梅勒的数字紫:神经网络的开发者责任
Anders Søgaard1, Filippos Stamatiou2
1Department of Computer Science, University of Copenhagen, København, Denmark.
Science and engineering ethics
|November 26, 2025
概括
开发人员不仅仅负责神经网络的预测,因为无法区分可预见的和不可预见的结果是不可能的. 这挑战了对人工智能和人类责任的传统观念,表明没有特定技术的差距存在.
科学领域:
- 技术的哲学技术的哲学
- 人工智能伦理学 人工智能伦理学
- 法律哲学 法律哲学
背景情况:
- 人工智能 (AI) 的发展,特别是神经网络,提出了关于开发人员对AI产生的结果的问责制的问题.
- 关于人工智能引入的潜在"责任差距"存在哲学辩论,开发人员可能会逃避对不可预测的人工智能行为负责.
研究的目的:
- 分析神经网络是否为其开发者创造了独特的责任差距.
- 调查区分可预见和不可预见的AI预测对责任分配的影响.
- 根据人工智能能力和人类的错误性,重新审视责任的经典概念.
主要方法:
- 在神经网络预测的背景下对责任分配的概念分析.
- 检查关于神经网络输出的可预测性的经验事实.
- 重新审视和重新解释已建立的哲学和法律案例 (例如,Mele的Zygote,Palsgraf) 以与人工智能的责任进行平行.
主要成果:
- 经验证据表明,不可能可靠地区分可预见和不可预见的神经网络预测.
- 这种无法区分的情况带来了两难局面:开发商必须承担全部责任或没有责任,可能会造成差距.
- 然而,预测中的同样的经验挑战也适用于人类行为,这表明问题在于经典的责任概念,而不是人工智能本身.
结论:
- 人工智能开发人员没有独特的"技术诱导"责任差距.
- 赋予神经网络的责任的复杂性反映了赋予人类行为的责任的复杂性.
- 这项研究表明,需要修订传统的责任观念,以解释复杂系统中固有的不可预测性,无论是人工还是人为.
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