Evaluating Language Models on Grooming Risk Estimation Using Fuzzy Theory

Current AI systems struggle with subtle manipulation tactics: Language models like SBERT show high prediction variance when detecting grooming that relies on indirect language rather than explicit sexual content. This means AI tools designed to protect children may miss dangerous conversations that use coded or implicit language—exactly the tactics real predators employ.

Training data limitations create blind spots: The study found that existing grooming detection systems are trained primarily on law enforcement and vigilante conversations, which may not accurately represent how actual predators communicate with victims. This gap between training data and real-world grooming means deployed systems could fail to identify genuine threats.

High-risk domains require higher accuracy standards: The paper emphasizes that child safety applications demand exceptionally high precision because false negatives (missed grooming) have severe consequences. Current language models' inconsistent performance on risky cases suggests they may not yet be reliable enough for deployment in this critical domain without additional safeguards.

Indirect speech acts need better AI understanding: The research identifies a specific technical gap: language models need to improve at understanding indirect manipulation tactics (like building trust or isolation) rather than just detecting explicit harmful language. This capability is essential for protecting children from sophisticated predators.

More realistic evaluation methods are needed: The study calls for better testing approaches that use actual victim-predator conversations rather than approximations, suggesting that responsible AI development in child safety requires more authentic data and evaluation frameworks.