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相关概念视频

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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规范性不确定性和社会偏好:评估标准的问题

Sietze Kai Kuilman1, Koji Andriamahery2, Catholijn M Jonker1

  • 1Intelligent Systems Department, Faculty of Electrical Engineering, Mathematics & Computer Science, Delft University of Technology, Delft, Netherlands.

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概括

博尔达投票可以通过聚合偏好来帮助人工智能 (AI) 系统与人类价值观保持一致. 然而,成功实施人工智能道德需要仔细考虑道德原则的制定和不确定性管理.

关键词:
伦理学 伦理 伦理学形式的限制形式的限制.这是一个道德机器.规范性不确定性 规范性不确定性喜欢的个人资料,偏好的个人资料.自动驾驶汽车可以自动驾驶.

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科学领域:

  • 人工智能道德和治理
  • 计算式的社会选择
  • 伦理哲学的道德哲学

背景情况:

  • 技术系统的自主性越来越大,需要将伦理与人类价值观保持一致.
  • 人工智能系统带来重大风险,需要强大的控制机制,超越传统的错误处理.
  • 社会价值多元主义使对机器的普遍伦理原则的定义变得复杂.

研究的目的:

  • 研究Borda投票作为一种在AI伦理实施中最大限度地提高预期选择价值的方法.
  • 使用从道德机器实验的经验数据来评估博尔达投票的有效性.
  • 分析将集体选择机制应用于人工智能伦理方面的挑战和局限性.

主要方法:

  • 考察了博尔达投票作为聚合多种伦理偏好的机制.
  • 利用道德机器实验的数据来模拟和评估投票系统的性能.
  • 分析了不同道德原则制定对选择价值最大化的影响.

主要成果:

  • 博尔达的投票表明,在实现大多数人喜欢的结果方面,平均有效率.
  • 实现最大化预期的选择价值的成功非常敏感于道德原则的精确制定.
  • 在准确制定信誉和管理这些系统中的不确定性方面仍然存在重大挑战.

结论:

  • 博尔达投票为人工智能系统提供了一个潜在的框架,以坚持集体道德信念.
  • 由于依赖原则制定和最大限度地提高选择价值的固有困难,实施需要谨慎.
  • 在广泛采用之前,需要进一步的研究来解决道德AI治理的根本挑战.