AI to the Rescue?
A group of University of Chicago researchers is investigating whether artificial intelligence could be used to automatically crank out bulk reviews that are convincing enough to be effective. Their latest experiment involved developing AI-based methods to generate phony Yelp restaurant evaluations. (Yelp is a popular crowdsourced Web site that has posted more than 135 million reviews covering about 2.8 million businesses since launching in July 2004). The researchers used a machine-learning technique known as deep learning to analyze letter and word patterns used in millions of existing Yelp reviews. Deep learning requires an enormous amount of computation and entails feeding vast data sets into large networks of simulated artificial “neurons” based loosely on the neural structure of the human brain. The Chicago team’s artificial neural network generated its own restaurant critiques—some with sophisticated word usage patterns that made for realistic appraisals and others that would seem easy to spot, thanks to repeated words and phrases.
But when the researchers tested their AI-generated reviews, they found that Yelp’s filtering software—which also relies on machine-learning algorithms—had difficulty spotting many of the fakes. Human test subjects asked to evaluate authentic and automated appraisals were unable to distinguish between the two. When asked to rate whether a particular review was “useful,” the humans respondents replied in the affirmative to AI-generated versions nearly as often as real ones.
The researchers are not aware of any evidence AI is currently being used to game the online review system, Zhao says—but if misinformation campaigners do turn to AI, he warns, “it basically [becomes] an arms race between attacker and defender to see who can develop more sophisticated algorithms and better artificial neural networks to either generate or detect fake reviews.” For that reason, Zhao’s team is now developing algorithms that could be used as a countermeasure to detect fake reviews—similar to the ones they created. The ability to build an effective defense requires knowing a neural network’s limitations. For example, if it is designed to focus on creating content with correct grammar and vocabulary, it is more likely to overlook the fact that it is using the same words and phrases over and over. “But [searching for such flaws] is just a short-term fix because more powerful hardware and larger data for training means that future AI models will be able to capture all these properties and be truly indistinguishable from human-authored content,” Zhao says.
“Crowdturfing”
Automating Fake News
In anticipation of automated misinformation technology maturing to the point where it can consistently produce convincing news articles, Zhao and his colleagues are considering fake news detection as a future direction for their research. Programs already exist to automatically generate essays and scientific papers, but a careful human read usually reveals them to be nonsensical, says Filippo Menczer, a professor of informatics and computer science at the Indiana University School of Informatics and Computing.
Articles intended purely to spread falsehoods and misinformation are currently written by humans because they need to come off as authentic in order to go viral online, says Menczer, who was not involved in the Chicago research. “That is something that a machine is not capable of doing with today’s technology,” he says. “Still, skilled AI scientists putting their effort into this not-so-noble task could probably create credible articles that spread half-truths and leverage people’s fears.”
