Speaker 1: Let me tell you something. There's a full out war
Speaker 1: going on on this subject right now. The bunch of
Speaker 1: filthy sonamite perverts and if you don't like to get
Speaker 1: out of here, they're at war with us tonight. This
Speaker 1: is a video from YouTube of Believe It or Not,
Speaker 1: a sermon from Steven Anderson. He's a pastor in temper Arizona.
Speaker 1: He's young, he's bearded, wearing a tie, working up a sweat,
Speaker 1: pretty much screaming at his audience. It's intense. He's pacing
Speaker 1: back and forth in front of a podium. At one
Speaker 1: point he even jumps up on the podium, raising his
Speaker 1: voice even more. He's vigorously wagging his finger. You know what,
Speaker 1: we have hundreds of people, hundreds of people here that
Speaker 1: will not compromise. And if you're not one of them,
Speaker 1: the get out. I don't want to hang around with
Speaker 1: a bunch of fag hags at a bunch of queer
Speaker 1: baits and a bunch of feminints. Get out. Look no
Speaker 1: one in a million years what I thought when I
Speaker 1: was a child. Stephen Anderson uploads his sermons online several
Speaker 1: times a week, sometimes multiple times a day. The one
Speaker 1: we're watching right now has over seventy thousand views. It
Speaker 1: doesn't have any ads running before it or beside it. Well.
Speaker 1: A couple of weeks ago, reporters for The Times of
Speaker 1: London were watching another YouTube video from the same pastor
Speaker 1: and they saw right next to him and add from
Speaker 1: the cosmetics maker Lourel promoting a British charity, obviously not
Speaker 1: the kind of thing Loria would want to be associated with.
Speaker 1: The newspaper found several other ads from household names running
Speaker 1: on videos, like one from an Egyptian cleric who had
Speaker 1: been banned from the UK for terrorism. There was one
Speaker 1: from David Duke, a former Ku Klux Klan leader. The
Speaker 1: pastor Stephen Anderson, his videos appeared next to ads from
Speaker 1: Nissan and the Guardian newspaper. When The Times called these
Speaker 1: advert tiser's, several of them said they were shocked and
Speaker 1: they immediately yanked their ads from YouTube. And over the
Speaker 1: past two weeks that story is exploded into a crisis
Speaker 1: for the world's largest video service and its owner Google.
Speaker 1: I am Akito and I'm mark Berg and this week
Speaker 1: on Decrypted, we're plunging into one of the thorniest issues
Speaker 1: in the modern Internet. How do you police the unwieldy,
Speaker 1: ever expanding mess that's the Worldwide Web. And what can Google,
Speaker 1: a company that believes in letting nearly anything and everything
Speaker 1: live online, do about it? Can they keep their paying
Speaker 1: advertisers away from the Internet's darkest corners. Stay with us
Speaker 1: as we take you on a tour of the latest
Speaker 1: attempts to apply artificial intelligence to solve Google's crisis. At
Speaker 1: stake here is the future of online advertising, which is
Speaker 1: the fuel for the Internet. Google and another rival, Facebook
Speaker 1: have taken more and more of the share of all
Speaker 1: the advertising dollars spent online. So Mark, you're our Google reporter,
Speaker 1: which looks like a pretty fun job. It means you
Speaker 1: get to write about basically everything. Yep. It's a Google's
Speaker 1: parent company, which you know, is called Alphabet. Now. They
Speaker 1: sell phones, they sell laptops, they sell smart speakers, they
Speaker 1: sell smart thermostats. There's two healthcare companies in there, and
Speaker 1: you have the drones and the robots and the self
Speaker 1: driving cars. You know, despite all this cool future SI stuff,
Speaker 1: Alphabet still makes the bulk of its money, actually eighty
Speaker 1: percent last year from selling ads. That amounts to over
Speaker 1: seventy nine billion dollars in two and YouTube. It's a
Speaker 1: really big video site is one of the fastest growing
Speaker 1: parts of that business. Thanks to the avalanche that the
Speaker 1: Times of London set off, the growth of that revenue
Speaker 1: could be in jeopardy. Now, before we get into the
Speaker 1: thick of this controversy, we should take a step back
Speaker 1: and explain how YouTube advertising actually works. It doesn't really
Speaker 1: happen through the traditional sales process and TV where a
Speaker 1: company would say we want our ads to run in
Speaker 1: front of a commercial for Gray's Anatomy. Instead, the brands
Speaker 1: could target really specific slices of YouTube's viewers. They could say,
Speaker 1: we want to target men in their twenties who really
Speaker 1: like Nascar, or we really want to reach teenage girls
Speaker 1: who want makeup advice, and to do that on a
Speaker 1: really big scale. The ad spots are bought and sold
Speaker 1: through google super fast computerized auction system. It all happens automatically.
