Grammarly’s ’Expert Review’ Draws Criticism for Using Deceased Scholars’ Names

Grammarly (rebranded under parent Superhuman) launched an AI feature called Expert Review that provides writing feedback framed through the perspectives of named experts — including journalists, academics and some deceased scholars. The feature uses the company’s large language models to surface suggestions “inspired by” publicly available and widely cited works and lets users opt into expert-styled critiques via the Superhuman Go browser extension. Academics have complained the tool appears to ‘resurrect’ dead scholars without consent, calling the practice “obscene” and ethically troubling, while others warn it could deepen distrust of AI in education. Grammarly says Expert Review doesn’t claim endorsement from the named experts and that selections are based on publicly available publications. The rollout follows similar industry moves to create persona-driven AI assistants (e.g., Meta’s celebrity chatbots, Khan Academy’s historical role-play tutors). Key names highlighted by tests include Margaret Sullivan, Jack Shafer, Lawrence Lessig, Timnit Gebru and Helen Nissenbaum. The controversy centers on consent, attribution, and academic trust rather than any direct technical risk to crypto markets, but it underscores broader regulatory and reputational risks for AI product makers.
Neutral
This story is primarily about AI ethics, product design and reputational risk rather than anything that directly affects cryptocurrency markets or on-chain activity. For traders, the immediate market impact is neutral: no direct token, protocol, or regulatory change tied to crypto is reported. Similar controversies (e.g., persona-based chatbots or disputed AI training data) typically generate PR backlash, potential legal exposure, and regulatory attention for the AI companies involved, which can affect those firms’ valuations or partnerships but rarely move crypto prices. Short-term: possible heightened caution among tech investors toward AI-adjacent stocks or services, but crypto traders are unlikely to change positions based on this alone. Long-term: broader regulatory scrutiny of AI (including rules on data, likeness and consent) could influence investor sentiment across tech sectors; if rules materially affect AI infrastructure providers tied to crypto (cloud providers, AI-tokenized services), there could be downstream effects. Overall, the article signals reputational and legal risk trends in AI — important for macro/tech-aware traders — but it does not present immediate bullish or bearish catalysts for crypto assets.