Narrow Your Choices

Refine By Topic

Refine By Series

Refine By Language

Refine By Date

—

Refine By IMF Taxonomy

Refine By JEL Taxonomy

Refine by LOC Taxonomy

Refine By THEMA Taxonomy

  • Print
  • Save

Asia and Pacific > India

You are looking at 1 - 5 of 5 items for :

  • Type: Journal Issue x
  • Intelligence (AI) & Semantics x
Clear All Modify Search
Cover IMF Working Papers
The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions
Mariarosaria Comunale
and
Andrea Manera
We review the literature on the effects of Artificial Intelligence (AI) adoption and the ongoing regulatory efforts concerning this technology. Economic research encompasses growth, employment, productivity, and income inequality effects, while regulation covers market competition, data privacy, copyright, national security, ethics concerns, and financial stability. We find that: (i) theoretical research agrees that AI will affect most occupations and transform growth, but empirical findings are inconclusive on employment and productivity effects; (ii) regulation has focused primarily on topics not explored by the academic literature; (iii) across countries, regulations differ widely in scope and approaches and face difficult trade-offs.
Keywords:
Artificial Intelligence (AI); labor market; task exposure; productivity; regulation; governance.; productivity effect; regulation of AI; AI system; AI model; AI technology; Artificial intelligence; Machine learning; Automation; Global
Cover Staff Discussion Notes
Gen-AI: Artificial Intelligence and the Future of Work
Mauro Cazzaniga
,
Florence Jaumotte
,
Longji Li
,
Giovanni Melina
,
Augustus J Panton
,
Carlo Pizzinelli
,
Emma J Rockall
, and
Marina Mendes Tavares
Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills
Keywords:
Artificial Intelligence; Labor Market; Job Displacement; Income Inequality; Advanced Economies; Emerging Market Economies; Low-Income Developing Countries; AI preparedness index; AI benefit; AI exposure; ICT employment share; AI adoption; Emerging and frontier financial markets; Income; Global; Africa
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
Overcoming Data Sparsity: A Machine Learning Approach to Track the Real-Time Impact of COVID-19 in Sub-Saharan Africa
Karim Barhoumi
,
Seung Mo Choi
,
Tara Iyer
,
Jiakun Li
,
Franck Ouattara
,
Mr. Andrew J Tiffin
, and
Jiaxiong Yao
The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics.
Keywords:
Sub-Saharan Africa; Economic Activity; GDP; Machine Learning; Nowcasting; COVID-19; machine learning approach; data sparsity; GDP statistics; crisis in Sub-Saharan Africa; learning framework; Oil prices; Real effective exchange rates; Africa; Global