Tech UPTechnologyCan artificial intelligence end fake news?

Can artificial intelligence end fake news?

Do you want to sound like Obama? In the past, that might have required physically mimicking her voice. And even if you were very good at it, it almost certainly would not pose a danger to our democracy. But technology has changed this. Now you can make anyone say anything with ease and precision through AI. You simply have to use the service of an online program to record a prayer and listen to it with the voice of a famous person.

Programs like this are often referred to as deep fakes – artificial intelligence systems that adapt audio, images, and videos to get people to say and do things they never did.

These technologies could usher in a new era of fake news and misinformation on the web. In 2017, Hany Farid, a computer scientist at Dartmouth College, USA, who detects fake videos, said that the rapid proliferation of new manipulation techniques has led to an “arms race.” Imagine what the elections will be like when we can no longer rely on video and audio. But some researchers are now fighting back and showing that AI can also be used for good.

“AI has a lot of ethical issues,” said Francesco Nucci, director of applications research at the Engineering Group, based in Italy. “But sometimes it can also be the solution. You can use AI in unethical ways to, for example, create and spread fake news, but you can also use it to do good, for example to combat misinformation. “


Fact checkers

He is the principal investigator of the Fandango project, whose objective is precisely this. The team is creating software tools to help journalists and fact-checkers detect and combat fake news, Nucci says. They hope to serve journalists in three ways.

The first component is what Nucci calls content independent detection using tools that target the shape of the content.

Nucci explains that today, images and videos can be easily manipulated, either through simple Photoshop or more complex techniques like deep fakes. Fandango’s systems can reverse engineer those changes and use algorithms to help journalists detect tampered content.

As these tools look at the form, they don’t check to see if the content itself makes false claims, which is what Fandango’s second line of research does. Here they link to stories that have been proven false by human fact-checkers and search for online pages or social media posts with similar words and statements.

“The tools can detect which fake news shares the same root and allow journalists to investigate them,” Nucci clarifies.

Both components rely heavily on various artificial intelligence algorithms, such as natural language processing. The third component allows journalists to respond to fake news.

A false story could, for example, claim that a very high percentage of crime in a European country is committed by foreign immigrants. In theory, that could be an easy claim to disprove due to the vast amount of open data available, but journalists waste valuable time finding that data. Therefore, the Fandango tool links all types of European open data sources, groups and visualizes them. For example, journalists can use pooled national data to address crime complaints or apply data from European Copernicus satellites to climate change debates.

“In this way, journalists can respond quickly to false stories and not waste time,” explains the expert.

Its tools are currently being tested by Belgian public broadcaster VRT, ANSA, the leading Italian news agency, and CIVIO, a Spanish non-profit organization.


Fake news detection

However, detecting fake news could not only be a matter of finding false claims, but also analyzing massive amounts of patterns to share on social media, says Michael Bronstein, a professor at the University of Lugano in Switzerland and Imperial College London. , UK.

He runs a project called GoodNews , which uses artificial intelligence to take an atypical approach to detecting fake news.

“Most of the existing approaches analyze content,” said Professor Bronstein. “They analyze the semantic characteristics that are characteristic of fake news. Which works up to a point, but you run into all kinds of problems.

“There are, for example, language barriers, platforms like WhatsApp do not give you access to content because it is encrypted and, in many cases, fake news can be an image, which is more difficult to analyze using techniques such as processing the natural language “.

So Professor Bronstein and his team turned this model upside down and instead watched fake news spread.

Essentially, previous studies show that fake news is shared on the internet in different ways than real news, says Professor Bronstein. Fake news can have a lot more shares than likes on Facebook, while regular posts tend to have more likes than shares. By spotting patterns like these, GoodNews gives a news story a credibility score.

The team has built their first prototype, which uses graph-based machine learning, an artificial intelligence technique in which Professor Bronstein is an expert. The prototype is trained on data from Twitter, where researchers track stories verified by journalists and proven to be false. In this way, journalists train the AI algorithm by showing it which stories are false and which are not.

The GoodNews team hopes to monetize this service through a London-based startup called Fabula AI. Although they hope to launch the product by the end of the year, they anticipate having clients such as large media companies like Facebook and Twitter, but also individual users.

“Our broader vision is that we want to become a news credibility rating agency , in the same way that certain companies rate a person’s consumer credit rating,” Bronstein explains.


Solve the problem

Of course, that leaves a bigger question: can technology really solve fake news? Both researchers are skeptical, but they are convinced that technology can help. Nucci emphasizes that the concept of fake news is controversial and that the stories are often not entirely true, but not entirely false either.

“Fake news is not a mathematical question of algorithms and data,” he said. “But it is a very philosophical question of how we deal with the truth. However, our technology can help improve transparency around false statements and misinformation. “

Professor Bronstein says it would be naive to expect technology to solve the fake news problem.

“It is not just about detecting fake news. It is also a problem of confidence and a lack of critical thinking. People are losing confidence in institutions and traditional media, and that is not something that can be mitigated only through technology.

“It requires the effort of all stakeholders and, hopefully, our project can participate in this larger effort,” he concludes.


Original article

This article was originally published in Horizon, the EU Research and Innovation Magazine

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