The possibilities of increasing confidence in the conclusions and results of AI systems through the use of interpretations and explanations formed on the basis of cause-and-effect relationships are discussed. However, the task of determining the causes by the observed effects does not always have a correct solution. Some examples of semantic defects of the causes based on the need to represent the semantics of objects and phenomena analyzed by the computer AI system by syntactic means are analyzed. Using the example of identifying fraudulent loan issuance schemes, some important features of mathematical tools for identifying cause-and-effect relationships hidden in empirical data are demonstrated. A mathematical model for monitoring the correctness of performed actions (i.e. prohibition and absence of fraudulent operations) is proposed and it is shown that in such monitoring false alarms in general cannot be reduced to zero.