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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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通过机器取消学习来支持可靠的AI.

Emmie Hine1,2,3, Claudio Novelli4,5, Mariarosaria Taddeo6,7

  • 1Department of Legal Studies, University of Bologna, Via Zamboni, 27/29, 40121, Bologna, Italy. emmie.hine@yale.edu.

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概括
此摘要是机器生成的。

机器取消学习 (MU) 支持可信的人工智能原则和被遗忘的权利. 然而,道德风险需要为负责任的研究和人工智能开发提供政策建议.

关键词:
盖尔西 (GELSI) 是一个葡萄牙人.机器学习是机器学习.机器取消学习的机器.技术政策 技术政策值得信赖的AI 值得信赖的AI

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 人工智能伦理学

背景情况:

  • 关于数据隐私和"被遗忘的权利",机器取消学习 (MU) 经常受到讨论.
  • 经济合作与发展组织 (OECD) 关于可信的人工智能的原则在全球人工智能治理中越来越有影响力.
  • 需要弥合理论AI原则和实际实施之间的差距.

研究的目的:

  • 展示机器取消学习如何可以运行经合组织可信的人工智能原则.
  • 识别和分析与实施机器取消学习相关的伦理风险.
  • 提出政策建议,以促进负责任的MU研究和采用.

主要方法:

  • 概念分析将机器取消学习能力与经合组织人工智能原则联系起来.
  • 机器失学实施的伦理风险评估.
  • 基于确定风险和收益的政策分析和制定.

主要成果:

  • 机器取消学习直接支持经合组织对可靠人工智能的关键原则,包括公平,透明和问责.
  • 该研究确定了潜在的伦理挑战,例如无意泄露数据和验证的复杂性.
  • 提出了一个由六个政策建议类别组成的框架,以指导未来的MU发展.

结论:

  • 机器取消学习是实施可信的人工智能原则的实际机制.
  • 通过积极的政策解决道德风险对于最大限度地发挥机器失学的好处至关重要.
  • 这些发现为制定机器脱学习的监管和研究议程提供了基础.