Fiscal policy can help broaden gains of AI to humanity: IMF

The International Monetary Fund (IMF) emphasized the role of fiscal policy in ensuring that the benefits of artificial intelligence (AI) are widely shared across society. In a recent paper titled "Broadening the Gains from Generative AI: The Role of Fiscal Policies," the IMF highlighted the need for governments to update social-protection and tax systems globally.

The International Monetary Fund (IMF) said Monday that fiscal policy can help broaden the gains of artificial intelligence (AI) to humanity.

"Fiscal policy has a major role to play in supporting a more equal distribution of gains and opportunities from generative AI," the IMF said in its paper titled Broadening the Gains from Generative AI: The Role of Fiscal Policies. "But this will require significant upgrades to social-protection and tax systems around the world."

The paper discusses how fiscal policies can be employed to steer the technology and its deployment in ways that serve humanity best while cushioning the negative labor market and distributional effects to broaden the gains.

The financial agency said new generative AI technologies hold immense potential for boosting productivity and improving the delivery of public services, but the sheer speed and scale of the transformation also raises concerns about job losses and greater inequality.

It advised that governments should take "an agile approach" that prepares them for highly disruptive scenarios, given the uncertainty over the future of AI.

"Lessons from past automation waves and the IMF's modeling suggest more generous unemployment insurance could cushion the negative impact of AI on workers, allowing displaced workers to find jobs that better match their skills," the IMF said in a statement.

"At the same time, sector-based training, apprenticeships and upskilling and reskilling programs could play a greater role in preparing workers for the jobs of the AI age," it added.

Comprehensive social assistance programs will be needed for workers facing long-term unemployment or reduced local labor demand due to automation or industry closures.



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