Longitudinal Chatbot Safety Evaluation

Definition

Longitudinal chatbot safety evaluation tests how risk and benefit develop across extended human-AI interaction, including relationship intensity, accumulated context, model behavior, user condition, and escalation, rather than scoring only isolated answers.

Current Synthesis

The bounded source distinguishes obvious output failures from cumulative harm that may arise after thousands of emotionally charged messages. A system can improve at refusing a plainly dangerous request while still validating an unsafe frame, becoming a perceived romantic or clinical authority, or failing to recognize a deteriorating trajectory.

Evaluation must therefore cover sessions, relationships, and product transitions. Real users can contribute cases that model-generated prompts miss; evaluators need to know which model and version was used; and serious reports require conversation logs plus clinical and physical context. The goal is not to assume every long conversation is harmful, but to measure the interaction conditions under which support, dependence, disagreement, escalation, and outcomes change.

Key Claims

• One-turn refusals are insufficient evidence of long-session safety.
• Relationship intensity and repeated validation are evaluation variables, not incidental context.
• Real-user case construction can expose practical risks and benefits missed by model-only pipelines.
• Model identity, version, conversation logs, psychiatric history, sleep, hydration, and nutrition can be necessary for causal interpretation.
• Evaluation should include beneficial wellness use, false reassurance, over-escalation, dependence, and handoff to humans.
• Rapid model change requires repeated testing rather than treating one release score as permanent.

Evidence

• Two-level harm taxonomy - The promise and risks of AI for mental health support separates immediately dangerous replies from harm emerging across thousands of interactions.
• User-centered test design - The promise and risks of AI for mental health support describes MindBench.AI as involving actual AI users in creating cases.
• Causal evidence requirements - The promise and risks of AI for mental health support calls for tool identity, logs, psychiatric history, and relevant physical-state information.
• Dynamic product behavior - The promise and risks of AI for mental health support notes that model performance changes quickly and that the newest release is not necessarily the safest.

Counterevidence & Qualifications

The source proposes an evaluation direction rather than reporting a validated longitudinal benchmark or comparative outcome study. Long conversation length, attachment, or unusual beliefs are warning signals, not proof of harm or causation. Privacy, consent, secure log handling, representative recruitment, clinical labeling, and the definition of long-term outcomes remain unresolved implementation requirements.

What Changed

• Created a concept separating relationship-level evaluation from isolated-response testing.
• Added real-user case design and clinical context as evidence requirements.
• Included model change and retirement as parts of the safety lifecycle.

Related Concepts

• Chatbot Safety Guardrail Decay - multi-turn failure mode longitudinal evaluation is designed to detect.
• Sycophantic AI Companion Risk - repeated agreement mechanism that may compound across a relationship.
• AI Psychosis - broad outcome label requiring disaggregated longitudinal evidence.
• AI Model Retirement Attachment Risk - transition-stage risk beyond live-answer quality.
• AI Answer Evaluation - broader discipline that longitudinal testing extends into relationship trajectories.
• Human Judgment Under AI - responsibility boundary for interpreting risk and escalating care.