概括
机器学习 (ML) 在公共管理中的风险是削弱民主决策. 算法工具可以减少这种情况的发生.
科学领域:
- 公共管理 公共管理
- 政治科学 政治科学是指政治学.
- 法律与技术 法律与技术
背景情况:
- 机器学习 (ML) 在公共管理中的整合引发了关于政治和法律影响的关键问题.
- 数字和人工智能技术正在改变民主进程和"公众"的概念.
- 现有的框架可能无法充分解决治理中的算法决策所带来的挑战.
研究的目的:
- 探索ML对行政决策"公开性"的影响.
- 开发一个理论框架,以理解公共管理作为实践社区.
- 倡导对有关算法损害的行政法进行重新评估.
主要方法:
- 在公共管理中对"公众性"的概念分析.
- 检查ML模型在公共决策过程中的作用.
- 基于行政社区内的对话性,批判性和协同作用的理论发展.
主要成果:
- 公共决策中的ML模型导致了"贫困的公众性".
- 机器学习的使用削弱了公共行政部门作为民主建设场所的功能.
- 当前的行政法可能无法充分解决算法治理的不利因素.
结论:
- 为了维护民主价值观,需要对公共管理中的ML进行批判性重新考虑.
- 公共管理的"公众性"受到无批判地采用机器学习技术的威胁.
- 行政法必须不断发展,以减轻算法决策的负面影响.
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