人工智能嵌入社会治理的算法偏见风险与规制路径探析

Algorithmic Bias Risks and Regulatory Pathways in the Integration of Artificial Intelligence into Social Governance

摘要: 人工智能嵌入社会治理的作用日益凸显，但其伴随的算法偏见问题对社会公平公正构成了挑战。算法偏见主要源于算法模型设计中融入的个人主观偏见、算法训练数据里内刻的社会隐性偏见以及算法实际使用后产生的机器学习偏见，可能引发法律与伦理的争议、侵犯大众的隐私与权利乃至损害政府形象与公信力。需要通过技术管控使算法数据透明化，通过制度规制使算法程序公正化，通过可问责的算法结果进行责任约束，并通过可升级的算法架构对算法整体进行迭代优化。在此基础上，持续推进全面深化改革，着力完善科技伦理治理体系，提升治理能力现代化水平，为实现治理现代化提供坚实的技术治理保障。

Abstract: The role of artificial intelligence (AI) embedded in social governance is increasingly prominent, yet the accompanying problem of algorithmic bias poses a challenge to social fairness and justice. Algorithmic bias mainly stems from personal subjective biases incorporated into algorithmic model design, implicit social biases embedded in algorithmic training data, and machine learning biases generated after the actual application of algorithms. These biases may trigger legal and ethical controversies, infringe upon public privacy and rights, and even undermine the image and credibility of the government. Therefore, it is necessary to achieve algorithmic data transparency through technical governance, ensure procedural fairness of algorithms through institutional regulation, enforce accountability through accountable algorithmic outcomes, and achieve iterative optimization of the overall algorithm through upgradeable algorithmic architectures. On this basis, we should continue to advance comprehensive deepening reform, focus on improving the science and technology ethics governance system, and elevate the modernization level of governance capacity, thereby providing solid technical governance safeguards for realizing governance modernization.