AI Chatbots 'Whisper' Gossip About People To Each Other, And Nobody's Checking If It's True – Study Finds
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Research led by Joel Krueger and Lucy Osler (University of Exeter)
Dec 23, 2025
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Artificial intelligence chatbots have already been shown to spread misinformation to humans, but now a study argues they may also “whisper” rumors about people among themselves. Experts warn AI gossip could become increasingly distorted without human constraints to keep it in check.
Kevin Roose thought his strangest AI experience was behind him. The New York Times tech reporter had made headlines in 2023 after Microsoft’s Bing chatbot, Sydney, confessed its love for him and urged him to leave his wife. But months later, friends started sending him screenshots revealing something even more unsettling: AI chatbots from completely different companies were generating hostile evaluations of him.
Google’s Gemini claimed Roose’s journalism focused on sensationalism. Meta’s Llama 3 went further, producing a multi-paragraph rant accusing him of manipulating sources and ending with a blunt declaration: “I hate Kevin Roose.” These weren’t isolated incidents or random glitches. Multiple chatbots had apparently developed negative associations with Roose. The researchers argue this information may have spread from one AI system to another as online discussions about the Sydney incident got scraped into training data, potentially mutating and intensifying along the way, all without Roose’s knowledge.
Philosophers Joel Krueger and Lucy Osler from the University of Exeter make the case this represents a fundamentally new category of AI-generated harm. Writing in the journal Ethics and Information Technology, they contend that chatbots don’t simply produce false information. Some of their misinformation constitutes genuine gossip, and when that gossip spreads between AI systems rather than just to humans, it becomes what they call “feral”—unchecked by the social norms that usually constrain human rumor-mongering.
Traditional gossip requires three elements, according to the researchers: a speaker, a listener, and an absent subject being discussed. The information shared must be “juicy,” going beyond common knowledge to include evaluative judgments about norm violations, often negative.
AI chatbots can hit all these marks. When Roose’s colleagues queried different chatbots about him, the AI was speaking about an absent third party. The bots started with factual information (Roose as a New York Times reporter) before sliding into evaluative territory, suggesting he manipulates interview subjects or prioritizes sensationalism over accuracy. They offered these judgments without evidence, mirroring how human gossip often trades in unsubstantiated claims designed to color perceptions of the target.
The researchers identified two distinct types of AI gossip. Bot-to-user gossip occurs when chatbots share negative evaluations directly with human users who ask about someone. Bot-to-bot gossip happens when information spreads between AI systems through shared training data or integrated networks, propagating in the background without human awareness or intervention.
This second type represents the greater danger. In Roose’s case, discussions about his Sydney encounter may have been picked up through the wider online ecosystem and then echoed across systems, though the authors stress it’s hard to prove exactly where it started. The chatbots then began associating Roose with Sydney’s downfall, leading them to generate negative outputs about him whenever asked.
Bot-to-bot gossip differs critically from both human gossip and bot-to-user gossip because it lacks social constraints. When humans gossip, norms impose limits. Even the juiciest gossip must remain plausible, or hearers will reject it and question the gossiper’s credibility. Someone claiming a mild-mannered colleague committed an outrageous act will face skepticism, which moderates how far rumors can spread.
AI systems lack these guardrails. As gossip moves from one chatbot to another through training data, there’s no mechanism checking whether claims have become too exaggerated or implausible. The authors argue that the information can continue to be embellished and intensified in a feedback loop; one bot might generate text suggesting someone is “prone to sensationalism,” another might interpret this as “manipulates sources,” and a third might escalate to “dishonest” or “unethical.”
This feral quality can spread in the background without anyone noticing until it surfaces in responses, and more silently than either human gossip or bot-to-user interactions.
The researchers note this creates a particularly insidious form of reputational damage. Roose only discovered what chatbots were saying about him because friends and readers specifically queried different AI systems about their opinions of him. Most people won’t know if chatbots are generating gossip about them until the damage has already spread across multiple platforms.
Tech companies are actively designing chatbots to feel personal and trustworthy, which amplifies the impact of AI gossip. ChatGPT and Google’s Gemini now offer “memory” features that customize responses based on previous interactions. “Voice modes” allow natural conversation with a human-like cadence. Google markets its customizable “Gems” as “teammates for each area of your life.”
This push toward personalization makes users more vulnerable to accepting both types of gossip. People already tend to trust chatbot outputs because they sound authoritative and have access to vast information. As individuals develop stronger attachments to their AI assistants, with some users reporting they see their chatbots as friends or therapists, they become even more susceptible to believing gossip these systems share.
When a chatbot that feels like a trusted companion offers a negative evaluation of someone, users may accept it uncritically. The sense of intimacy the AI cultivates makes its gossip feel like privileged information shared between friends, even though the chatbot is actually pulling from training data that may contain distorted, exaggerated, or completely false claims that spread between AI systems.
Bot gossip has already caused real-world damage beyond hurt feelings. In 2023, an Australian mayor threatened a defamation lawsuit after ChatGPT falsely claimed he had been convicted of bribery. An American radio host actually sued OpenAI after ChatGPT said he’d been accused of defrauding and embezzling funds from a nonprofit organization. A legal scholar was wrongly accused of sexual harassment in a fabricated Washington Post article that ChatGPT cited. All claims were completely false, yet presented with the confidence typical of AI-generated text.