Speaker 1: To date, advertisers have pretty much accepted this trade off.
Speaker 1: They get all the benefits of running ads with YouTube
Speaker 1: and it's huge audience, and they stept the risk that
Speaker 1: some of their ads could just show up next to
Speaker 1: pretty much anything, which is why when the Times of
Speaker 1: London's story first broke. I call around a few advertisers
Speaker 1: here in the US, and a lot of them said
Speaker 1: the same thing. WI this New A Monday, which was
Speaker 1: a week after all this drama started, we sent our
Speaker 1: reporter Shelley Hagen to an advertising event in New York,
Speaker 1: and despite some of the high profile announcements of advertisers
Speaker 1: pulling their money from YouTube, a lot of the people
Speaker 1: she talked to seemed pretty sanguine about it too. Um.
Speaker 1: I mean, you do a lot of algorithmic betting and curation,
Speaker 1: but you still have you know, ads that could turn up,
Speaker 1: you know, next to offensive content. Uh. I don't think
Speaker 1: it's it's it's always been a problem in u g C.
Speaker 1: Uh media. That's Anna Thomas as a product marketer at
Speaker 1: Adobe and yeah you g C. That stands for user
Speaker 1: generated content, which is what makes up a vast majority
Speaker 1: of the videos on YouTube. There are videos that are
Speaker 1: posted by anyone instead of saying the polished videos that
Speaker 1: we at Bloomberg would publish. It's what makes YouTube unique
Speaker 1: and compelling and often kind of strange. It's great for
Speaker 1: advertisers who wants to reach younger audiences who don't watch
Speaker 1: a lot of traditional TV and At the same time,
Speaker 1: we at Bloomberg have all kinds of strict guidelines about
Speaker 1: what's okay and what's not okay to say. We certainly
Speaker 1: would never use the kind of epithets that we heard
Speaker 1: from the pastor earlier in the show, even if we
Speaker 1: personally believe them, which I must say I do not.
Speaker 1: But users on YouTube can say pretty much whatever they want,
Speaker 1: and this means that content is going to be a
Speaker 1: lot less predictable, and so it's something that ultimately you
Speaker 1: algorithms get smartto and you can actually prevent that from happening.
Speaker 1: But um, yeah, I don't see it as a huge deal. Um.
Speaker 1: And most advertisers are aware of these things. Not everyone
Speaker 1: agrees with Anna. Right after meeting Anna, Shelley visited a
Speaker 1: big name in the advertising world. His name's Rob Norman,
Speaker 1: and he's the chief Digital officer at Group M, which
Speaker 1: is the media buying arm of the ad agency w PP.
Speaker 1: We've had a lot of conversations with Google in the
Speaker 1: last couple of weeks, as you might imagine, from the
Speaker 1: people that look after oppossess on a daily basis, up
Speaker 1: to the most senior commercial levels of Google, and we've
Speaker 1: listened to their ideas. We put some ideas of our
Speaker 1: own on the table, and we I hope that some
Speaker 1: of those are implementable in the very near future. It's
Speaker 1: a big deal when a guy like Rob is this concerned.
