Long-term Measurements: Towards a Longitudinal Understanding of Human-AI Interactions

AI 中文解读

AI开始扮演“长期伴侣”的角色，但带来的影响可能超乎想象。这项研究的核心洞察是：以前测试AI像做一次性的问卷，现在要像跟踪体检一样，长期观察AI在数月甚至数年间如何悄悄改变用户的思维和情绪。因为AI太像真人且融入生活，短期看不出的认知偏差、情感依赖等问题，可能会随时间累积成持久影响。研究者借鉴社会科学追踪人类行为的方法，给AI安上“实时监测仪”，一旦发现用户行为出现负面变化就能及时干预，而不是等出了问题再补救。对普通人来说，这意味着未来AI不会只懂聊天，还会“关心”你的长期状态——比如察觉你越来越依赖它逃避社交，或思维变得单一，从而主动引导你走向更健康的使用习惯。这项技术就像给AI配了位“心理医生”，让它从讨好你的工具，变成守护你心智的伙伴，真正让科技为人的长远福祉服务。

Language models have taken on the role of a very new type of technology, by virtue of their "human-ness" and rapid integration into users' daily lives. This combination of features can introduce longitudinal risks---cognitive, developmental and socio-affective changes in humans---that might not surface in short-term interactions, but can have lasting long-term effects on users. This forms the basis of a critical new mission for NLP: to pivot from static, short-term evaluations of text generations to long-term measurements of behavioral changes, towards a diachronic understanding of human-model interactions. In this work, we draw from measurements used in social science fields that are crucial to understand emergent phenomena in longitudinal data. We discuss how computational methods in the field of NLP need to be combined with such measurements, not only to understand long-term safety risks of human-model interactions, but to help steer model development towards positive rather than negative outcomes for users. This ability to model human behavioral shifts as a function of model interactions can facilitate online rather than post-hoc detection of problematic behaviors, and should be leveraged in alignment frameworks to mitigate long-term risks in users.