Maintaining Human Verification Capacity under Automation

2026-09-29

AI Summary

This study addresses the concern that automated inspection may undermine human motivation for independent verification, creating detection blind spots upon system failure. To investigate this, we propose a theoretical framework linking detection objectives to domain expertise and training pipelines, distinguishing between minimizing expected loss and sustaining verification capability. The analysis integrates longitudinal testing, standardized experiments, and empirical data from colonoscopy and aviation domains. Our findings elucidate the mechanisms underlying automation bias and quantify the effects of different inspector configurations on error detection rates. Ultimately, this work provides strategic guidance for preserving long-term verification capabilities within human-machine collaborative systems.

Abstract

Human verification depends on expertise that must be maintained before it is needed. This paper links reliance on automated checks, investment in human checking ability, and performance during an interruption. Better checking lowers the error reduction gained from an extra unit of human skill while the checker works. It can therefore reduce the incentive to preserve independent expertise, even when it lowers the best achievable expected cost of maintenance and errors. In an illustration, a more informative checker raises detection while it works from \hoPowWorkA{} to \hoPowWorkC{} percent, but the organization then keeps no routine practice and detects \hoPowOnsetC{} percent of errors when the checker first fails, against \hoPowOnsetA{} percent with a less informative checker. A detection requirement therefore concerns both current capability and its survival until new training becomes effective. Evidence from colonoscopy and aviation documents weaker unaided performance under routine automation, without isolating the mechanism. The framework connects a detection target to an explicit reserve of expertise and a training pipeline. Standard detection tests estimate each quantity and reveal automation bias and silent checker failures. The framework distinguishes the requirement from minimizing expected loss and proposes a longitudinal test.

Problem

• automation reliance
• human verification capacity
• skill degradation
• automation bias
• expertise maintenance

Innovation

• Human Verification Capacity
• Automation Reliance
• Expertise Maintenance
• Automation Bias
• Longitudinal Test