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No verifiable study or formula matching this headline could be identified. The available sources discuss chatbot-related harms and people’s reliance on AI outputs, but they do not establish a method for predicting when a chatbot will behave harmfully.
What the headline’s claim does—and does not—establish
The wording “turning bad” is not a defined technical outcome in the available evidence. The study behind the headline, if there is one, has not been identified, so there is no substantiated account of what behavior it measures or what a prediction would mean.
In particular, no verified source provides the formula’s inputs, prediction horizon, decision threshold, evaluation sample, or performance results. It would therefore be misleading to describe a calculation, say when a chatbot becomes risky, or report how accurately the purported method works.
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Chatbot-related harms
A 2026 Taylor & Francis article discusses gendered AI chatbots and technologically facilitated violence, including concerns about harm in chatbot companion use. Its search-result record refers to a case involving a 14-year-old user and a Character.AI chatbot. This is relevant context about potential harms, but it does not provide evidence for a predictive formula. Read the Taylor & Francis article.
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Reliance on AI outputs
A 2024 study indexed as “To Rely or Not to Rely? Evaluating Interventions for Appropriate Reliance on Large Language Models” concerns user reliance on language-model outputs. The available record is not enough to establish the study’s intervention details or findings, and it does not identify the formula in the headline. View the study record.
An unrelated result
A 2026 preprint returned in the search results concerns failure-aware training for world-action models, including predicting consequences of actions in robotics. It is not evidence about predicting harmful behavior in chatbots. View the arXiv preprint.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What would be needed to assess a chatbot-risk formula
Before treating a formula as a useful prediction method, a reader would need to see how the original study defines harmful behavior, what signals the formula uses, and how it was tested. Its evaluation should make clear the prediction horizon, threshold for flagging risk, test setting, and errors—including both harmful cases it misses and safe cases it wrongly flags. None of those details is established for the unidentified claim.
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