Slashdot: Ex-NSA Hacker Is Building an AI To Find Hate and Far-Right Symbols on Twitter and Facebook
Ex-NSA Hacker Is Building an AI To Find Hate and Far-Right Symbols on Twitter and Facebook
Published on January 06, 2018 at 02:00AM
Motherboard reporter Lorenzo Franceschi-Bicchierai has interviewed Emily Crose, a former NSA hacker, who has built NEMESIS, an AI-powered program that can help spot symbols that have been co-opted by hate groups to signal to each other in plain sight. Crose, who has also moderated Reddit in the past, thought of building NEMESIS after the Charlottesville, Virginia incident last year. From the report: Crose's motivation is to expose white nationalists who use more or less obscure, mundane, or abstract symbols -- or so-called dog whistles -- in their posts, such as the Black Sun and certain Pepe the frog memes. Crose's goal is not only to expose people who use these symbols online but hopefully also push the social media companies to clamp down on hateful rhetoric online. "The real goal is to educate people," Crose told me in a phone call. "And a secondary goal: I'd really like to get the social media platforms to start thinking how they can enforce some decency on their own platforms, a certain level of decorum." [...] At a glance, the way NEMESIS works is relatively simple. There's an "inference graph," which is a mathematical representation of trained images, classified as Nazi or white supremacist symbols. This inference graph trains the system with machine learning to identify the symbols in the wild, whether they are in pictures or videos. In a way, NEMESIS is dumb, according to Crose, because there are still humans involved, at least at the beginning. NEMESIS needs a human to curate the pictures of the symbols in the inference graph and make sure they are being used in a white supremacist context. For Crose, that's the key to the whole project -- she absolutely does not want NEMESIS to flag users who post Hindu swastikas, for example -- so NEMESIS needs to understand the context. "It takes thousands and thousands of images to get it to work just right," she said.
Published on January 06, 2018 at 02:00AM
Motherboard reporter Lorenzo Franceschi-Bicchierai has interviewed Emily Crose, a former NSA hacker, who has built NEMESIS, an AI-powered program that can help spot symbols that have been co-opted by hate groups to signal to each other in plain sight. Crose, who has also moderated Reddit in the past, thought of building NEMESIS after the Charlottesville, Virginia incident last year. From the report: Crose's motivation is to expose white nationalists who use more or less obscure, mundane, or abstract symbols -- or so-called dog whistles -- in their posts, such as the Black Sun and certain Pepe the frog memes. Crose's goal is not only to expose people who use these symbols online but hopefully also push the social media companies to clamp down on hateful rhetoric online. "The real goal is to educate people," Crose told me in a phone call. "And a secondary goal: I'd really like to get the social media platforms to start thinking how they can enforce some decency on their own platforms, a certain level of decorum." [...] At a glance, the way NEMESIS works is relatively simple. There's an "inference graph," which is a mathematical representation of trained images, classified as Nazi or white supremacist symbols. This inference graph trains the system with machine learning to identify the symbols in the wild, whether they are in pictures or videos. In a way, NEMESIS is dumb, according to Crose, because there are still humans involved, at least at the beginning. NEMESIS needs a human to curate the pictures of the symbols in the inference graph and make sure they are being used in a white supremacist context. For Crose, that's the key to the whole project -- she absolutely does not want NEMESIS to flag users who post Hindu swastikas, for example -- so NEMESIS needs to understand the context. "It takes thousands and thousands of images to get it to work just right," she said.
Read more of this story at Slashdot.
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