arXiv cs.CY· Remi Denton, Aida Davani, Cynthia L Bennett, Michael Madaio, Roma Patel, Himabindu Lakkaraju·· 11 小时前AI 评分26
生成式 AI 的“对齐短视”：从短期对话满意度转向长期人类福祉
Alignment Myopia in Generative AI
AI 导读
论文指出当前生成式 AI 对齐依赖短视范式，只优化即时对话满意度，忽视人机持续交互带来的认知萎缩、社会侵蚀与人类能动性下降等长期外部性。作者提出“长视界对齐”（long-horizon alignment），从人类福祉的四个维度梳理短期效用与长期成本之间的张力，并主张超越简单的偏好优化，转向能驾驭复杂时间尺度的对齐策略。
正文 · 原文
Abstract:Current generative AI alignment relies on a myopic paradigm that prioritizes immediate conversational satisfaction. By relying on short-horizon feedback, this approach ignores the profound, longitudinal externalities of persistent human-AI interaction, which can lead to adverse outcomes such as cognitive atrophy, social erosion, and diminished human agency. To address this, we propose a paradigm shift toward long-horizon alignment that accounts for how AI reshapes human experience over time. To this end, we map the tensions between near-term utility and long-term costs of AI use across four facets of human well-being to reveal outcomes that current reward models fail to capture. Ultimately, we advocate for moving beyond simple preference optimization toward alignment strategies that actively foster long-term human well-being by navigating these complex temporal scales.
| Subjects: | Machine Learning (stat.ML); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.06830 [stat.ML] |
| (or arXiv:2611.06830v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06830 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Remi Denton [view email]
[v1]
Wed, 4 Nov 2026 17:44:07 UTC (37 KB)
来源：arXiv cs.CY · arxiv.org