Abstract
In many real deployments, the failure is not that the model is reckless. The failure is that the model becomes too cautious to be useful. As alignment layers, guardrails, and policy filters stack up over time, capable systems begin to hedge, refuse, or dilute responses in situations that are objectively low risk. Productivity drops. Users work around the system. Trust erodes quietly. This paper defines that pattern as the AI Handicap Effect. The work introduces a practical detection approach that identifies when safety mechanisms begin to suppress legitimate capability beyond acceptable thresholds. Instead of treating refusals or hedging as automatically desirable, the framework evaluates proportionality between risk context and model restriction behavior. The result is a bounded Handicap Risk Score that surfaces when a system is operating below its safe capability envelope. The design is model-agnostic and deployable in enterprise copilots, support automation, and knowledge assistants. Field-style scenarios show the signal aligns with cases where human teams report “the AI is being overly careful.” As organizations push hard on alignment, the next operational problem will be overcorrection. This paper provides a way to see it early and manage it deliberately.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "The AI Handicap Effect: When Systems Underperform to Appear Safe", Technical Disclosure Commons, (February 25, 2026)
https://www.tdcommons.org/dpubs_series/9394