Abstract
Large language models increasingly generate responses that are fluent, structured, and delivered with strong apparent confidence. While this improves usability, it introduces a subtle operational risk: users often interpret linguistic certainty as evidence strength. In practice, model confidence signals and underlying factual support are not always aligned. This disclosure introduces the concept of the AI Trust Mirage, a condition in which AI outputs appear highly reliable despite weak, missing, or poorly grounded supporting evidence. The proposed framework detects confidence–evidence mismatch by jointly analyzing linguistic certainty markers, response structure, citation grounding, and contextual support signals. A composite Mirage Risk Score (MRS) is computed to identify outputs that may induce unwarranted user trust. The approach is model-agnostic and suitable for real-time deployment in AI-assisted workflows. By surfacing high confidence but weakly supported outputs, the framework helps preserve calibrated human trust and reduces the risk of silent decision degradation in enterprise and operational environments.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "The AI Trust Mirage: Detecting When Confident Outputs Betray Reality", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/9383