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.

AI 系统的效率取决于其与给定任务的特定要求的一致性；然而，任务的固有复杂性常常引入潜在的有害影响或不良行为。本研究探讨了 AI 系统中关键的能力概念，即系统应该提供的功能；该概念的表达包括明确定义的结果，但实现这一能力可能会受到实施和测试不足的影响，反映了系统能力与其成功执行的能力之间的差距。探索 AI 系统有效执行任务的能力存在一项核心挑战，这一挑战尚处于初级阶段，有时表现为表示成功概率的置信区间。对于 AI 系统的能力的明确建模和详细规范是建立对其的信任的关键，它与系统的能力密切相关。本研究提出了一个框架，来说明 AI 系统的能力。受到类似 “Glass Door problem” 这样的实际场景的启发，其中一个人由于能力上的失败而无意间遇到玻璃障碍物，本研究强调了深入研究能力动态的重要性。通过详细层面上弥合能力与功能之间的差距，本研究促进了增强 AI 系统在现实应用中可靠性的讨论。