AI for Good by unknow

Author:unknow
Language: eng
Format: epub
Publisher: John Wiley & Sons, Incorporated
Published: 2024-02-21T00:00:00+00:00

Why Is This Important?

The serious negative effects of misinformation have been made evident by numerous academic studies, as well as by public incidents like “Pizzagate.” While misinformation is not a new phenomenon, the growth of social media online news platforms has enabled false stories to spread to more people more quickly than they would through traditional media.

The material Internet users see is influenced not only by their own choices, but also by the options presented to them—or indeed recommended to them—by the sites they visit. These sites are financially motivated to keep users on their platform engaging with more material. There are several ways to profit from increased user engagement—including subscriptions and donations—but the principal method of monetization is ad revenue. The implication is that, for unreliable sites that promote misinformation, efforts to keep users in their ecosystems create the potential for “rabbit holes” of unreliable content.

Additionally, there may be incentives for unreliable sites to send users to other unreliable sites via links embedded in their articles. Such incentives could include referral programs or the ideological alignment of these sites. If these patterns exist, they could also contribute to the formation of misinformation rabbit holes, as users encounter multiple unreliable sites reinforcing the same false narratives.

As part of Microsoft, the AI for Good Lab has the remarkable opportunity to conduct research using first-party data that can shed light on how people around the world interact with digital technologies. Accompanying this opportunity is a responsibility to investigate how these technologies are serving the users they are meant to benefit.

In this collaboration, the AI For Good Lab and subject matter experts from Princeton University's Empirical Studies of Conflict Project worked to develop insights on how Internet users use both reliable and unreliable websites and applied those insights to the development of a machine learning model that could be deployed at scale to identify new misinformation sites.

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