提出新方法提升人机协作决策中的信息互补性。
Explaining and Improving Information Complementarities in Multi-Agent Decision-making
- 基于决策理论定义互补信息，识别协作潜力
- 新解释技术ILIV-SHAP使错误率下降优于传统SHAP
- 适用于医疗影像与深度伪造检测等场景
多智能体协作决策常期望通过信息互补实现整体性能超越个体。然而，如何优化协作依赖于理解各智能体所用信息与策略。本文聚焦人机配对，提出一种决策论框架以刻画信息价值，定义互补信息并识别其利用机会。我们开发了新型解释技术ILIV-SHAP，将SHAP解释适配为突出人类可补足的信息。在胸部X光诊断与深度伪造检测案例中验证框架有效性，结果表明：与非AI辅助决策相比，使用ILIV-SHAP的AI预测能更可靠地降低错误率，优于普通SHAP解释。
原文摘要 · Abstract (English)
Multiple agents are increasingly combined to make decisions with the expectation of achieving complementary performance, where the decisions they make together outperform those made individually. However, knowing how to improve the performance of collaborating agents requires knowing what information and strategies each agent employs. With a focus on human-AI pairings, we contribute a decision-theoretic framework for characterizing the value of information. By defining complementary information, our approach identifies opportunities for agents to better exploit available information in AI-assisted decision workflows. We present a novel explanation technique (ILIV-SHAP) that adapts SHAP explanations to highlight human-complementing information. We validate the effectiveness of our framework and ILIV-SHAP through a study of human-AI decision-making, and demonstrate the framework on examples from chest X-ray diagnosis and deepfake detection. We find that presenting ILIV-SHAP with AI predictions leads to reliably greater reductions in error over non-AI assisted decisions more than vanilla SHAP.
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