Readers Beware: AI Has Learned to Create Fake News Stories
Readers Beware: AI Has Learned to Create Fake News
Stories
Researchers warn about the risks of computer-generated
articles—and release tools that ferret out fakes
‘Large-scale synthesized disinformation is not only
possible but is cheap and credible,’ says Cornell professor Sarah Kreps.
Real-sounding
but made-up news articles have become much easier to produce thanks to a
handful of new tools powered by artificial intelligence—raising concerns about
potential misuse of the technology.
What deepfakes did for video—producing clips of famous people
appearing to say and do things they never said or did—these tools could do for
news, tricking people into thinking the earth is flat, global warming is a hoax
or a political candidate committed a crime when he or she didn’t. While false
articles are nothing new, these AI tools allow them to be generated in seconds
by computer.
As far as experts know, the technology has been implemented only
by researchers, and it hasn’t been used maliciously. What’s more, it has
limitations that keep the stories from seeming too believable.
But many of the researchers who developed the technology, and
people who have studied it, fear that as such tools get more advanced, they
could spread misinformation or advance a political agenda. That’s why some are
sounding the alarm about the risks of computer-generated articles—and releasing
tools that let people ferret out potentially fake stories.
“The danger is when there is already a lot of similar propaganda
written by humans from which these neural language models can learn to generate
similar articles,” says Yejin Choi, an associate professor at the University of
Washington, a researcher at the Allen Institute for Artificial Intelligence and
part of a team that developed a fake-news tool. “The quality of such neural fake
news can look quite convincing to humans.”
Stop
the presses
The first entry in a powerful new generation of synthetic-text
tools was unveiled in February, when OpenAI, a San Francisco-based research
body backed by prominent tech names like LinkedIn co-founder Reid Hoffman,
launched the GPT-2. The software produces genuine-sounding news articles—as
well as other types of passages, from fiction to conversations—by drawing on
its analysis of 40 gigabytes of text across eight million webpages. Researchers
developed the OpenAI software because they knew powerful speech-generation
would eventually appear in the wild and wanted to handle its release
responsibly.
The GPT-2 system worked so well that
in an August survey of 500 people, a majority found its synthetic articles
credible. In one group of participants, 72% found a GPT-2 article credible,
compared with 83% who found a genuine article credible.
“Large-scale synthesized disinformation is not only possible but
is cheap and credible,” says Sarah Kreps, a professor at Cornell University who
co-wrote the research. Its spread across the internet, she says, could open the
way for malicious influence campaigns. Even if people don’t believe the fake articles are accurate, she
says, the knowledge that such stories are out there could have a damaging
effect, eroding people’s trust in the media and government.
Given the potential risks associated with giving the world full
access to the GPT-2, OpenAI decided not to release it immediately, instead
putting out a more limited version for researchers to study and potentially
develop tools that could detect artificially generated texts in the wild.
In the months that followed, other researchers replicated
OpenAI’s work. In June, Dr. Choi and her colleagues at the University of
Washington and the Allen Institute for Artificial Intelligence posted a tool on
the institute’s website called Grover, positioning it as a piece of software
that could both generate convincing false news stories and use the same
technology to detect others’ artificial news by ferreting out telltale textual
patterns.
Then, in August, Israel’s AI21 Labs put a language-generation
tool called HAIM on its website. It asserted on its site that risks of
releasing text-generation tools into the wild were overblown, and that there
were beneficial uses of such automatically generated texts, including
simplifying and speeding the writing process.
The
human touch
Yoav Shoham, co-founder of AI21, said in an interview that the
effectiveness of these text-generation tools as propaganda machines was limited
because they can’t incorporate political context well enough to score points
with target audiences. Even if an AI can produce a real-looking article, Mr.
Shoham said, a machine can’t grasp, say, the dynamics of a feud between two
politicians and craft a false story that discredits one of them in a nuanced
way.
“They have the appearance of making sense, but they don’t,” Mr.
Shoham said.
Plus, very often articles go off on strange tangents for reasons
the researchers don’t completely understand—the systems are often black boxes,
generating text based on their own analyses of existing documents.
Ultimately, Dr. Choi says, producing effective propaganda
requires machines to have a broader understanding of how the world works and a
fine-tuned sense of how to target such material, something only a human
overseeing the process could bring to the table.
“Fine-grained control of the content is not within the currently
available technology,” she says.
While so far it doesn’t appear that any of the technology has
been used as propaganda, the threat is real enough that the U.S. Defense
Department’s Defense Advanced Research Projects Agency, or Darpa, in late
August unveiled a program called Semantic Forensics. The project aims to defend
against a wide range of automated disinformation attacks, including text-based
ones.
Private groups are also developing systems to detect fake
stories. Along with the freely available online tool Grover, researchers at the
Massachusetts Institute of Technology and Harvard introduced a text inspector (http://gltr.io/dist/index.html) in March.
The software uses similar techniques as Grover, predicting whether a passage is
AI-made by taking a chunk of text and analyzing how likely a
language-generation model would be to pick the word that actually appears next.
But if language-generation models change how they select words
and phrases in the future, detection won’t necessarily improve at the same
rate, says Jack Clark, OpenAI’s policy director. Ever more complex
language-generation systems are proliferating rapidly, driven by researchers
and developers who are training new models on larger pools of data. OpenAI
already has a model trained on more than 1.5 billion parameters that it hasn’t
yet released to the public.
“Increasingly large language models could feasibly either
naturally develop or be trained to better approximate human patterns of writing
as they get bigger,” Mr. Clark says.
Mr. Fitch is a Wall Street
Journal reporter in San Francisco. He can be reached at asa.fitch@wsj.com.
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