Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database
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Summary:
Using the 2010, 2015, and 2020/2021 datasets of the IMF’s Central Bank Legislation Database (CBLD), we explore artificial intelligence (AI) and machine learning (ML) approaches to analyzing patterns in central bank legislation. Our findings highlight that: (i) a simple Naïve Bayes algorithm can link CBLD search categories with a significant and increasing level of accuracy to specific articles and phrases in articles in laws (i.e., predict search classification); (ii) specific patterns or themes emerge across central bank legislation (most notably, on central bank governance, central bank policy and operations, and central bank stakeholders and transparency); and (iii) other AI/ML approaches yield interesting results, meriting further research.
Series:
Working Paper No. 2023/241
Subject:
Artificial intelligence Central bank autonomy Central bank governance Central bank legislation Central bank transparency Central Banks Technology
Frequency:
regular
English
Publication Date:
November 17, 2023
ISBN/ISSN:
9798400260636/1018-5941
Stock No:
WPIEA2023241
Format:
Paper
Pages:
33
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