Abstract
This disclosure introduces a Confidence–Reliability Divergence Detection framework designed to identify conditions in which artificial intelligence systems express high apparent certainty while underlying predictive reliability degrades. As modern AI models increasingly expose probability scores, verbal certainty cues, or calibrated confidence metrics, human operators and downstream systems frequently treat these signals as reliable proxies for correctness. However, real-world deployments demonstrate that model confidence can become systematically misaligned with true performance under conditions such as distribution shift, adversarial prompting, long-context reasoning saturation, data sparsity, or environmental volatility. This divergence creates a subtle but high-impact failure mode in which AI outputs appear authoritative precisely when additional scrutiny is most required. The proposed system introduces a continuous monitoring architecture that evaluates confidence behavior against historical accuracy patterns, contextual uncertainty indicators, and behavioral feedback loops. By detecting emerging confidence–reliability gaps in real time, the framework enables proportionate intervention before overconfident outputs propagate into operational, financial, or safety-critical errors. The approach is model-agnostic and applicable across large language models, recommendation engines, autonomous systems, and enterprise decision platforms.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "The Confidence Trap Identifying When AI Sounds Sure but Is Actually Wrong", Technical Disclosure Commons, (February 23, 2026)
https://www.tdcommons.org/dpubs_series/9371