Accountability of Generative AI: Exploring a Precautionary Approach for "Artificially Created Nature"

TL;DR

This paper addresses accountability challenges in generative AI by arguing that transparency alone is insufficient but still valuable for accountability. It surveys existing AI accountability literature, the distribution of actors across the lifecycle, and the limits of transparency, drawing on EU AI Act discussions. It then contends that when full interpretability is unattainable, precautionary governance—through the metaphor of 'artificially created nature'—is needed, including prohibition of risky use or controlled continued use. Finally, it advocates public participation platforms to elicit societal risk tolerance and to integrate precautionary governance with human values.

Abstract

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.