Artificial Intelligence and Poverty Alleviation: A Conceptual Framework
Acceptance: 02/08/2026
Published: 21/09/2026
Abstract
Artificial intelligence (AI) is creating new opportunities to support poverty alleviation by improving productivity, expanding financial inclusion, strengthening social-protection delivery, and improving the targeting of public support. This conceptual paper examines how AI can contribute to poverty reduction in four areas: agriculture, microfinance, social-welfare targeting, and smart subsidy distribution. In agriculture, AI-enabled analysis of satellite imagery, sensor data, and other digital information can support resource-efficient farming, crop monitoring, and risk forecasting. In microfinance, machine-learning techniques and alternative data can complement conventional approaches to credit assessment and customer service. In social protection, AI and data-integration techniques can support poverty mapping, eligibility assessment, and administrative processing. AI can also contribute to more targeted subsidy systems by linking assistance to observed needs and by supporting fraud detection. At the same time, these applications face important limitations, including data gaps, digital exclusion, algorithmic bias, privacy and governance concerns, and inadequate digital infrastructure. The paper therefore presents AI as a complementary tool rather than a standalone solution to poverty. It proposes a conceptual pathway—data → AI analysis → targeted action → poverty outcomes—and highlights the need for transparent governance, human oversight, inclusive digital infrastructure, and empirical evaluation
Keynote: Artificial intelligence; poverty alleviation; financial inclusion; precision agriculture; social protection; smart subsidies; digital divide
Author Name:
Gundappa
Pages:
192-197
DOI Number:
10.5281/zenodo.22867128
Country:
India
Orcid:
Full Article
Reference
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Writer Name
Gundappa
Pages
192-197
DOI Numbers
10.5281/zenodo.22867128
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