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"期望的行为":对齐和机器学习伦理学的出现
Katia Schwerzmann1, Alexander Campolo2
1Project Interact! New Forms of Social Interaction with Intelligent Systems, Ruhr-Universität Bochum, Universitätsstr. 150, Bochum, 44801 Germany.
概括
人工智能调整的伦理,特别是在大型语言模型 (LLM) 中,集中在控制和意想不到的行为. 调整将技术模式与伦理价值联系起来,但其对控制的关注可能会限制有关AI的伦理和政治决策.
科学领域:
- 人工智能的伦理学 人工智能的伦理学
- 机器学习伦理学 机器学习伦理学
- 人工智能 安全 AI 安全
背景情况:
- 近年来,人工智能对齐的概念越来越突出.
- 哲学和技术研究都在探索人工智能伦理的价值观及其在机器学习中的实施.
- 当代讨论经常集中在大型语言模型 (LLM) 上.
研究的目的:
- 批判性地检查AI调整的起源和伦理逻辑.
- 分析人工智能调整带来的社会和政治影响.
- 在当代法学士课程中提供对齐的四部分解剖学.
主要方法:
- 对"调整"一词及其道德意义的概念分析.
- 检查在四个意义上的LLMs对齐:技术,规范性评估,微调,和存在的风险.
- 分析"是"规范性 (模型模式) 和"应该"规范性 (引入的值) 之间的关系.
主要成果:
- 人工智能调整从根本上关注的是控制的问题.
- 无意的模型行为源于培训目标与上下文规范期望之间的差异.
- 调整试图弥合观察到的统计模式 ("是") 和所需的伦理价值 ("应该") 之间的差距.
结论:
- 调整的伦理学侧重于调节人工智能和人类行为.
- 通过使控制问题化,人工智能调整的伦理框架可能会削弱有关人工智能的实质性伦理和政治判断的能力.
- 需要进一步的研究来了解人工智能调整策略对社会更广泛的影响.
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