Moderating a large network like YouTube is no easy task. Aside from the sheer volume of content uploaded every day, moderators are subjected to the worst of humanity and often end up requiring therapy. They’re the unsung heroes.

AI has been hailed as helping to deal with some of the aforementioned issues. Either by automating the moderation process entirely or by offering a helping hand to humans.

Google was left with little choice but to give more power to its AI moderators as the COVID-19 pandemic took hold… but it hasn’t been smooth sailing.

In late August, YouTube said that it had removed 11.4 million videos over the three months prior–the most since the site launched in 2005.

That figure alone should raise a few eyebrows. If a team of humans were removing that many videos, they probably deserve quite the pay rise.

Of course, most of the video removals weren’t done by humans. Many of the videos didn’t even violate the guidelines.

Neal Mohan, chief product officer at YouTube, told the Financial Times:

“One of the decisions we made [at the beginning of the COVID-19 pandemic] when it came to machines who couldn’t be as precise as humans, we were going to err on the side of making sure that our users were protected, even though that might have resulted in [a] slightly higher number of videos coming down.”

Some of the removals left content creators bewildered, angry, and out of pocket in some cases.

Around 320,000 of videos taken down were appealed, and half of the appealed videos were reinstated.

Deciding what content to ultimately remove feels like one of the many tasks which needs human involvement. Humans are much better at detecting nuances and things like sarcasm.

However, the sheer scale of content needing to be moderated also requires an AI to help automate some of that process.

“Over 50 percent of those 11 million videos were removed without a single view by an actual YouTube user and over 80 percent were removed with less than 10 views,” Mohan said. “That’s the power of machines.”

AIs can also help to protect humans from the worst of the content. Content detection systems are being built to automatically blur things like child abuse enough so that human moderators know what it is to remove it—but to limit their psychological impact.

Some believe AIs are better in helping to determine what content should be removed simply using logic rather than a human’s natural biases like their political-leaning, but we know human biases seep into algorithms.

In May, YouTube admitted to deleting messages critical of the Chinese Communist Party (CCP). YouTube later blamed an “error with our enforcement systems” for the mistakes. Senator Josh Hawley even wrote (PDF) to Google CEO Sundar Pichai seeking answers to “troubling reports that your company has resumed its long pattern of censorship at the behest of the Chinese Communist Party.”

Google appears to have quickly realised that replacing humans entirely with AI is rarely a good idea. The company says many of the human moderators who were “put offline” during the pandemic are now coming back.

(Photo by Rachit Tank on Unsplash)

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We’ve all scratched our heads at some recommendations when using online platforms. Just yesterday, Verge reporter Megan Farokhmanesh shared how her Instagram recommendations have been plagued by some rather bizarre CGI images of teeth.

Farokhmanesh’s account is of a recommendation algorithm going rogue in a relatively harmless and amusing way, but that’s not always the case.

Algorithms need to be unbiased. It’s easy to imagine how, without due scrutiny, algorithms could recommend content which influences a person to think or vote a certain way. The bias may not even be intentional, but it doesn’t make it any less dangerous.

YouTube’s algorithms, in particular, have been called out for promoting some awful content – including paedophilia and radicalisation. To really put that danger in perspective, around 70 percent of YouTube’s viewing time comes from recommendations.

Mozilla’s newly-launched site features 28 horror stories caused by YouTube’s algorithms. The site was launched following a Mozilla-led social media campaign where users shared their stories using the #YouTubeRegrets hashtag.

In one story, an individual provided an account of their preschool son who – like many his age – liked watching Thomas the Tank Engine videos. YouTube’s recommendations led him into watching graphic compilations of train wrecks.

At that early stage in a person’s life, what they see can be detrimental to their long-term development. However, that doesn’t mean adults aren’t also affected.

Another person said: “I started by watching a boxing match, then street boxing matches, and then I saw videos of street fights, then accidents and urban violence… I ended up with a horrible vision of the world and feeling bad, without really wanting to.”

Yet another person said they’d often watch a drag queen who did a lot of positive-affirmation and confidence-building videos. YouTube’s recommendations allegedly served up a ton of anti-LGBT content for ages after, which could have a devastating impact on already too-often marginalised communities.

In September, Mozilla advised YouTube it could improve its service – and trust in it – by taking three key steps:

• Provide independent researchers with access to meaningful data.
• Build simulation tools for researchers.
• Empower researchers by not implementing restrictive API rate limits and provide access to a historical archive of videos.

We’ll have to wait and see whether YouTube takes on Mozilla’s advice, but we hope changes are made sooner rather than later. Around 250 million hours of YouTube content per day is watched via recommendations, and we can never be certain for how long those videos will last in the minds of those viewing them.

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