Key takeaways
- Machine learning models excel at harnessing massive computing power to impose structure on
unstructured data, giving rise to artificial intelligence (AI) applications that have seen rapid and
widespread adoption in many fields. - The rise of AI has implications for the financial system and its stability, as well as for macroeconomic
outcomes via changes in aggregate supply (through productivity) and demand (through investment,
consumption and wages) - Central banks are directly affected by AI’s impact, both in their role as stewards of monetary and
financial stability and as users of AI tools. To address emerging challenges, they need to anticipate AI’s effects across the economy and harness AI in their own operations. - Data availability and data governance are key enabling factors for central banks’ use of AI, and both
rely on cooperation along several fronts. Central banks need to come together and foster a “community of practice” to share knowledge, data, best practices, and AI tools.
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