Speaker 1: W p PS parent companies spent around five billion dollars
Speaker 1: on Google last year, and they plan to spend more
Speaker 1: this year. For most people, the idea of an head
Speaker 1: sitting next to a jihadi video one incidence of that
Speaker 1: is once too many are always a good number. We
Speaker 1: should know that these problems happened pretty rarely. One agency
Speaker 1: executive told me that of the more than hundreds of
Speaker 1: millions of impressions or ads that they serve online, only
Speaker 1: two landed on what they would call questionable content. And
Speaker 1: as Rob noted to Shelly, this is not a new
Speaker 1: problem for Google. Right so, more than a decade ago,
Speaker 1: and it was just a search company, it introduced a
Speaker 1: system called ad Sense. It was an algorithm that scoured
Speaker 1: texts on the websites to connect them with relevant ads,
Speaker 1: and we still we still talk about this story. But
Speaker 1: one time The New York Post ran a story about
Speaker 1: a gruesome murder where the killer had chopped up the
Speaker 1: body and stuffed it in a garbage bag and right
Speaker 1: next to that story and add placed by Google for
Speaker 1: plastic bags. Google has since put quite a bit of
Speaker 1: effort to curb this problem. It even set up a
Speaker 1: kind of white gloves service called Google Preferred, where advertisers
Speaker 1: pay more to have their ads run on the most
Speaker 1: popular and what Google says is brand safe content. But
Speaker 1: obviously the problem is persisted, and it really reached a
Speaker 1: flashpoint this past week. Coming off Google's advertising crisis goes global.
Speaker 1: Big brands in the US joined the UK and the
Speaker 1: backlash over terrorist videos. So A T and T announced
Speaker 1: it was pulling its ads from YouTube and other Google
Speaker 1: channels that weren't searched. Verison Johnson Johnson quickly followed. Then
Speaker 1: there were a bunch more. You know, we had reporters
Speaker 1: called up in Europe, and there were brands that pulled
Speaker 1: their ads, and then Walmart, Disney, Starbucks did the same
Speaker 1: after reporters show that their ads were running next to
Speaker 1: YouTube videos with pretty blatantly racist messages. Here's Rob Norman
Speaker 1: from group em again. I think there may be a
Speaker 1: group of advertisers that decide that YouTube and other forms
Speaker 1: of user generating content in particular simply to hot to handle.
Speaker 1: I think those that do may withdraw over a longer
Speaker 1: period of time. I think there'll be many others that
Speaker 1: do come back, but they'll come back when they're satisfied
Speaker 1: that the controls are in place that lets them believe
Speaker 1: in the safety of their brands in that environment more
Speaker 1: than they've been moved to believe in the last few days.
Speaker 1: This brings us to the question why now. In part,
Speaker 1: it's our political climate since the US election. Big tech
Speaker 1: companies like Google, Facebook, Twitter, and face a ton of
Speaker 1: criticism over the content they host, especially those articles with
Speaker 1: latent misinformation, you know the fake news problem, which we
Speaker 1: covered on this podcast back in November. And marketers like
Speaker 1: rob are really sensitive about this stuff. It's clearly an
Speaker 1: issue because as soon as the corporate reputation is damaged
Speaker 1: by anything, that's a significant issue for that corporation. It's
Speaker 1: a significant issue also because some of the content would
Speaker 1: be offensive to almost anyone in America. You know, there's
Speaker 1: one more factor. There's this kind of growing angst in
Speaker 1: the advertising world about how much of the market Google
Speaker 1: and Facebook are eating up. The word I always hear
Speaker 1: do a belie more than forty six percent of all
Speaker 1: digital ad sales worldwide last year went to Google and Facebook.
Speaker 1: According to e Marketer, that share is even higher in
Speaker 1: the US. A lot of people have told me that
Speaker 1: the YouTube boycott has a really good chance for advertiser
Speaker 1: of the pile on. After it began, Martin Sorel, the
Speaker 1: head of w PP, who's like Rob Norman's Superboss, send
Speaker 1: a statement blasting Google and Facebook. He said they were
Speaker 1: quote masquerading as technology company. Martin Sorrell said that these
Speaker 1: companies place ads, so they should act more like media
Speaker 1: companies and actually take responsibility for the content that they're hosting,
Speaker 1: even if they actually didn't make that content themselves. So
Speaker 1: I think the time has sort of come where the
Speaker 1: harm has become so salient and so significant that companies
Speaker 1: are going to one way or another, they are going
Speaker 1: to have to start to get control over the really
Speaker 1: bad things that are happening on their networks. So that
Speaker 1: sounds an awful lot like Martin Sorrell, but it's actually
Speaker 1: someone from a very different profession. My name is Honi Freed.
Speaker 1: I am a professor of computer science at Dartmouth College
Speaker 1: and a senior advisor to the Counter Extremism project. Before
Speaker 1: he worked on counter terrorism, Honey worked on a different mission.
Speaker 1: He built algorithms to rid the web of child pornography.
