生成式AI中的对齐短视
Alignment Myopia in Generative AI
Remi Denton, Aida Davani, Cynthia L Bennett, Michael Madaio, Roma Patel, Himabindu Lakkaraju
AI 速览：作者指出当前生成式AI对齐依赖短视范式、只优化即时对话满意度，忽视长期人机交互的外部性，并倡议转向长时程对齐。
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.