提出人机认知协作新理论，提升决策系统可解释性与可信度
Explainable and Human-Grounded AI for Decision Support Systems: The Theory of Epistemic Quasi-Partnerships
- 构建基于理由、反事实和置信度的RCC解释框架
- 实证表明现有解释方法难以有效提升用户信任与准确率
- 新理论支持伦理设计，适合医疗、金融等高风险决策场景
在人工智能决策支持系统（AI-DSS）中，为满足伦理与可解释性要求，本文提出需向人类决策者提供三类以人为本的解释：理由、反事实和置信度，称为RCC方法。通过回顾现有实证XAI研究，分析LIME、SHAP、Anchors等解释方法对模型可信度及用户准确率的影响，发现当前理论无法充分解释这些证据或给出合理的开发伦理建议。为此，本文提出新的交互理论——认知准伙伴关系理论（Epistemic Quasi-Partnerships, EQP）。EQP不仅能解释已有实证结果，提供符合伦理的开发指导，且自然导出RCC方法的必要性。
原文摘要 · Abstract (English)
In the context of AI decision support systems (AI-DSS), we argue that meeting the demands of ethical and explainable AI (XAI) is about developing AI-DSS to provide human decision-makers with three types of human-grounded explanations: reasons, counterfactuals, and confidence, an approach we refer to as the RCC approach. We begin by reviewing current empirical XAI literature that investigates the relationship between various methods for generating model explanations (e.g., LIME, SHAP, Anchors), the perceived trustworthiness of the model, and end-user accuracy. We demonstrate how current theories about what constitutes good human-grounded reasons either do not adequately explain this evidence or do not offer sound ethical advice for development. Thus, we offer a novel theory of human-machine interaction: the theory of epistemic quasi-partnerships (EQP). Finally, we motivate adopting EQP and demonstrate how it explains the empirical evidence, offers sound ethical advice, and entails adopting the RCC approach.
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