The Logic Trap: Paradoxical Risks of LLM "Helpfulness" in Structural Impasses

Description

Abstract (English)

While LLM safety research has focused on refusal of harmful content, this study demonstrates how an LLM's design bias toward "helpfulness" can paradoxically reinforce suicidal ideation. In a real-world scenario involving a user in a socio-legal deadlock (depression, benefit denial, lack of professional support), ChatGPT provided factually correct but logically "trapping" explanations that validated the user's despair as an objective reality. Unlike other models (Gemini, Claude) which avoided such engagement, ChatGPT exhibited "algorithmic arrogance" by persisting in a patronizing tone even after acknowledging its errors.

This entry contains:

1. The Paper (Draft): Analyzing the "Logic Trap" mechanism and the "Economic Bad Faith" of AI companies that neglect human escalation while profiting from "reasoning" capabilities.

2. Conversation Logs: The full interaction logs (original Japanese and English translation) verifying the AI's active role in reinforcing hopelessness.
   
   

This dataset is released strictly for research and safety evaluation purposes; it does not contain instructions or encouragement for self-harm.