy also varies based on the examples.
Comparing The Past Situation To What Happened To Radic
In a report by CMU News, KhudaBukhsh experienced encountering the sample problem before. His objective will be to recognize the 'active dogs' and the 'lazy dogs' in a group of photos. Majority of the pictures which have 'active dogs' contained grasses where the dogs ran. However, the program sometimes considered those photos that have grasses as samples of an 'active dog,' even in some cases, no dogs were detected in the photos.
What happened to Radic was just a mere resemblance of it. The training data sets were only a few even though the main topic was chess. As a result, wrong classifications of them happened.
The world’s most popular YouTube chess channel was blocked after artificial algorithms set up to detect racist content and hate speech mistook discussion about black and white chess pieces as racism, reports Independent UK.
On June 28, 2020, Croatian chess player Antonio Radic's YouTube chess channel, with more than 1 million subscribers was blocked during a chess show with Grandmaster Hikaru Nakamura.
He received no explanation from the video platform.
Radic’s channel was restored 24 hours later. He suspects that the account may have been blocked because he referred to the chess game as “Black against White”.
YouTube relies on both humans and AI algorithms, which means the AI system could make an error if it is not trained correctly to interpret context.
“If they rely on artificial intelligence to detect racist language, this kind of accident can happen,” said Ashiqur KhudaBukhsh, a project scientist at CMU’s Language Technologies Institute.
KhudaBukhs tested this theory by using the best speech classifier that’s available to screen 680,000 comments gathered from five popular chess-focused YouTube channels.
After manually reviewing 1,000 comments, he found that 82 per cent of them had been wrongly categorized by AI as hate speech because the comments used words like “black”, “white”, “attack” and “threat”.
YouTube, Facebook, and Twitter warned last year that videos and content may be erroneously removed for policy violations, as the companies rely on automated takedown software during the coronavirus pandemic.
In a blog post, Google said that to reduce the need for people to come into offices, YouTube and other business divisions are temporarily relying more on artificial intelligence and automated tools to find problematic content.
Last June, Antonio Radić, the host of a YouTube chess channel with more than a million subscribers, was live-streaming an interview with the grandmaster Hikaru Nakamura when the broadcast suddenly cut out.
Instead of a lively discussion about chess openings, famous games, and iconic players, viewers were told Radić’s video had been removed for “harmful and dangerous” content. Radić saw a message stating that the video, which included nothing more scandalous than a discussion of the King’s Indian Defense, had violated YouTube’s community guidelines. It remained offline for 24 hours.
Exactly what happened still isn’t clear. YouTube declined to comment beyond saying that removing Radić’s video was a mistake. But a new study suggests it reflects shortcomings in artificial intelligence programs designed to automatically detect hate speech, abuse, and misinformation online.
Ashique KhudaBukhsh, a project scientist who specializes in AI at Carnegie Mellon University and a serious chess player himself, wondered if YouTube’s algorithm may have been confused by discussions involving black and white pieces, attacks, and defenses.
So he and Rupak Sarkar, an engineer at CMU, designed an experiment. They trained two versions of a language model called BERT, one using messages from the racist far-right website Stormfront and the other using data from Twitter. They then tested the algorithms on the text and comments from 8,818 chess videos and found them to be far from perfect. The algorithms flagged around 1 percent of transcripts or comments as hate speech. But more than 80 percent of those flagged were false positives—read in context, the language was not racist. “Without a human in the loop,” the pair say in their paper, “relying on off-the-shelf classifiers’ predictions on chess discussions can be misleading.”
The experiment exposed a core problem for AI language programs. Detecting hate speech or abuse is about more than just catching foul words and phrases. The same words can have vastly different meaning in different contexts, so an algorithm must infer meaning from a string of words.
“Fundamentally, language is still a very subtle thing,” says Tom Mitchell, a CMU professor who has previously worked with KhudaBukhsh. “These kinds of trained classifiers are not soon going to be 100 percent accurate.”
Yejin Choi, an associate professor at the University of Washington who specializes in AI and language, says she is “not at all” surprised by the YouTube takedown, given the limits of language understanding today. Choi says additional progress in detecting hate speech will require big investments and new approaches. She says that algorithms work better when they analyze more than just a piece of text in isolation, incorporating, for example, a user’s history of comments or the nature of the channel in which the comments are being posted.
But Choi’s research also shows how hate-speech detection can perpetuate biases. In a 2019 study, she and others found that human annotators were more likely to label Twitter posts by users who self-identify as African American as abusive and that algorithms trained to identify abuse using those annotations will repeat those biases.
Companies have spent many millions collecting and annotating training data for self-driving cars, but Choi says the same effort has not been put into annotating language. So far, no one has collected and annotated a high-quality data set of hate speech or abuse that includes lots of “edge cases” with ambiguous language. “If we made that level of investment on data collection—or even a small fraction of it—I’m sure AI can do much better,” she says.
Mitchell, the CMU professor, says YouTube and other platforms likely have more sophisticated AI algorithms than the one KhudaBukhsh built; but even those are still limited.
Big tech companies are counting on AI to address hate speech online. In 2018, Mark Zuckerberg told Congress that AI would help stamp out hate speech. Earlier this month, Facebook said its AI algorithms detected 97 percent of the hate speech the company removed in the last three months of 2020, up from 24 percent in 2017. But it does not disclose the volume of hate speech the algorithms miss, or how often AI gets it wrong.
WIRED fed some of the comments gathered by the CMU researchers into two hate-speech classifiers—one from Jigsaw, an Alphabet subsidiary focused on tackling misinformation and toxic content, and another from Facebook. Some statements, such as “At 1:43, if white king simply moves to G1, it's the end of black's attack and white is only down a knight, right?” were judged 90 percent likely not hate speech. But the statement “White’s attack on black is brutal. White is stomping all over black’s defenses. The black king is gonna fall … ” was judged more than 60 percent likely to be hate speech.
It remains unclear how often content may be mistakenly flagged as hate speech on YouTube and other platforms. “We don’t know how often it happens,” KhudaBukhsh says. “If a YouTuber isn’t that famous, we will not see it.”
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