让对话AI适应人类认知局限，减少偏见风险。
Bounded Minds, Generative Machines: Envisioning Conversational AI that Works with Human Heuristics and Reduces Bias Risk
- 基于有限理性设计AI，适配人类启发式思维
- 提出评估系统需关注决策质量而非仅事实正确性
- 适合研究人机交互与可信AI的学者参考
对话式AI正成为信息获取与决策的主要接口，但多数系统仍假设用户为理想化状态。现实中，人类认知受注意力有限、知识不均等制约，依赖适应性强但易产生偏见的启发式策略。本文提出应以有限理性为基础，设计能与人类启发式协同的对话AI。重点方向包括：识别认知脆弱性、支持不确定性下的判断、以及超越事实准确性的评估体系，转向决策质量与认知鲁棒性。该路径旨在提升AI在真实人类使用场景中的可靠性与公平性。
原文摘要 · Abstract (English)
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.
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