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YouTube has played a significant role in radicalizing people into conspiracy theories that promote white supremacy, anti-vaxxing, denial of mass shootings, climate change denial, and distrust of mainstream media, by aggressively recommending (and autoplaying) videos on these topics to people who weren’t even looking for them.

However, when the New York Times interviewed YouTube’s most senior product executive, Neal Mohan, he made a series of statements that, in my opinion, were highly misleading, perpetuated misconceptions, denied responsibility, and minimized an issue that has destroyed lives.

Unfortunately, these recommendations are disproportionately for conspiracy theories promoting white supremacy, anti-vaxxing, denial of mass shootings, climate change denial, and denying the accuracy of mainstream media sources.

Guillaume Chaslot, who has a PhD in artificial intelligence and previously worked at Google on YouTube’s recommendation system, wrote software which does a YouTube search with a “seed” phrase (such as “Donald Trump”, “Michelle Obama”, or “is the earth round or flat?”), and records what video is “Up Next” as the top recommendation, and then follows what video is “Up Next” next after that, and so on.

When Guardian reporters analyzed the videos, they found that they were 6 times as likely to be anti-Hillary Clinton (regardless of whether the user had searched for “Trump” or “Clinton”), and that many contained wild conspiracy theories: “There were dozens of clips stating Clinton had had a mental breakdown, reporting she had syphilis or Parkinson’s disease, accusing her of having secret sexual relationships, including with Yoko Ono.

Google said it ‘strongly disagreed’ with the research—but after Senator Mark Warner raised concerns about YouTube promoting what he called ‘outrageous, salacious, and often fraudulent content,’ Google thanked The Guardian for doing the story.” (emphasis mine) Why would Google claim that they had evidence refuting Chaslot’s research, and then never publish it?

YouTube’s Chief Product Officer, Neal Mohan, was interviewed in the New York Times, where he seemed to deny a well-documented phenomenon, ignored that 70% of time spent on the site comes from autoplaying recommendations (instead blaming users for what videos they choose to click on), made a nonsensical “both sides” argument (even though YouTube has extremist videos, they also have non-extremist videos…?), and perpetuated misconceptions (suggesting that since extremism isn’t an explicit input to the algorithm, that the results can’t be biased towards extremism).

“It is not the case that ‘extreme’ content drives a higher version of engagement or watch time than content of other types.” –Neal Mohan Unfortunately, any recommendation system trying only to maximize time spent on its own platform will incentivize content that tells you the rest of the media is lying.

All I’m saying is that it’s not inevitable.” –Neal Mohan This statement ignores the way that YouTube’s autoplay works in conjunction with recommendations, which drives 70% of time that users spend on the site, according to a previous talk Neal Mohan gave.

This has resulted in a years long harassment campaign against these grieving parents– many of them have had to move multiple times to try to evade harassment and one father recently committed suicide.

they are always ranking one video, pin, or group against another when they’re deciding what to show you.” Autoplaying conspiracy theories boosts YouTube’s revenue– as people are radicalized, they stop spending time on mainstream media outlets and spend more and more time on YouTube.

He continued, “And again, our systems are not doing this, because that’s not a signal that feeds into the recommendations.” Mohan is suggesting that since extremism is not an explicit variable that is fed into the algorithm, the algorithm can’t be biased towards extremist material.

For example, the COMPAS recidivism algorithm, used in many USA courtrooms as part of bail, sentencing, or parole decisions, was found to have nearly twice as high a false positive rate for Black defendents compared to white defendents.

Not only does ignoring factors like race, gender, or extremism not protect you from biased results, many machine learning experts recommend the opposite: you need to be measuring these quantities to ensure that you are not unjustly biased.

Like many people around the world, I’m alarmed by the resurgence in white supremacist movements and continued denialism of climate change, and it sickens me to think how much money YouTube has earned by aggressively promoting such conspiracy theories to people who weren’t even looking for them.

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