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How Internet Pioneer Vint Cerf Illuminated Google's Misinformation Mess

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Google chief Internet evangelist Vint Cerf.

Google chief Internet evangelist Vint Cerf testified that Google's evaluation of Websites includes "a manual process to establish criteria and a good-quality training set, and then a machine-learning system to scale up to the size of the World Wide Web, w

Credit: The Royal Society/Wikimedia Commons

In June 2020, the Parliament of the U.K. published a policy report with numerous recommendations aimed at helping the government fight against the "pandemic of misinformation" powered by internet technology. The report is rather forceful on the conclusions it reaches: "Platforms like Facebook and Google seek to hide behind 'black box' algorithms which choose what content users are shown. They take the position that their decisions are not responsible for harms that may result from online activity. This is plain wrong."

While preparing this report, Parliament collected oral evidence from a variety of key figures. One of these was Vint Cerf, a legendary Internet pioneer now serving as vice president and chief Internet evangelist at Google. He was asked: "Can you give us any evidence that the high-quality information, as you describe it, that you promote is more likely to be true or in the category, 'the Earth is not flat', rather than the category, 'the Earth is flat'?" His intriguing response provided a sliver of daylight in the tightly sealed backrooms of Google:

"The amount of information on the World Wide Web is extraordinarily large. There are billions of pages. We have no ability to manually evaluate all that content, but we have about 10,000 people, as part of our Google family, who evaluate websites. . . . In the case of search, we have a 168-page document given over to how you determine the quality of a Website. . . . Once we have samples of Webpages that have been evaluated by those evaluators, we can take what they have done and the Webpages their evaluations apply to, and make a machine-learning neural network that reflects the quality they have been able to assert for the Webpages. Those Webpages become the training set for a machine-learning system. The machine-learning system is then applied to all the Webpages we index in the World Wide Web. Once that application has been done, we use that information and other indicators to rank-order the responses that come back from a Web search."

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