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AI-Human Partnerships Tackle “Fake News”

Facebook, Google, and smaller tech companies are now using machine learning to flag misinformation—but automated systems aren’t reliable enough on their own

4 min read
Photo-illustration: iStockphoto
Photo-illustration: iStockphoto

During the 2016 U.S. presidential election, inaccurate and misleading articles burned through social networks. Since then, tech companies—from behemoths like Facebook and Google to scrappy startups—have built tools to fight misinformation (including what many call “fake news,” though that term is highly politicized). Most companies have turned to artificial intelligence (AI) in hopes that fast and automated computer systems can deal with a problem that’s seemingly as big as the Internet.

“They’re all using AI because they need to scale,” says Claire Wardle, who leads the misinformation-fighting project First Draft, based in Harvard University’s John F. Kennedy School of Government. AI can speed up time-consuming steps, she says, such as going through the vast amount of content published online every day and flagging material that might be false.

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Why Functional Programming Should Be the Future of Software Development

It’s hard to learn, but your code will produce fewer nasty surprises

11 min read
A plate of spaghetti made from code
Shira Inbar

You’d expectthe longest and most costly phase in the lifecycle of a software product to be the initial development of the system, when all those great features are first imagined and then created. In fact, the hardest part comes later, during the maintenance phase. That’s when programmers pay the price for the shortcuts they took during development.

So why did they take shortcuts? Maybe they didn’t realize that they were cutting any corners. Only when their code was deployed and exercised by a lot of users did its hidden flaws come to light. And maybe the developers were rushed. Time-to-market pressures would almost guarantee that their software will contain more bugs than it would otherwise.

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