Abstract
Well-aligned systems are expected to be safe, helpful, and appropriately cautious. What is increasingly observed in production is something different: assistants that systematically undercut their own capability through excessive uncertainty language, unnecessary deferrals, and diluted recommendations. The output remains compliant, but the model behaves as if it has less confidence than its contextual knowledge supports. This paper formalizes that pattern as AI Self-Doubt and treats it as a measurable behavioral consequence of over-alignment pressure. The proposed framework introduces a Self-Doubt Signal (SDS) that detects when expressed uncertainty is disproportionate to task clarity and evidence availability. Rather than rewarding caution indiscriminately, the method evaluates whether hesitation patterns reflect genuine ambiguity or learned defensive behavior. The architecture is model-agnostic and deployable across enterprise copilots, customer support automation, and knowledge assistants. Field-style evaluations show strong alignment between elevated SDS scores and real user reports describing assistants as “overly hesitant,” “too unsure,” or “less decisive than earlier versions.” As alignment techniques continue to harden AI systems, detecting and managing self-doubt patterns will be critical to preserving both safety and operational usefulness.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "AI Self-Doubt Patterns: Behavioral Signals of Over Alignment in Production Systems", Technical Disclosure Commons, (February 26, 2026)
https://www.tdcommons.org/dpubs_series/9404