The efficiency of an AI system is contingent upon its ability to align with the specified requirements of a given task. How-ever, the inherent complexity of tasks often introduces the potential for harmful implications or adverse actions. This note explores the critical concept of capability within AI systems, representing what the system is expected to deliver. The articulation of capability involves specifying well-defined out-comes. Yet, the achievement of this capability may be hindered by deficiencies in implementation and testing, reflecting a gap in the system's competency (what it can do vs. what it does successfully). A central challenge arises in elucidating the competency of an AI system to execute tasks effectively. The exploration of system competency in AI remains in its early stages, occasionally manifesting as confidence intervals denoting the probability of success. Trust in an AI system hinges on the explicit modeling and detailed specification of its competency, connected intricately to the system's capability. This note explores this gap by proposing a framework for articulating the competency of AI systems. Motivated by practical scenarios such as the Glass Door problem, where an individual inadvertently encounters a glass obstacle due to a failure in their competency, this research underscores the imperative of delving into competency dynamics. Bridging the gap between capability and competency at a detailed level, this note contributes to advancing the discourse on bolstering the reliability of AI systems in real-world applications.

翻译：摘要：人工智能系统的有效性取决于其与给定任务特定要求的一致性。然而，任务固有的复杂性往往可能引发有害后果或负面行为。本文探讨了人工智能系统中"能力"（capability）这一核心概念，即系统预期应交付的成果。能力的明确表述需要定义清晰的输出结果，但实现该能力可能因实施与测试过程中的缺陷而受阻，这反映出系统"胜任力"（competency）的不足（即系统"能做什么"与"成功做到什么"之间的差距）。如何阐明人工智能系统有效执行任务的胜任力成为关键挑战。目前，有关人工智能系统胜任力的探索仍处于初期阶段，偶尔表现为表征成功概率的置信区间。对人工智能系统的信任取决于对其胜任力的显式建模与详述，这与其能力紧密关联。本文通过提出人工智能系统胜任力表述框架来探究上述差距。受"玻璃门问题"等实际场景启发（例如个体因自身胜任力不足而意外撞击玻璃障碍），本研究强调深入探究胜任力动态机制的必要性。通过从细节层面弥合能力与胜任力之间的鸿沟，本文旨在推动增强人工智能系统在现实应用中可靠性的相关讨论。