Writing anonymously may not hide your identity for much longer if the US government’s latest artificial intelligence project proves successful.
The Office of the Director of National Intelligence has announced(Opens in a new window) a new AI project being managed by the Intelligence Advanced Research Projects Activity (IARPA)(Opens in a new window), which is focused on linguistic fingerprint technology. IARPA describes itself as investing in “high-risk, high-payoff research programs to tackle some of the most difficult challenges of the agencies and disciplines in the Intelligence Community,” and this certainly counts as one.
It’s called the Human Interpretable Attribution of Text Using Underlying Structure (HIATUS) program, and aims to advance human language technology to the point where authors can be identified simply by their writing style. The goal is for HIATUS to be multilingual and capable of differentiating authors based on stylistic features such as word choice, sentence phrasing, and organization of information.
While that may ring alarm bells for anyone who wishes to write anonymously, IARPA points out HIATUS can also work to protect an identity. By automatically modifying a known author’s linguistic pattern, it shouldn’t be possible for an AI to figure out who an author is. HIATUS also has the goal of being able to explain to “novice users” how it can attribute a piece of writing to a specific author.
HIATUS is seen as a big challenge, and IARPA awarded research contracts to six organizations to help it come to fruition. Those organizations include Charles River Analytics, Inc., Leidos, Inc., Raytheon BBN, SRI International, University of Pennsylvania, and University of Southern California.
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“Each of the selected performers brings a unique, novel, and compelling approach to the HIATUS challenge,” said program manager Dr. Tim McKinnon. “We have a strong chance of meeting our goals, delivering much-needed capabilities to the Intelligence Community, and substantially expanding our understanding of variation in human language using the latest advances in computational linguistics and deep learning.”
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