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Volume 57 Issue 10
November 2025
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    Citation: GU Xin and GUO Fenglin. The Public Governance of Algorithmic Bias: The Way of Collaborative Interactions among Government, Market, and Society[J]. Academic Monthly, 2025, 57(10): 14-24. shu

    The Public Governance of Algorithmic Bias: The Way of Collaborative Interactions among Government, Market, and Society

    • Abstract

      Algorithmic decisions driven by artificial intelligence are subject to the combined influence of multiple factors, including the inherent logic of the algorithms, human bounded rationality, and user feedback loops. This has led to issues such as insufficient representation of specific social groups, high error rates in identification, and biases in policy applicability. In response, the government, market, and social actors have all made certain efforts to address these issues, exploring into technical logic, ethical principles, and application limitations. However, the breadth, depth, and sustainability of these efforts are relatively limited. To build a public governance system that addresses algorithmic bias, it is necessary to promote collaborative interactions among the government, market, and various social stakeholders, facilitating the complementary embeddedness of bureaucratic, market and community mechanisms. The government must shift from merely relying on bureaucratic mechanisms to performing meta-governance functions. By establishing a public governance system centered on collaborative governance and shared responsibility among multiple stakeholders, and by institutionalizing the public governance of algorithmic bias, we can create new productive relationships and achieve the symbiotic development of technological transformation and social order.
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        XIN Fangkun . Agile Governance for Grassroots Cross-boundary Challenges. Academic Monthly, 2026, 58(3): 91-101.

      2. [2]

        HE Da'an ,  LI Huaizheng . Digital Adjustment Mechanism in the Application of Artificial General Intelligence. Academic Monthly, 2026, 58(7): 54-63.

      3. [3]

        SUO Liming ,  WEI He'ning . Platform Transition: The Re-Evolution of Network Governance Model. Academic Monthly, 2026, 58(7): 90-103.

      4. [4]

        XIE Yue . How State Capacity Is Generated in Practice——A Dynamic Equilibrium Model of Governance. Academic Monthly, 2026, 58(8): 106-117.

      5. [5]

        DING Huiping . Invisible Governance and the Rebuilding of Local Social Order: A Historical Sociology Analysis of Qi Biaojia's Famine Relief Action in Late Ming Dynasty. Academic Monthly, 2026, 58(3): 143-154.

      6. [6]

        XIONG Changkun ,  ZHANG Ziyuan . Multi-party Game and Informal Governance in Central-Local Fiscal Relations——Focusing on the Yinhao Reform of Guangdong Salt Tax in the Early Republic of China. Academic Monthly, 2026, 58(5): 198-210.

      7. [7]

        WEN Jun . From Technical Tokens to Social Tokens: the Ontological Construction of Token Sociology. Academic Monthly, 2026, 58(8): 145-156.

      8. [8]

        LI Ming ,  XU Haiqi ,  XU Changtuo . A Statistical Framework and Measurement Results for Public Finance Investing in People. Academic Monthly, 2026, 58(8): 91-105.

      9. [9]

        ZHOU Mujun . Between Theory and Empirical Facts: Habermas and the Sociological Debate on the Public Sphere. Academic Monthly, 2026, 58(8): 157-170.

      10. [10]

        ZHAO Lin ,  ZHANG Boyu ,  LIANG Hua . The Relationship Between Monopoly and Innovation in Information Product Markets——A Growth Network Perspective. Academic Monthly, 2026, 58(7): 64-76.

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          The Public Governance of Algorithmic Bias: The Way of Collaborative Interactions among Government, Market, and Society

          Abstract: Algorithmic decisions driven by artificial intelligence are subject to the combined influence of multiple factors, including the inherent logic of the algorithms, human bounded rationality, and user feedback loops. This has led to issues such as insufficient representation of specific social groups, high error rates in identification, and biases in policy applicability. In response, the government, market, and social actors have all made certain efforts to address these issues, exploring into technical logic, ethical principles, and application limitations. However, the breadth, depth, and sustainability of these efforts are relatively limited. To build a public governance system that addresses algorithmic bias, it is necessary to promote collaborative interactions among the government, market, and various social stakeholders, facilitating the complementary embeddedness of bureaucratic, market and community mechanisms. The government must shift from merely relying on bureaucratic mechanisms to performing meta-governance functions. By establishing a public governance system centered on collaborative governance and shared responsibility among multiple stakeholders, and by institutionalizing the public governance of algorithmic bias, we can create new productive relationships and achieve the symbiotic development of technological transformation and social order.

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