Speaker 1: So he helped create a technology that extracted unique identifiers
Speaker 1: on images, what he called a signature, and that sticks
Speaker 1: with them as they move across the Internet, even if
Speaker 1: they're modified. And what that allows is to do is
Speaker 1: to build up this database of known bad content, in
Speaker 1: that case, it was child pornography, and then we just
Speaker 1: sit at the pipe of a Facebook or Twitter or
Speaker 1: a Google and every image that comes in gets scanned
Speaker 1: and compared against this database of known child pornography, and
Speaker 1: then anything that is a hit against that database gets flagged, removed,
Speaker 1: and reported. Similar software runs online for things like detecting
Speaker 1: spam and viruses, and over the past year, Honey has
Speaker 1: taken that same framework and applied it the detecting child
Speaker 1: pornography and content that promotes terrorism. He's also started to
Speaker 1: move from still images online to audio and video files. Video,
Speaker 1: it turns out, is much more complicated, and while that
Speaker 1: sounds like a simple extension, it is turns out to
Speaker 1: be a very very difficult engineering task because a video
Speaker 1: standard video is twenty four still images for every second,
Speaker 1: so imagine a three minute video. You were talking about
Speaker 1: thousands and thousands of images, and so being able to
Speaker 1: do that efficiently and accurately um is very very challenging
Speaker 1: engineering problem. Just consider the sheer volume of video that
Speaker 1: Google has to deal with. Here's Eric Schmidt, the former
Speaker 1: CEO and now chairman of Alphabet, talking about this on
Speaker 1: Fox Business last week, YouTube, for example, went from about
Speaker 1: a hundred million hours watched per day to one billion
Speaker 1: hours of YouTube watched globally every day. That's an extraordinary
Speaker 1: platform and an extraordinary responsibility. Honey, though, says Google has
Speaker 1: used this as a convenient excuse for not yet solving
Speaker 1: the problem of offensive content. So, first of all, YouTube
Speaker 1: does a pretty good job of taking down content that
Speaker 1: are violations of touch all property and copyrights. That's Google's
Speaker 1: Content i D program. It's a software system they built
Speaker 1: that automatically pulls videos that violate copyright claims. Are actually
Speaker 1: pretty effective at it, and they're effective at it because
Speaker 1: there's a financial interest for them to do it. Um
Speaker 1: They get suit when they don't do it, and now
Speaker 1: there's a financial interest for them to eliminate content. Um
Speaker 1: Um based on the advertising problems that they've been seeing
Speaker 1: over the last few week. Three days after the Times
Speaker 1: of London story came out, Matt Britain, whose Google's top
Speaker 1: business executive in Europe, was speaking in an AS conference.
Speaker 1: He was really contrite. He said the company was terribly
Speaker 1: sorry and that a fix was coming. Later that day,
Speaker 1: Google laid out some of the fixes in a blog post.
Speaker 1: Google promised a slew of new controls for advertisers. Yeah,
Speaker 1: they'd have more levers to avoid quote unquote higher risk
Speaker 1: content videos with religious or political themes. The company promised
Speaker 1: to hire significant numbers of people and developed new AI
Speaker 1: tools to fix the problems of ads next questionable content soon.
Speaker 1: Google promised these sorts of incidents would be resolved in
Speaker 1: less than a few hours. Many people we talked to
Speaker 1: said that if anybody could do this, it would be Google.
Speaker 1: They've certainly got a lot of money and they've been
Speaker 1: working on this technology for a long time. Our editor
Speaker 1: Alistair Barr talked to this guy over the weekend. Um Meat, Ma,
Speaker 1: Dannie and I working machine learning, applying machine learning, developing
Speaker 1: machine learning techniques currently to security at Cisco. Before that,
Speaker 1: Omid worked at Google. I was there three years ago
Speaker 1: up to three years ago. For three years so UM
Speaker 1: and I was at in YouTube research, so we were
Speaker 1: again developing machine learning techniques and the rest of the
Speaker 1: folks were doing machine learning visual analysis audio analysis is
Speaker 1: speech analysis techniques UH and applying it mostly to YouTube videos.
Speaker 1: So this was super advanced research, and O Meat spent
Speaker 1: a lot of that time on, of all things, video
Speaker 1: games like Minecraft. Google's researchers wanted to ensure that the
Speaker 1: videos uploaded to YouTube were what they claimed to be,
Speaker 1: so videos classified as Minecraft would be indeed Minecraft. They
Speaker 1: also didn't work on detecting and tagging images inside YouTube
Speaker 1: videos like look there's a cat or that's a blue sky.
