Evaluations with accurate ground-truth labels (AGTLs) have been widely employed to assess predictive models for artificial intelligence applications. However, in some specific fields, such as medical histopathology whole slide image analysis, it is quite usual the situation that AGTLs are difficult to be precisely defined or even do not exist. To alleviate this situation, we propose logical assessment formula (LAF) and reveal its principles for evaluations with inaccurate ground-truth labels (IAGTLs) via logical reasoning under uncertainty. From the revealed principles of LAF, we summarize the practicability of LAF: (1) LAF can be applied for evaluations with IAGTLs on a more difficult task, able to act like usual strategies for evaluations with AGTLs reasonably; (2) LAF can be applied for evaluations with IAGTLs from the logical perspective on an easier task, unable to act like usual strategies for evaluations with AGTLs confidently.

使用准确的地面实况标签（AGTL）进行的评估已被广泛用于评估人工智能应用的预测模型。然而，在一些特定领域，例如医学组织病理学全切片图像分析，AGTL很难被精确定义甚至不存在的情况是很常见的。为了缓解这种情况，我们提出了逻辑评估公式（LAF），并通过不确定性下的逻辑推理揭示了其对不准确的真实标签（IAGTL）进行评估的原理。从LAF揭示的原理中，我们总结了LAF的实用性：（1）LAF可以应用于IAGTLs对更困难任务的评估，能够像通常的AGTLs评估策略一样合理地进行评估；(2) LAF 可以从逻辑角度应用于 IAGTL 评估，任务更容易，无法像通常的 AGTL 评估策略那样自信地进行评估。