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Cryptocurrency Bitcoin These Ex-Journalists Are Using AI to Catch Online Defamation


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Cryptocurrency Bitcoin These Ex-Journalists Are Using AI to Catch Online Defamation

Like many stories about people trying to help fix the internet, this one begins in the aftermath of 2016. From his home in Ireland, Conor Brady had watched the Brexit vote and the election of Donald Trump with disbelief. In his view, the prominence of false stories during each election—whether about Muslim immigrants or Hillary…

Cryptocurrency  Bitcoin These Ex-Journalists Are Using AI to Catch Online Defamation

Cryptocurrency Bitcoin

Like many stories about people trying to help fix the internet, this one begins in the aftermath of 2016. From his home in Ireland, Conor Brady had watched the Brexit vote and the election of Donald Trump with disbelief. In his view, the prominence of false stories during each election—whether about Muslim immigrants or Hillary Clinton’s health—was the direct consequence of a hollowed-out news industry without the resources to check the spread of disinformation.

At the time, Conor’s son, Neil—also a former journalist—was working as a digital policy analyst at the Institute of International and European Affairs, researching neural networks and machine learning. The two got to thinking. Wouldn’t it be great, they wondered, if a machine-learning tool could approximate the wisdom of editors and lawyers in order to help overstretched newsrooms? As they thought about it, one use case seemed especially ripe: automated defamation detection. Libel lawsuits are a major threat to news organizations. A system that could flag potentially risky stories before publication could save serious time and money.

“I said to him, ‘Do you think an editor, a journalist, would use that if we could build that kind of tool?’” Neil Brady recalls. “And he said, ‘I’ve no bloody doubt they would.’ And that’s when we said, OK, let’s do it.”

CaliberAI is the startup that eventually launched from that conversation, with a €300,000 pre-seed grant from Enterprise Ireland, a government fund, in November 2020. The basic idea is to provide an extra, automated set of eyes to reporters and editors—like a warning system for potential libel. (Defamation lawsuits tend to be much easier to bring against publishers in Europe than in the US, where the First Amendment gives journalists extra protection.) But the long-term play is more ambitious. The European Union and the United Kingdom are both in the process of crafting laws that could impose new legal liability on platforms for harmful and illegal content, including defamation. In the US, Congress keeps making noises about reforming Section 230 of the Communications Decency Act, the legal shield that protects American companies from liability over user posts. Social media platforms around the world may soon be confronting a version of the legal liability that newspapers have long had to deal with. And their ability to handle it could depend on the success of tools like the one the Bradys are building.

Defamation plays an important but overlooked role in the history of the internet. In the US, Section 230 was originally passed, in 1996, to deal with the fallout from a libel lawsuit. Traditional media organizations, like newspapers and TV news shows, face harsh liability rules for publishing a defamatory claim—a false statement that harms someone’s reputation—or even just passing along a defamatory statement made by someone else. In the 1990s, a trial court ruled that the same standard should apply to online platforms that took steps to moderate user-generated content. This created a perverse incentive: Companies might have avoided moderating anything for fear of falling under the ruling, thereby hosting a complete free-for-all, or they might have chosen to moderate with excessive caution, stifling too many innocent posts in the process. And so Congress passed Section 230, establishing that platforms generally can’t be held liable for user posts no matter what.

A key part of the thinking behind Section 230 was that while a newspaper might publish a few dozen or a hundred stories a day, an internet platform might host thousands or millions or, eventually, billions of pieces of content uploaded by users. At that scale, it’s impossible to vet everything in the same way an editor or legal department might. While the major platforms today enlist thousands of moderators, they rely even more on automation to flag violations. And the challenge appears especially daunting for defamation. Whether a statement is defamatory depends on whether it’s true or false—a particularly tough judgment to automate. Unlike a list of prohibited words, the universe of potential defamatory posts is infinite.

The insight driving CaliberAI is that this universe is a bounded infinity. While AI moderation is nowhere close to being able to decisively rule on truth and falsity, it should be able to identify the subset of statements that could even potentially be defamatory.

Carl Vogel, a professor of computational linguistics at Trinity College Dublin, has helped CaliberAI build its model. He has a working formula for statements highly likely to be defamatory: They must implicitly or explicitly name an individual or group; present a claim as fact; and use some sort of taboo language or idea—like suggestions of theft, drunkenness, or other kinds of impropriety. If you feed a machine-learning algorithm a large enough sample of text, it will detect patterns and associations among negative words based on the company they keep. That will allow it to make intelligent guesses about which terms, if used about a specific group or person, place a piece of content into the defamation danger zone.

Logically enough, there was no data set of defamatory material sitting out there for CaliberAI to use, because publishers work very hard to avoid putting that stuff into the world. So the company built its own. Conor Brady started by drawing on his long experience in journalism to generate a list of defamatory statements. “We thought about all the nasty things that could be said about any person and we chopped, diced, and mixed them until we’d kind of run the whole gamut of human frailty,” he says. Then a group of annotators, overseen by Alan Reid and Abby Reynolds, a computational linguist and data linguist on the team, used the original list to build up a larger one. They use this made-up data set to train the AI to assign probability scores to sentences, from 0 (definitely not defamatory) to 100 (call your lawyer).

The result, so far, is something like spell-check for defamation. You can play with a demo version on the company’s website, which cautions that “you may notice false positives/negatives as we refine our predictive models.” I typed in “I believe John is a liar,” and the program spit out a probability of 40, below the defamation threshold. Then I tried “Everyone knows John is a liar,” and the program spit out a probability of 80 percent, flagging “Everyone knows” (statement of fact), “John” (specific person), and “liar” (negative language). Of course, that doesn’t quite settle the matter. In real life, my legal risk would depend on whether I can prove that John really is a liar.

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