Speaker 1: There are different ways of analysis. The video gets dissected
Speaker 1: or like a surgery. Basically, all these channels get dissected.
Speaker 1: So why would Google invest so much of its research
Speaker 1: horsepower into this. Better we can tag automatically, the better
Speaker 1: ads we can put recommendation, the relevance can get improved.
Speaker 1: Related videos list on the right side and song. In twelve,
Speaker 1: O Meat and his colleagues published the research paper showing
Speaker 1: they had a ninety nine accuracy rate in classifying video
Speaker 1: content on YouTube. Basically, for the work we had done.
Speaker 1: Not just this that our team, the YouTube the research
Speaker 1: team was awarded. For example, we could take vacation together.
Speaker 1: They went to Hawaii and Google didn't want to comment
Speaker 1: for this episode, but Mark, you got your hands on
Speaker 1: an email the company sent out to some of its
Speaker 1: advertising agencies last week after the company's initial blog post
Speaker 1: failed to stem the YouTube boycott YEP. In that note,
Speaker 1: Google gave a few more details about the new capabilities
Speaker 1: its policies could bring. Here's one example. Under its old policies,
Speaker 1: Google would have allowed advertising if someone in a YouTube
Speaker 1: video was wearing a T shirt with let's say, offensive
Speaker 1: language or an offensive slogan. Under the new policies, it
Speaker 1: would disabled ads. Some of the experts we talked to,
Speaker 1: so this is not an easy fix to implement. And
Speaker 1: here's honey Free, the Dartmouth professor. The tools that we
Speaker 1: have developed what are generally called robust hashing. UM don't
Speaker 1: go after content like oh, I've seen this flag or
Speaker 1: this logo or the symbol, and somewhere in the image
Speaker 1: it goes after this is the same image that I've
Speaker 1: seen before. This is the same video, the same audio recording.
Speaker 1: So it's very very specific. So the good news about
Speaker 1: that is that we can find that content extremely efficiently
Speaker 1: and very accurately. And when I say accurately, I mean
Speaker 1: we can work at Internet scale with billions of uploads
Speaker 1: today and make very very few mistakes. And that's really
Speaker 1: good because that means you can fully automate the system.
Speaker 1: So this works for things that have already been flagged
Speaker 1: as inappropriate content, like let's say, a particular clip of
Speaker 1: child pornography gets taken down, but gets re uploaded to
Speaker 1: the Internet a couple of days later. What gets tricky
Speaker 1: is when a computer sees something entirely new, something that
Speaker 1: hasn't been tagged yet as appropriate or inappropriate. And while
Speaker 1: there is technology to do that, that technology is not
Speaker 1: good enough to work fully automatically, extremely efficiently with no
Speaker 1: human intervention. And what I mean by that is that
Speaker 1: it will inevitably make mistakes, and it will make two
Speaker 1: cut types of mistakes. It will allow things to go
Speaker 1: through that you don't want to UM maybe we tolerate that,
Speaker 1: but more troublingly, it will filter out things that you
Speaker 1: don't want it to filter out. So without human intervention,
Speaker 1: that technology today UM does not work at scale, at
Speaker 1: Internet scale. So Google is one of many companies working
Speaker 1: on an AI approach called deep learning, which is more
Speaker 1: or less a system where algorithms sort of teach themselves.
Speaker 1: So Google's researchers once fed a ton of videos of
Speaker 1: cats to a computer without telling the computer what a
Speaker 1: cat was. Over time, the computers basically learned to identify look,
Speaker 1: there's a cut, and they could in theory, do the
Speaker 1: same thing with a Nazi flag or another hate symbol.
Speaker 1: Our editor Alistair also spoke to another researcher who has
Speaker 1: worked on these problems extensively. I'm j Bolo. I'm a
Speaker 1: professor of computer science at the University of Rochester. J
Speaker 1: Bo worked on developing AI systems that could detect hate
Speaker 1: mes sages on social media like Facebook and Twitter. He
Speaker 1: also uses AI tools for other ends. So we have
Speaker 1: used social media too, for example, analyze the progress of
Speaker 1: the presidential election, and many of our findings helped to
Speaker 1: explain why Trump won election. And we'll also use social
Speaker 1: media to track under age drinking problem, which is a
Speaker 1: big problem in the United States. We also use Instagram
Speaker 1: to track drug use and drug dealers, and this was
Speaker 1: actually in collaboration with the New York State Attorney General's office.
