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Current machine learning models aren't yet up to the task of distinguishing false news reports, two new papers by MIT researchers show.

The big picture: After different researchers showed that computers can convincingly generate made-up news stories without much human oversight, some experts hoped that the same machine-learning-based systems could be trained to detect such stories. But MIT doctoral student Tal Schuster's studies show that, while machines are great at detecting machine-generated text, they can't identify whether stories are true or false.

Details: Many automated fact-checking systems are trained using a database of true statements called Fact Extraction and Verification (FEVER).

  • In one study, Schuster and team showed that machine learning-taught fact-checking systems struggled to handle negative statements ("Greg never said his car wasn't blue") even when they would know the positive statement was true ("Greg says his car is blue").
  • The problem, say the researchers, is that the database is filled with human bias. The people who created FEVER tended to write their false entries as negative statements and their true statements as positive statements — so the computers learned to rate sentences with negative statements as false.
  • That means the systems were solving a much easier problem than detecting fake news. "If you create for yourself an easy target, you can win at that target," said MIT professor Regina Barzilay. "But it still doesn't bring you any closer to separating fake news from real news."
  • Both studies were headed by Schuster with teams of MIT collaborators.

The bottom line: The second study showed that machine-learning systems do a good job detecting stories that were machine-written, but not at separating the true ones from the false ones.

Yes, but: While you can generate bogus news stories more efficiently using automated text, not all stories created by automated processes are untrue.

Go deeper

Ben Geman, author of Generate
4 mins ago - Energy & Environment

Japan vows deeper emissions cuts ahead of White House summit

Japanese Prime Minister Yoshihide Suga. Photo: Carl Court/Getty Images

Japan on Thursday said it will seek to cut greenhouse gas emissions by 46% below 2013 levels by 2030, per the AP and other outlets.

Why it matters: The country is the world's fifth-largest largest carbon dioxide emitter and a major consumer of coal, oil and natural gas.

Biden pledges to cut greenhouse gas emissions by up to 52% by 2030

U.S. President Joe Biden seen in the Oval Office on April 15. (Photo by Doug Mills-Pool/Getty Images)

The Biden administration is moving to address global warming by setting a new, economy-wide greenhouse gas emissions reduction target of 50% to 52% below 2005 levels by 2030.

Why it matters: The new, non-binding target is about twice as ambitious as the previous U.S. target of a 26% to 28% cut by 2025, which was set during the Obama administration. White House officials described the goal as ambitious but achievable during a call with reporters Tuesday night.

Exclusive: Chauvin trial prosecution worked with strategic communications firm

People gather at the intersection of 38th Street and Chicago Avenue to celebrate the guilty verdict in the Derek Chauvin trial on April 20, 2021 in Minneapolis, Minnesota. Photo: Brandon Bell/Getty Images

For most of the past year, a strategic communications firm with deep Washington ties has played an integral role for the prosecution in the State of Minnesota v. Derek Chauvin — operating without pay and so under-the-radar that most of its own staff had no idea.

The big picture: Finsbury Glover Hering — formerly known as the Glover Park Group — has been conducting media monitoring and analysis as part of legal team special prosecutor Neal Katyal's vision for a three-pronged "modern appeal/trial strategy."