The “Drunk Pelosi” video shows that cheapfakes can be as damaging as deepfakes.
▻https://slate.com/technology/2019/06/drunk-pelosi-deepfakes-cheapfakes-artificial-intelligence-disinformation.html
The A.I.-generated “deepfake” video implicitly but unmistakably calls for Facebook to make a public statement on its content moderation polices. The platform has long been criticized for permitting the spread of disinformation and harassment, but it became particularly acute recently, when the company said that it would not remove the “Drunk Pelosi” video.
On Thursday, the House Permanent Select Committee on Intelligence will hold an open hearing on A.I. and the potential threat of deepfake technology to Americans. Many technology researchers believe that deepfakes—realistic-looking content developed using machine learning algorithms—will herald a new era of information warfare. But as the “Drunk Pelosi” video shows, slight edits of original videos may be even more difficult to detect and debunk, creating a cascade of benefits for those willing to use these digital dirty tricks.
The video, posted to a self-described news Facebook page with a fan base of about 35,000, depicted Nancy Pelosi slurring her words and sounding intoxicated. However, when compared with another video from the same event, it was clear even to nonexperts that it had been slowed down to produce the “drunken” effect. Call it a “cheapfake”—it was modified only very slightly. While the altered video garnered some significant views on Facebook, it was only after it was amplified by President Donald Trump and other prominent Republicans on Twitter that it became a newsworthy issue. The heightened drama surrounding this video raises interesting questions not only about platform accountability but also about how to spot disinformation in the wild.
“Cheapfakes” rely on free software that allows manipulation through easy conventional editing techniques like speeding, slowing, and cutting, as well as nontechnical manipulations like restaging or recontextualizing existing footage that are already causing problems. Cheapfakes already call into question the methods of evidence that scientists, courts, and newsrooms traditionally use to call for accountability
Many will never know the video was a fake, but the advantages it gave to pundits will echo into the future. It’s a recent example of what legal theorists Bobby Chesney and Danielle Citron call the liar’s dividend . Those wishing to deny the truth can create disinformation to support their lie, while those caught behaving badly can write off the evidence of bad behavior as disinformation. In a new survey from Pew Research Center, 63 percent of respondents said that they believe altered video and images are a significant source of confusion when it comes to interpreting news quality. That loss of trust works in favor of those willing to lie, defame, and harass to gain attention.
As Daniel Kreiss and others have pointed out, people don’t just share content because they believe it. They do it for a host of reasons, not the least of which is simply because a message speaks to what users see as an implicit truth of the world even as they know it is not factually true. Researchers have found that creating and sharing hateful, false, or faked content is often rewarded on platforms like Facebook.
The looming threat of the deepfake is worth attention—from politicians, like at the upcoming hearing; from journalists; from researchers; and especially from the public that will ultimately be the audience for these things. But make no mistake: Disinformation doesn’t have to be high tech to cause serious damage.
#Fake_news #Deep_fake #Cheap_fake #Nancy_Pelosi #Médias_sociaux