Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.

中文翻译：

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人工智能中拟人化的修辞与现实

人工智能 (AI) 在历史上一直以拟人化的术语进行概念化。一些算法采用仿生设计，有意尝试实现人类大脑的某种数字同构。其他人则利用更普遍的学习策略，这些策略恰好与认知科学和社会认识论的流行理论相吻合。在这篇论文中，我挑战了神经网络算法的拟人化凭据，我认为它与人类认知的相似性被大大夸大了，并且被狭隘地解释了。我认为三种替代的监督学习方法——即套索惩罚、装袋和提升——为人类推理提供了更微妙、更有趣的类比，作为个体和社会现象。然而，尽管在讨论人工智能时很容易回到拟人化的比喻，我的结论是，这种言论充其量是误导性的，最坏的情况是彻头彻尾的危险。将算法人性化的冲动是正确概念化新兴技术带来的伦理挑战的障碍。