生成式AI输出波动性低于真实世界，可能引发社会问题。
Variance reduction in output from generative AI
- 发现生成式AI存在'均值回归'现象，输出方差趋于缩小。
- 指出输出波动性降低会影响社会、群体与个体层面的多样性。
- 建议服务提供方与用户共同干预，缓解潜在负面影响。
生成式AI模型（如ChatGPT）将在诸多重要任务中逐步替代人类生成内容。尽管以往研究多聚焦于模型平均性能优于人类，却较少关注生成内容方差显著低于真实世界分布的问题。本文指出，生成式AI固有地呈现‘均值回归’现象，导致输出方差收缩。我们从社会、群体与个体三个层面，以及物质与非物质两个维度，探讨该现象的潜在影响，并讨论缓解负面效应的干预措施，强调服务提供方与用户在应对这一挑战中的协同作用。本文旨在提升对生成式AI输出方差重要性的认识，推动多方合作应对该问题。
原文摘要 · Abstract (English)
Generative AI models, such as ChatGPT, will increasingly replace humans in producing output for a variety of important tasks. While much prior work has mostly focused on the improvement in the average performance of generative AI models relative to humans' performance, much less attention has been paid to the significant reduction of variance in output produced by generative AI models. In this Perspective, we demonstrate that generative AI models are inherently prone to the phenomenon of "regression toward the mean" whereby variance in output tends to shrink relative to that in real-world distributions. We discuss potential social implications of this phenomenon across three levels-societal, group, and individual-and two dimensions-material and non-material. Finally, we discuss interventions to mitigate negative effects, considering the roles of both service providers and users. Overall, this Perspective aims to raise awareness of the importance of output variance in generative AI and to foster collaborative efforts to meet the challenges posed by the reduction of variance in output generated by AI models.
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