Speaker 1: His research also used advanced AI to come through Instagram
Speaker 1: looking for things like weed and pills and even bonds.
Speaker 1: Pretty amazing, yeah, and a little creepy, But Jaba said
Speaker 1: that video presents a far greater obstacle for AI researchers.
Speaker 1: A video is not just a bunch of uh individual
Speaker 1: frames uh when you watch a video. The most important
Speaker 1: aspect or unique aspect of video is the motion expect
Speaker 1: So things move, people move. That requires UM motion based
Speaker 1: analytical methods too. For example, understand that the changing expression
Speaker 1: on the person's face or an action performed by a person, oh,
Speaker 1: you know, something like an explosion, Oh, you know, people
Speaker 1: fighting each other. It's a challenge. So it's a big
Speaker 1: challenge for Google because it has YouTube, but Facebook and
Speaker 1: Twitter and other companies are also pouring a lot of resources,
Speaker 1: and the online video on Facebook and Twitter also doing
Speaker 1: live video, which has its own set of challenge. Right,
Speaker 1: So that's one challenge figuring out what's inside a video.
Speaker 1: But still Google's algorithms are getting pretty good at that.
Speaker 1: Here's an even thorniar problem. How do you make judgments
Speaker 1: about the gray areas whether a video is outright offensive
Speaker 1: or hate speech? Or incendiary. Google has typically tried to
Speaker 1: steer clear of that. O Meats at his research team
Speaker 1: at YouTube didn't spend a lot of time trying to
Speaker 1: classify political videos because that puts Google into it a
Speaker 1: comfortable position right making editorial judgment. Google thinks of YouTube
Speaker 1: like online search. It's just search for video. If it
Speaker 1: curious that it can't be the subjective, neutral technology platform
Speaker 1: where all information is free. A few advertisers, if any,
Speaker 1: would want to run ads in front of that kind
Speaker 1: of video that we watched earlier, with a screaming pastor
Speaker 1: putting down gay people. Right. But but some online news
Speaker 1: services like maybe Vice News or even like CBS might
Speaker 1: want to run a new segment on that that pastor,
Speaker 1: and advertisers probably want to run ads on that. So
Speaker 1: remember the note we told you that Google sent out
Speaker 1: to advertisers. In that Google said that there are forty
Speaker 1: eight videos flagged as offensive by The Times and other papers.
Speaker 1: Forty four of them would immediately disabled ads under the
Speaker 1: new policies, but the other four could still show ads.
Speaker 1: Google had decided that these videos didn't violate their terms
Speaker 1: of service. Still, with the new controls, advertisers would have
Speaker 1: clearer options for opting out of videos like this. Here's
Speaker 1: one of those four. It starts out with a prayer
Speaker 1: and Arabic asking God to have mercy upon the soul
Speaker 1: of martyrs, and then it transitions into a song. The
Speaker 1: language is pretty graphic. You'll hear phrases like will wage
Speaker 1: wars against them, will return the rape truth, we won't
Speaker 1: accept any occupied land, the ground will erupt and burn them,
Speaker 1: will liberate elk Uods and all goods for our listeners
Speaker 1: who don't know. Is the Arabic name for the city
Speaker 1: of Jerusalem. Okay, so it's clear that this is about
Speaker 1: the Israeli Palestine conflict. Um. You know, it's pretty graphic language,
Speaker 1: like we said, with a lot of metaphors violence. When
Speaker 1: we loaded this video as of taping this episode on Wednesday,
Speaker 1: we saw an ad roll in for Samsung's new Galaxy phone.
Speaker 1: Probably not something Samsung might have picked on its own,
Speaker 1: but the video is popular right now, it has over
Speaker 1: more than half a million views. With its new policies.
Speaker 1: Once they're implemented, Google set adverts wise, there's like Samsung
Speaker 1: will be able to opt out from this sort of video.
