面对生成式AI难追溯的困境，提出用预防原则和公众参与来增强责任机制。
Accountability of Generative AI: Exploring a Precautionary Approach for "Artificially Created Nature"
- 以预防原则应对生成式AI不可透明带来的责任难题。
- 强调透明非问责充分条件，但能促进问责改进。
- 建议建立公民参与平台共治AI风险，适合政策制定者参考。
生成式人工智能技术的快速发展引发对社会技术系统责任归属的担忧。当前生成式AI依赖复杂机制，甚至专家也难以完全追溯输出结果的原因。本文首先回顾现有AI透明性与问责研究，认为透明性并非问责的充分条件，但有助于提升问责水平。若无法实现透明，生成式AI在隐喻意义上成为“人为创造的自然”。为此，我们建议采用预防原则来评估AI风险，并提出需要建立公民参与平台以应对生成式AI带来的潜在风险。
原文摘要 · Abstract (English)
The rapid development of generative artificial intelligence (AI) technologies raises concerns about the accountability of sociotechnical systems. Current generative AI systems rely on complex mechanisms that make it difficult for even experts to fully trace the reasons behind the outputs. This paper first examines existing research on AI transparency and accountability and argues that transparency is not a sufficient condition for accountability but can contribute to its improvement. We then discuss that if it is not possible to make generative AI transparent, generative AI technology becomes ``artificially created nature'' in a metaphorical sense, and suggest using the precautionary principle approach to consider AI risks. Finally, we propose that a platform for citizen participation is needed to address the risks of generative AI.
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