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As an additional case study of the AI FORA research project, the chapter delves into the intricate relationship between AI and public policy by investigating its application within the Iranian Targeted Subsidies Plan (TSP). The research methodology encompasses experimental techniques, involving data collection, document analysis, interviews, questionnaires, and quantitative data analysis. This chapter not only underscores the remarkable potential of AI in optimizing social services but also highlights challenges such as data access, privacy concerns, and governance issues. Findings reveal that the integration of AI in the TSP has led to substantial financial savings over the past decade. While the TSP initially succeeded in reducing poverty and narrowing the wealth gap, it ultimately fell short of its primary objective due to various factors, including economic instability. AI-driven algorithms have enhanced the accuracy and fairness of household eligibility assessments. Furthermore, the study demonstrates a remarkably high level of public acceptance (87%) and trust (79%) in AI’s role within the TSP. In conclusion, the study showcases how AI has become a transformative force in data-driven policy-making within Iran’s TSP, facilitated by the IWDB.

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Social Assessment for the Targeted Subsidies Plan as a Social Service Provision in Iran: AI Application in the Targeted Subsidies Plan

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Hassan Bashiri

摘要

As an additional case study of the AI FORA research project, the chapter delves into the intricate relationship between AI and public policy by investigating its application within the Iranian Targeted Subsidies Plan (TSP). The research methodology encompasses experimental techniques, involving data collection, document analysis, interviews, questionnaires, and quantitative data analysis. This chapter not only underscores the remarkable potential of AI in optimizing social services but also highlights challenges such as data access, privacy concerns, and governance issues. Findings reveal that the integration of AI in the TSP has led to substantial financial savings over the past decade. While the TSP initially succeeded in reducing poverty and narrowing the wealth gap, it ultimately fell short of its primary objective due to various factors, including economic instability. AI-driven algorithms have enhanced the accuracy and fairness of household eligibility assessments. Furthermore, the study demonstrates a remarkably high level of public acceptance (87%) and trust (79%) in AI’s role within the TSP. In conclusion, the study showcases how AI has become a transformative force in data-driven policy-making within Iran’s TSP, facilitated by the IWDB.