  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.
