Polisis AI Developed to Help People Understand Privacy Policies

It looks as though this AI development could be quite useful in helping people avoid the exploitation of their personal information. Someone reading this may also want to look into a resource called Terms of Service; Didn’t Read, which “aims at creating a transparent and peer-reviewed process to rate and analyse Terms of Service and Privacy Policies in order to create a rating from Class A to Class E.”

But one group of academics has proposed a way to make those virtually illegible privacy policies into the actual tool of consumer protection they pretend to be: an artificial intelligence that’s fluent in fine print. Today, researchers at Switzerland’s Federal Institute of Technology at Lausanne (EPFL), the University of Wisconsin and the University of Michigan announced the release of Polisis—short for “privacy policy analysis”—a new website and browser extension that uses their machine-learning-trained app to automatically read and make sense of any online service’s privacy policy, so you don’t have to.

In about 30 seconds, Polisis can read a privacy policy it’s never seen before and extract a readable summary, displayed in a graphic flow chart, of what kind of data a service collects, where that data could be sent, and whether a user can opt out of that collection or sharing. Polisis’ creators have also built a chat interface they call Pribot that’s designed to answer questions about any privacy policy, intended as a sort of privacy-focused paralegal advisor. Together, the researchers hope those tools can unlock the secrets of how tech firms use your data that have long been hidden in plain sight.

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Polisis isn’t actually the first attempt to use machine learning to pull human-readable information out of privacy policies. Both Carnegie Mellon University and Columbia have made their own attempts at similar projects in recent years, points out NYU Law Professor Florencia Marotta-Wurgler, who has focused her own research on user interactions with terms of service contracts online. (One of her own studies showed that only .07 percent of users actually click on a terms of service link before clicking “agree.”) The Usable Privacy Policy Project, a collaboration that includes both Columbia and CMU, released its own automated tool to annotate privacy policies just last month. But Marotta-Wurgler notes that Polisis’ visual and chat-bot interfaces haven’t been tried before, and says the latest project is also more detailed in how it defines different kinds of data. “The granularity is really nice,” Marotta-Wurgler says. “It’s a way of communicating this information that’s more interactive.”

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The researchers’ legalese-interpretation apps do still have some kinks to work out. Their conversational bot, in particular, seemed to misinterpret plenty of questions in WIRED’s testing. And for the moment, that bot still answers queries by flagging an intimidatingly large chunk of the original privacy policy; a feature to automatically simplify that excerpt into a short sentence or two remains “experimental,” the researchers warn.

But the researchers see their AI engine in part as the groundwork for future tools. They suggest that future apps could use their trained AI to automatically flag data practices that a user asks to be warned about, or to automate comparisons between different services’ policies that rank how aggressively each one siphons up and share your sensitive data.

“Caring about your privacy shouldn’t mean you have to read paragraphs and paragraphs of text,” says Michigan’s Schaub. But with more eyes on companies’ privacy practices—even automated ones—perhaps those information stewards will think twice before trying to bury their data collection bad habits under a mountain of legal minutiae.