In this article, we consider the system of quality management of legislation in the Russian Federation: we define its basic elements, the main tools for quality control (management) in the form of legal examinations, provide a generalized algorithm for quality assessment, propose a simple general classification of controlled factors in legal examinations, and introduce the concept of a bill readiness indicator. The most important legal examination - anti-corruption (ALE) - was chosen as an example for conducting quality control of legislation. Within the framework of general trends in automation, informatization, and digitalization, we considered the use of artificial intelligence (AI) for the purposes of conducting ALE, which, in some cases of "routine work", could provide all possible assistance to specialists in the field of legal examinations and their digitalization. In this regard, a step-by-step algorithm for pre-training AI was formulated using examples from regulatory legal acts (RLA) containing corruption factors (CF); a classification of CF was carried out; a scale of AI errors in detecting CF was developed; frequency characteristics of AI errors were determined; Preliminary conclusions were obtained on the possibility of using AI in AEC.
Keywords: artificial intelligence, corruption factor, anti-corruption expertise, normative legal act, corruption factor indicator, pre-training, algorithm, errors, frequency distribution, classification, dialogue.
This article proposes a hybrid algorithm for assessing the quality of regulatory legal acts (RLA). This algorithm uses the methods of fuzzy mathematics, processes with their help the results of the questionnaire survey of the expert group. The result of the algorithm is the quality characteristic of the NLA in the form of a fuzzy term of a linguistic variable. As an option for automating this algorithm, a specialized automated system is proposed.
Keywords: regulatory legal acts, quality assessment, fuzzy algorithms, automated systems, linguistic variables