动态筛选信息，提升黑箱决策者判断力
Dynamic Information Sub-Selection for Decision Support
- 根据每条数据动态选关键特征和选项，精准传递信息
- 在多个任务中表现优于现有方法，提升决策效率
- 适合需要优化专家分配或增强大模型决策的场景
我们提出动态信息子选择（DISS）框架，通过为每个实例定制信息处理策略，提升黑箱决策者（如人类或实时系统）的决策性能。这类决策者常因认知偏见或资源限制无法处理全部信息，导致判断质量下降。DISS采用动态策略，选择最有效特征与选项传递给决策者。我们设计了可扩展的频数主义数据获取策略和决策者模拟技术，以提高预算效率。在偏见决策支持、专家分配优化、大语言模型决策辅助及可解释性等多场景中验证了该方法的有效性，实证结果表明其在多种应用中均显著优于现有先进方法。
原文摘要 · Abstract (English)
We introduce Dynamic Information Sub-Selection (DISS), a novel framework of AI assistance designed to enhance the performance of black-box decision-makers by tailoring their information processing on a per-instance basis. Blackbox decision-makers (e.g., humans or real-time systems) often face challenges in processing all possible information at hand (e.g., due to cognitive biases or resource constraints), which can degrade decision efficacy. DISS addresses these challenges through policies that dynamically select the most effective features and options to forward to the black-box decision-maker for prediction. We develop a scalable frequentist data acquisition strategy and a decision-maker mimicking technique for enhanced budget efficiency. We explore several impactful applications of DISS, including biased decision-maker support, expert assignment optimization, large language model decision support, and interpretability. Empirical validation of our proposed DISS methodology shows superior performance to state-of-the-art methods across various applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。