Google Cousin Develops Technology to Flag Toxic Online Comments

googleImplications for the classroom? When inviting students to participate online there is always a danger of unacceptable conduct online. From stupid aliases to unfavorable comments. We know this is a problem in many areas, not only among young people. This article caught my attention and is a smart start for any conversations on the topic.

Google has launched an artificial intelligence tool that identifies abusive comments online, helping publishers respond to growing pressure to clamp down on hate speech.  Google’s freely available software, known as Perspective, is being tested by a range of news organisations, including The New York Times, The Guardian and The Economist, as a way to help simplify the jobs of humans reviewing comments on their stories. “News organisations want to encourage engagement and discussion around their content, but find that sorting through millions of comments to find those that are trolling or abusive takes a lot of money, labour and time,” said Jared Cohen, president of Jigsaw, the Google social incubator that built the tool. “As a result, many sites have shut down. Source: Financial times

Jigsaw, a technology incubator within Alphabet, says it has developed a new tool for web publishers to identify toxic comments that can undermine a civil exchange of ideas. Starting Thursday, publishers can start applying for access to use Jigsaw’s software, called Perspective, without charge.

“We have more information and more articles than any other time in history, and yet the toxicity of the conversations that follow those articles are driving people away from the conversation,” said Jared Cohen, president of Jigsaw, formerly known as Google Ideas.

Unless carefully managed, discussion in comments sections often devolves into a hateful exchange. This has prompted some publishers to turn off the comments section because moderating them can be time-consuming.

With machine learning, a computer system is programmed to learn from repetition. It takes in training data — essentially, example after example — until it is familiar enough to anticipate with a high degree of confidence the proper response. Read article here.

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