Addressing the Challenges of Government Service Provision with AI


  • Yongqing Zheng Shanda Dareway Software Pte Ltd.
  • Han Yu Nanyang Technological University
  • Lizhen Cui Shandong University
  • Chunyan Miao Nanyang Technological University
  • Cyril Leung The University of British Columbia
  • Yang Liu WeBank, China
  • Qiang Yang Hong Kong University of Science and Technology



In complete contract theory, the main approach to limit moral hazard is through modifying incentives for the agents. However, such modifications are not always feasible. One prominent example is Chinese government service provision. Over the years, it has been plagued with inefficiencies as a result of moral hazard. Previous attempts to address these challenges are not effective, as reforms on civil servant incentives face stiff hindrance. In this article, we report an alternative platform — SmartHS — to address these challenges in China without modifying incentives. Through dynamic teamwork, automation of key steps involved in service provision, and improved transparency with the help of artificial intelligence, it places civil servants into an environment that promotes efficiency and reduces the opportunities for moral hazard. Deployment tests in the field of social insurance service provision in three Chinese cities involving close to 3 million social insurance service cases per year demonstrated that the proposed approach significantly reduces moral hazard symptoms. The findings are useful for informing current policy discussions on government reform in China and have the potential to address long-standing problems in government service provision to benefit almost one-fifth of the world’s population.

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How to Cite

Zheng, Y. ., Yu, H., Cui, L. ., Miao, C., Leung, C., Liu, Y., & Yang, Q. (2020). Addressing the Challenges of Government Service Provision with AI. AI Magazine, 41(1), 33-43.



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