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The AI That Could Heal a Divided Internet

TIME – “…Today, on social media platforms, content tends to be ranked by how much engagement it receives. Over the last two decades politics, the media, and culture have all been reshaped to meet a single, overriding incentive: posts that provoke an emotional response often rise to the top. Efforts to improve the health of online spaces have long focused on content moderation, the practice of detecting and removing bad content. Tech companies hired workers and built AI to identify hate speech, incitement to violence, and harassment. That worked imperfectly, but it stopped the worst toxicity from flooding our feeds. There was one problem: while these AIs helped remove the bad, they didn’t elevate the good. “Do you see an internet that is working, where we are having conversations that are healthy or productive?” asks Yasmin Green, the CEO of Google’s Jigsaw unit, which was founded in 2010 with a remit to address threats to open societies. “No. You see an internet that is driving us further and further apart.” What if there were another way? Jigsaw believes it has found one. On Monday, the Google subsidiary revealed a new set of AI tools, or classifiers, that can score posts based on the likelihood that they contain good content: Is a post nuanced? Does it contain evidence-based reasoning? Does it share a personal story, or foster human compassion? By returning a numerical score (from 0 to 1) representing the likelihood of a post containing each of those virtues and others, these new AI tools could allow the designers of online spaces to rank posts in a new way. Instead of posts that receive the most likes or comments rising to the top, platforms could—in an effort to foster a better community—choose to put the most nuanced comments, or the most compassionate ones, first. The breakthrough was made possible by recent advances in large language models (LLMs), the type of AI that underpins chatbots like ChatGPT. In the past, even training an AI to detect simple forms of toxicity, like whether a post was racist, required millions of labeled examples. Those older forms of AI were often brittle and ineffectual, not to mention expensive to develop. But the new generation of LLMs can identify even complex linguistic concepts out of the box, and calibrating them to perform specific tasks is far cheaper than it used to be. Jigsaw’s new classifiers can identify “attributes” like whether a post contains a personal story, curiosity, nuance, compassion, reasoning, affinity, or respect.”

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