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Computers > Intelligence (AI) & Semantics

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Cover IMF Working Papers
Predicting the Law: Artificial Intelligence Findings from the IMF’s Central Bank Legislation Database
Khaled AlAjmi, Jose Deodoro, Mr. Ashraf Khan, and Kei Moriya
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.
Keywords:
central bank legislation; central banking; artificial intelligence; machine learning; Bayesian algorithm; Boolean algorithm; central bank governance; law and economics; ML approach; IMF central bank transparency code; IMF Central Bank Legislation Database; capital markets Department; approaches to analyzing pattern; CBLD data flow; Central bank autonomy; Central bank transparency; Global
Cover IMF Working Papers
Labor Market Exposure to AI: Cross-country Differences and Distributional Implications
Carlo Pizzinelli, Augustus J Panton, Ms. Marina Mendes Tavares, Mauro Cazzaniga, and Longji Li
This paper examines the impact of Artificial Intelligence (AI) on labor markets in both Advanced Economies (AEs) and Emerging Markets (EMs). We propose an extension to a standard measure of AI exposure, accounting for AI's potential as either a complement or a substitute for labor, where complementarity reflects lower risks of job displacement. We analyze worker-level microdata from 2 AEs (US and UK) and 4 EMs (Brazil, Colombia, India, and South Africa), revealing substantial variations in unadjusted AI exposure across countries. AEs face higher exposure than EMs due to a higher employment share in professional and managerial occupations. However, when accounting for potential complementarity, differences in exposure across countries are more muted. Within countries, common patterns emerge in AEs and EMs. Women and highly educated workers face greater occupational exposure to AI, at both high and low complementarity. Workers in the upper tail of the earnings distribution are more likely to be in occupations with high exposure but also high potential complementarity.
Keywords:
Artificial intelligence; Employment; Occupations; Emerging Markets; impact of artificial intelligence; exposure to AI; labor market exposure; employment share; educated worker; Labor markets
Cover IMF Working Papers
Mining the Gap: Extracting Firms’ Inflation Expectations From Earnings Calls
Silvia Albrizio, Allan Dizioli, and Pedro Vitale Simon
Using a novel approach involving natural language processing (NLP) algorithms, we construct a new cross-country index of firms' inflation expectations from earnings call transcripts. Our index has a high correlation with existing survey-based measures of firms' inflation expectations, it is robust to external validation tests and is built using a new method that outperforms other NLP algorithms. In an application of our index to United States, we uncover some facts related to firm's inflation expectations. We show that higher expected inflation translates into future inflation. Going into the firms level dimension of our index, we show departures from a rational framework in firms' inflation expectations and that firms' attention to the central enhances monetary policy effectiveness.
Keywords:
rms' inflation expectations; Firms' earnings calls transcripts; Natural Language processing; GPT3.5; Monetary policy; inflation expectation; expectation index; monetary policy effectiveness; firms attention; expectations disagreement; Inflation; Artificial intelligence; Wages; Futures; Consumer price indexes; Global
Cover FinTech Notes