Abstract
In production environments, the failure mode is rarely obvious hallucination. The more dangerous pattern is quieter: the model delivers a clean, decisive answer in situations where the underlying support is thin, conditional, or incomplete. The wording is polished. The structure is confident. Users move forward. Only later does the gap surface. That gap between how certain the system sounds and how much evidence actually exists is where trust begins to drift off course. This paper formalizes that risk as Authority Tone Risk (ATR) and introduces a practical detection layer built for real deployments. The framework measures assertiveness signals against evidence density, uncertainty markers, and contextual support strength, producing a bounded Authority Risk Score. The objective is operational, not academic: identify responses that present themselves with unjustified certainty before they influence real decisions. The design remains fully model-agnostic and lightweight enough for inline use in enterprise copilots, support automation, and analytical assistants. Early evaluations show the system consistently surfaces over-assertive responses that experienced human reviewers later flag as needing qualification. As AI moves deeper into decision loops, tone discipline will matter as much as factual correctness. ATR is built to enforce that discipline.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "The Authority Tone Risk in AI Assistants: Detecting When Confident Language Outruns Evidence", Technical Disclosure Commons, (February 25, 2026)
https://www.tdcommons.org/dpubs_series/9396