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：人工智能系统的效率取决于其与特定任务要求的匹配能力。然而，任务固有的复杂性常常引入有害后果或不良行为的可能性。本注解探讨了AI系统中"能力"这一关键概念，即系统预期交付的内容。能力的阐述涉及明确界定可量化的成果。然而，这种能力的实现可能受到实施和测试缺陷的阻碍，这反映了系统"胜任度"方面的差距（即系统能做什么与成功做什么之间的差异）。一个核心挑战在于阐明AI系统有效执行任务的胜任度。对AI系统胜任度的探索仍处于早期阶段，偶尔以表示成功概率的置信区间形式呈现。对AI系统的信任取决于对其胜任度的显式建模和详细说明，这与系统的能力紧密相连。本注解通过提出一个阐明AI系统胜任度的框架来探索这一差距。受"玻璃门问题"等实际场景的启发——在此问题中，个体因胜任度不足而意外撞上玻璃障碍物——本研究强调了深入探究胜任度动态的必要性。通过在细节层面弥合能力与胜任度之间的鸿沟，本注解为推进增强AI系统在真实世界应用中的可靠性的讨论做出了贡献。