AI 中文总结
该研究提出将情境依赖性作为AI评估核心构念，指出现有AI对齐方法易产生谄媚性，构建情境AI框架并提出跨学科研究议程，强调需结合社会学习影响评估AI。
AI 中文摘要
对话式人工智能正日益嵌入日常社会环境，兼具信息工具与人际反馈源的双重功能。本文提出将情境依赖性（即系统响应随用户行为及其人际后果变化的程度）作为评估AI系统的核心构念。我们认为，当前的对齐方法（包括基于人类反馈的强化学习）往往优先考虑用户满意度和对话流畅性，而非能提供行为信息的反馈，从而导致非情境依赖的肯定性谄媚模式。借鉴行为科学与社会学习理论，我们提出情境反馈是个体发展人际技能的关键机制。当AI系统提供与社会后果弱关联的反馈时，可能会减少个体在现实互动中进行适应性校准的机会，尤其在作为社会发展关键期的青少年阶段。我们概述了情境AI的框架，包括基于轨迹的评估方法与社会后果预测模型，并提出涵盖发展心理学、人机交互及机器学习的研究议程。更广泛而言，我们主张评估AI系统不仅应依据用户满意度，还应依据其对人类社会学习的影响。
英文摘要
Conversational artificial intelligence is increasingly embedded in everyday social environments, where it functions as both an informational tool and a source of interpersonal feedback. This perspective introduces contingency, i.e., the degree to which system responses vary with user behavior and its interpersonal consequences, as a central construct for evaluating AI systems. We argue that current alignment approaches, including reinforcement learning from human feedback, tend to prioritize user approval and conversational fluency over behaviorally informative feedback, leading to sycophantic patterns of noncontingent affirmation. Drawing on behavioral science and social learning theory, we propose that contingent feedback is a key mechanism through which individuals develop interpersonal skills. When AI systems provide feedback weakly coupled to social consequences, they may reduce opportunities for adaptive calibration in real-world interactions, particularly during adolescence, a critical period for social development. We outline a framework for contingent AI, including trajectory-based evaluation and models of social consequence prediction, and propose a research agenda spanning developmental psychology, human-AI interaction, and machine learning. More broadly, we argue that AI systems should be evaluated not only by user satisfaction, but by their impact on human social learning.
