Abstract. The aim of the study is to study the impact of data quality, the factors associated with it, and to formalize the logical and mathematical connections between the arguments of the neural network training function or artificial intelligence to demonstrate the relationship with flight safety.
To achieve the goal, it is necessary to solve the following tasks. The first is to assess the current state of the issue of the use of artificial intelligence in the procedures of pre-flight inspection, as well as repair and maintenance of the aircraft. This is necessary to determine the level of technological integration of neural networks and artificial intelligence. Secondly, based on the data obtained, it is necessary to determine relevant platforms for training artificial intelligence. This will allow you to identify additional technical arguments that affect the final result of AI training. The third is to formalize the logical and mathematical connection between the influencing factors and the final result. Identify additional influencing factors and also formalize. The formal recording method allows for the construction of a procedurally consistent communication line to monitor safety risks.
The following methods were used to solve the problems. An observation method that has been applied to the information that has been collected in the course of tracking the history of the application of various automated optical fault detection technologies. Decomposition, which made it possible to separate the functional part of the computer program that detects malfunctions from the complex technology. Comparative analysis, which made it possible to determine the strengths and weaknesses of various neural networks and the architectures of technical systems for these neural networks and artificial intelligence Mathematical analysis, which allows you to formalize the expressions that characterize the influence of the arguments of a complex function and determine the additivity and multiplication of a complex function. Sabotage and functional analyses, which make it possible to determine the relationship between the arguments of complex functions and the final complex safety function.
As a result, expressions were presented that reflect the logical and mathematical connection in a functionally sequential transfer line from the arguments of a neural network and artificial intelligence to a complex indicator of flight safety.

Keywords: artificial intelligence, neural networks, pre-flight inspection, artificial intelligence models, comparative analysis, flight safety.