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In the current paper, dedicated to potential automation of anti-corruption expertise in legislation, we explore identification of corruption-prone factors in legal documents and their drafts. Particularly, we analyze the frequencies of the different corruption-prone factors and propose some approaches for assessing risk of their existence in a legal document. The frequency analysis considers the following parameters of the legal documents: the year of adoption, the legislation field, the level of the legal act. For the data labeling, we employed non-expert workers from our “crowd-intelligence” lab, who specialize in performing relatively elementary tasks in data annotation for machine learning. The results of our research suggest that efficiency of the anti-corruption expertise can be further enhanced based on the “weak AI” methods, i.e. through supporting decision-making of expert lawyers.

Выявление коррупционных факторов в нормативных актах методами крауд-интеллекта