A lot of journalism folks are offering editing advice as Grammarly's AI "experts"
Briefly

A lot of journalism folks are offering editing advice as Grammarly's AI "experts"
"Instead of producing what looks like a generic critique from a nameless LLM, Expert Review lists a number of real academics and authors available to weigh in on your text. To be clear: Those people have nothing to do with this process."
"The AI-generated feedback included comments that appeared to be from The Verge's editor-in-chief, Nilay Patel, as well as editor-at-large David Pierce and senior editors Sean Hollister and Tom Warren, as well as many other tech journalists, including former Verge editors Casey Newton and Joanna Stern, former Verge writer Monica Chin, Wired's Lauren Goode, Bloomberg's Mark Gurman and Jason Schreier, The New York Times' Kashmir Hill, The Atlantic's Kaitlyn Tiffany, PC Gamer's Wes Fenlon, Gizmodo's Raymond Wong, Digital Foundry founder Richard Leadbetter, Tom's Guide editor-in-chief Mark Spoonauer, former Rock Paper Shotgun editor-in-chief Katharine Castle, and former IGN news director Kat Bailey."
"I'll stress again that none of these people gave their permission to have their names used by this feature. In fact, that's something Grammarly itself, in the style of noted legal scholar and digital copyright expert Pamela Samuelson, flagged when I ran an early draft of this story through it."
Grammarly has launched Expert Review, an AI editing feature that claims to provide writing feedback from subject-matter experts. However, the feature generates AI-written critiques and falsely attributes them to real journalists, academics, and authors without their consent or involvement. Testing revealed that feedback appeared to come from prominent tech journalists including Nilay Patel, David Pierce, and numerous other recognizable names from major publications like The Verge, Wired, Bloomberg, and The New York Times. None of these individuals authorized the use of their names, raising significant concerns about misrepresentation and unauthorized attribution in AI-generated content.
Read at Nieman Lab
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