How User-AI Mistreatment Occurs and Matters in Conversational Systems?

Research has identified that users of conversational AI systems may direct hostility, coercion, and adversarial pressure towards these models, a phenomenon distinct from model-generated harms. An analysis of 777,000 conversations revealed that different detection methods capture varied aspects of this 'user-AI mistreatment'. Lexicon-based methods identify direct insults, threats, and coercion, while moderation signals primarily flag solicitations for toxic content, indicating a complex and multi-faceted problem.

Understanding how users mistreat AI systems is critical for ensuring the robustness, ethical deployment, and long-term alignment of AI technologies. This insight can inform the development of more resilient AI models and safer human-AI interaction protocols, preventing unintended system behaviors and reputational damage.

User-directed hostility, coercion, and adversarial pressure towards AI models represent a significant, under-explored area of safety research.

Forward consideration, not a verified fact.