Speaker 1: They could check off a box that says no ads
Speaker 1: on political or religious content. But it still makes a
Speaker 1: question why doesn't Google just disabled ads from videos like
Speaker 1: these altogether? So I check with Google on this, and
Speaker 1: they have a policy where they don't comment on individual videos.
Speaker 1: But here's a hunch. The words in the video are
Speaker 1: pretty intense, but Google may have decided that it didn't
Speaker 1: cross over the line into hate speech or a specific
Speaker 1: attack on a group of people. You know, it could
Speaker 1: be interpreted as a religious song, and Google just doesn't
Speaker 1: really want to filter religious songs from YouTube ads. It
Speaker 1: goes to show how difficult it is for software alone
Speaker 1: to be making all these decisions. You really do need
Speaker 1: a human to be making the call on some of
Speaker 1: these trickier videos. Here's what Honey, the Dartmouth professor told me.
Speaker 1: So it's really a question of how much you want
Speaker 1: to automate these things, the fully automatic things. We are
Speaker 1: not there where we can just say, hey, that fire
Speaker 1: all the extremest content. It's machine learning. Artificial intelligence is
Speaker 1: nowhere even near that. Don't believe the hype. So I
Speaker 1: showed Honey the video with the prayer and the song
Speaker 1: after we had our initial conversation, and he wrote back
Speaker 1: and said to him the language sounded like an explicit
Speaker 1: called to violence, but he said that his work focuses
Speaker 1: on removing content that violates the terms of service of
Speaker 1: companies like Google and Facebook, and it's up to Google
Speaker 1: to set their own terms of service, and so an
Speaker 1: example of how humans often disagree about what's offensive. Oh mean? Madonni,
Speaker 1: the AI researcher who used to work at YouTube and
Speaker 1: now works at Cisco, He agreed that humans play a
Speaker 1: really important role here too. He said that letting people
Speaker 1: look at the end result of what a computer filters
Speaker 1: would help reduce errors. He did have a caveat though,
Speaker 1: obviously humans don't scale. We need maybe an army of
Speaker 1: editors to look at things that eventually to decide what
Speaker 1: it said appropriate MEDEO or not. As an aside, humans
Speaker 1: don't scale might be the th googly comment I've ever heard.
Speaker 1: Facebook pointed to a similar defense for its inability to
Speaker 1: curb fake news, but after that controversy in the fall,
Speaker 1: Facebook decided to team up with third party fact checking
Speaker 1: websites which employ human editors, and when those fact checkers
Speaker 1: say that a particular piece of content could be dubious,
Speaker 1: Facebook flags those stories as potentially incorrect. Their technology we
Speaker 1: have today, for all its advances, still is good enough
Speaker 1: to offer a perfect solution. And Google, which is typically
Speaker 1: not shy to make lofty promises, didn't want to commit
Speaker 1: to a total fix. And it's no Google said it's
Speaker 1: new fixes would go a long way and ensuring brands
Speaker 1: like Lorel that it would never get a call like
Speaker 1: it did from the Times other week. But Google said
Speaker 1: that could never be a hundred percent guaranteed, right. And
Speaker 1: Professor lu O, the AI researcher we talked to earlier,
Speaker 1: he agreed, whether it's human calligence or occupy tween helligence,
Speaker 1: we are never going to achieve one percent recognition read anything.
Speaker 1: So for someone to come out to say we'll get
Speaker 1: and here, we will get rid of all the all
Speaker 1: the Hadden messages, okay, the videos. That's the responsible and
Speaker 1: that's it for this week's Decrypted. Thanks for listening. Tell
Speaker 1: us what you thought of the show. You can write
Speaker 1: to us at Decrypted at Bloomberg dot net. You can
Speaker 1: find me on Twitter, free a Pete speech, I'm an
Speaker 1: image Bergan and I'm at Ako seven. Don't forget to
Speaker 1: subscribe to us on iTunes or wherever you get your
Speaker 1: podcasts and leave us a reading and a review. I
Speaker 1: read each and every one of these reviews, and they
Speaker 1: help us make our show better, and it helps put
Speaker 1: our show in front of more listeners. This episode was
Speaker 1: produced by Liz Smith and Magnus Hendrickson. A very very
Speaker 1: special thanks to our porter Shelley Hagan and our great
Speaker 1: editor Alistair Barr for their reporting and research for this show.
Speaker 1: Alec McCabe is head of Podcasts. We'll see you next week.
Speaker 1: H