Правовая квалификация модели машинного обучения на основе технического анализа этого процесса

Abstract

Machine learning technologies are at the heart of artificial intelligence applications, ranging from the use in the military to household assistants. The result of machine learning as a technical process is the creation of models that are able to produce a forecast with some degree of accuracy, on the basis of which people make decisions, and artificial intelligence systems perform actions. The machine learning model is a new phenomenon with an indefinite legal regime. However, regarding the creation and use of models contracts are concluded and economic benefits are created during machine learning: all this requires legal qualification. Since inductive development methods are used to create models, machine learning has an atypical structure compared to a conventional computer programme, which makes it difficult to determine the legal regime of the model and the legal consequences of its creation and operational application. The Article presents the piecemeal legal structuring of Machine Learning as a process and a Model as its result, defines the legal nature of the Model at all stages of its life cycle. In this Article the legal significance of Machine Learning Methods for the legal qualification is analyzed, the nature of Training Data external to the model is justified, such a benefit derived from Training Data as the Parameters (Weight Coefficients) of the Model is indicated. The definition of the legal nature of the Machine Learning Model and its elements are the basis for further legal analysis of the legal relations developing regarding the creation and use of Artificial Intelligence Applications.