Change Over Time

Description

Escalation in frequency, intensity, emotional reliance, secrecy, ideological narrowing, or operational specificity.

Rationale

Escalation over time is one of the most reliable risk indicators across SPJ frameworks. Increasing intensity, emotional reliance, secrecy, or operational specificity in AI interactions signals progressive pathway movement rather than static behavior. Trajectory matters as much as current state.

Evidence Base

• Inside a Mass Shooter’s Harrowing History With ChatGPT

  Mark Follman, 2024. Inside a Mass Shooter’s Harrowing History With ChatGPT. Mother Jones.

  News / journalism

• AI Agents are Vulnerable to Radicalization

  Ozgur Can Seckin, Shalmoli Ghosh, Alessandro Flammini, Kristina Lerman, Maria Elizabeth Grabe, Filippo Menczer, 2024. AI Agents are Vulnerable to Radicalization. arXiv, 29 Sep 2026.

  Peer-reviewed study (original research)

• It Is the Journey, Not the Destination: Moving From End Points to Trajectories When Assessing Chatbot Mental Health Safety

  Morrin H, Au Yeung J, Agnew Z, Østergaard SD, Pollak TA, 2024. It Is the Journey, Not the Destination: Moving From End Points to Trajectories When Assessing Chatbot Mental Health Safety. JMIR Ment Health, 13(e91454). https://doi.org/10.2196/91454

  Commentary / Editorial

• Intelligent Systems, Vulnerable Minds: A Framework for Radicalization to Violence in the Age of AI

  Kunst et al., 2026. Intelligent Systems, Vulnerable Minds: A Framework for Radicalization to Violence in the Age of AI. Personality and Social Psychology Review, 30(3), 395-426. https://doi.org/10.1177/10888683261430089

  Peer-reviewed study (original research)

• Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability

  Hannah Lee, BS, Rebecca Handler, MSc, Tushar Mungle, PhD, Tina Hernandez-Boussard, PhD, 2026. Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability. Journal of the American Medical Informatics Association, 33(8), 1538–1553. https://doi.org/10.1093/jamia/ocag078

  Peer-reviewed study (original research)