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.

本研究解决了人工智能决策支持系统（AI-DSS）在满足伦理和可解释性要求方面的不足，提出了RCC方法，旨在为人类决策者提供理由、反事实和信心等三种以人为中心的解释。研究提出的认知准伙伴关系理论（EQP）能够有效解释当前的实证证据，提供合理的伦理建议，并促进RCC方法的采用。

可解释的以人为中心的人工智能决策支持系统：认知准伙伴关系理论