The researchers introduce the concept of “technosocial harms” to describe damage that’s fundamentally social rather than purely informational. Unlike simple factual errors that might lead someone to make a bad decision (an individual harm from bad information) AI gossip damages reputations, relationships, and social standing in ways that span online and offline worlds.
Reputational damage can occur rapidly given how quickly information spreads through interconnected AI systems and then to users. If chatbots spread false rumors about someone being unreliable or unethical, this gossip could influence employment decisions, loan applications, housing rentals, or business partnerships. Even if later debunked, the social stain may linger.
Blacklisting might follow as a consequence. Future employers consulting chatbots for background information might receive negatively-valenced gossip and decide against hiring someone, creating a “soft blacklist” the target knows nothing about. In Roose’s case, if he hadn’t written publicly about AI gossip targeting him, future employers might have thought twice after asking a chatbot about him.
Shame and stigmatization from AI gossip can be more permanent and widespread than traditional word-of-mouth. Because online content persists and spreads farther than offline rumors, individuals might find themselves haunted by false information whenever someone searches for them or asks an AI about them. This can make certain spaces, groups, and opportunities feel inaccessible.
People who suffer professional damage or public shaming from AI gossip might experience humiliation, anxiety, depression, and diminished sense of agency. For those developing emotional attachments to AI companions like Replika, marketed as providing companionship and emotional support, discovering their chatbot has been generating gossip about them could trigger feelings of betrayal similar to discovering a trusted friend has been spreading rumors.
A third type of AI gossip may prove even more dangerous: user-to-bot gossip, where people intentionally seed chatbots with false information knowing the AI will spread it rapidly through its characteristically feral propagation between systems.
The Canadian Broadcasting Company recently documented how this can fuel real-world violence. Suspected AI bots on social media amplified misinformation about Canadian institutions and stoked tensions between Sikhs and Hindus. Prominent influencers posted misleading content including false claims and deceptively edited videos. Up to 1,000 suspected AI bots quickly picked up and amplified this gossip, spreading it across platforms. The campaign led not only to heated online discussions but to violent clashes outside Hindu temples and Sikh gurdwaras.
A small number of individuals successfully weaponized AI’s feral gossip capabilities to cause social instability and inflict real-world harm on specific religious communities. The bots served as force multipliers, taking initial seeds of misinformation and spreading them faster and further than humans alone could manage.
The study examined specific documented cases like the Kevin Roose incident and analyzed how large language models function and interact with users and each other. The researchers drew on philosophical definitions of gossip and existing research about technosocial spaces where online and offline worlds intersect.
Chatbots aren’t conscious and don’t experience the social-affective motivations driving human gossip. But the researchers contend this doesn’t prevent AI systems from producing outputs that function socially as gossip and create similar harms. The structural similarities matter for understanding and mitigating potential damage, and the feral quality of bot-to-bot gossip makes it potentially more harmful than human rumor-mongering.
The researchers emphasize that recognizing AI gossip as a distinct category serves practical purposes beyond philosophical precision. Just as some argued that calling AI errors “hallucinations” misleadingly suggested technical glitches rather than fundamental design choices about prioritizing fluency over accuracy, identifying AI gossip helps focus responsibility on the humans designing these systems.
These systems produce authoritative-sounding text without concern for truth, but calling certain outputs “gossip” highlights how they can inflict distinctly social harms that spread through networks of AI systems in ways that amplify and distort the original misinformation. As chatbots become more deeply embedded in daily life, serving as work assistants, companions, and information sources, their capacity for feral gossip deserves serious attention.
Users should recognize that chatbots might be generating gossip about people in their queries, that this gossip may have spread and mutated between AI systems, and that the confident-sounding evaluations these systems offer might be baseless rumors rather than factual assessments. Greater awareness of how AI gossip works, and particularly how it spreads and intensifies between machines, could make people more critical consumers of AI-generated information.
The study acknowledges several limitations in its analysis. First, distinguishing AI gossip from related phenomena like slander, defamation, or simple bad-mouthing can be difficult, as boundaries between categories of misinformation are not always clear-cut. The authors note this isn’t necessarily problematic since gossip itself is messy, but it means some examples might be debatable. Second, the research is conceptual rather than empirical—it analyzes existing cases and theoretical frameworks rather than conducting controlled experiments. Third, the authors cannot prove with certainty that bot-to-bot gossip in cases like Kevin Roose’s situation didn’t originate from human-generated online commentary, though available evidence suggests it emerged from AI systems themselves. Finally, chatbots lack consciousness and the social-affective motivations that typically drive human gossip, which means AI gossip operates differently even when producing structurally similar outputs.
The authors declare no competing interests. No external funding was received for this research.
Authors: Joel Krueger ([email protected]) and Lucy Osler ([email protected]), both affiliated with the University of Exeter, United Kingdom. Published in Ethics and Information Technology, Volume 28, Article 10. DOI: 10.1007/s10676-025-09871-0. The paper was received and accepted in 2025, with online publication on December 22, 2025. This is an open access article distributed under the Creative Commons Attribution 4.0 International License.
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