有界心智，生成机器：设想一种与人类启发式方法协同工作并减少偏见风险的对话AI
Bounded Minds, Generative Machines: Envisioning Conversational AI that Works with Human Heuristics and Reduces Bias Risk
AI总结：
本文提出对话AI应与人类启发式方法协同工作，以减少偏见风险并提升决策质量。
摘要：
对话AI正迅速成为信息获取和决策的主要接口，但大多数系统仍假设理想化的用户。实际上，人类推理受限于有限的注意力、不均的知识和依赖于适应性强但易偏见的启发式方法。本文概述了基于有界理性性的研究路径，并论证对话AI应设计为与人类启发式方法协同工作，而非对抗。它识别了检测认知脆弱性、在不确定性下支持判断以及评估对话系统超越事实准确性，向决策质量和认知鲁棒性方向发展的关键方向。
英文摘要：
Conversational AI is rapidly becoming a primary interface for information seeking and decision making, yet most systems still assume idealized users. In practice, human reasoning is bounded by limited attention, uneven knowledge, and reliance on heuristics that are adaptive but bias-prone. This article outlines a research pathway grounded in bounded rationality, and argues that conversational AI should be designed to work with human heuristics rather than against them. It identifies key directions for detecting cognitive vulnerability, supporting judgment under uncertainty, and evaluating conversational systems beyond factual accuracy, toward decision quality and cognitive robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
