Human-AI collaborative decision making has emerged as a pivotal field in recent years. Existing methods treat human and AI as different entities when designing human-AI systems. However, as the decision capabilities of AI models become closer to human beings, it is necessary to build a uniform framework for capability modeling and integrating. In this study, we propose a general architecture for human-AI collaborative decision making, wherein we employ learnable capability vectors to represent the decision-making capabilities of both human experts and AI models. These capability vectors are utilized to determine the decision weights of multiple decision makers, taking into account the contextual information of each decision task. Our proposed architecture accommodates scenarios involving multiple human-AI decision makers with varying capabilities. Furthermore, we introduce a learning-free approach to establish a baseline using global collaborative weights. Experiments on image classification and hate speech detection demonstrate that our proposed architecture significantly outperforms the current state-of-the-art methods in image classification and sentiment analysis, especially for the case with large non-expertise capability levels. Overall, our method provides an effective and robust collaborative decision-making approach that integrates diverse human/AI capabilities within a unified framework.

翻译：近年来，人机协同决策已成为关键研究领域。现有方法在设计人机系统时将人类与人工智能视为不同实体。然而，随着AI模型的决策能力日益接近人类水平，有必要建立统一的能力建模与集成框架。本研究提出一种通用的人机协同决策架构，通过可学习的能力向量表示人类专家与AI模型的决策能力。这些能力向量结合各决策任务的上下文信息，用于确定多位决策者的决策权重。该架构适用于包含不同能力水平的多位人机决策者的场景。此外，我们引入一种免学习方法，通过全局协作权重建立基线模型。在图像分类和仇恨言论检测任务上的实验表明，所提架构在图像分类与情感分析任务中显著优于当前最优方法，尤其在非专业能力水平较高的场景下表现突出。总体而言，本方法提供了一种有效且鲁棒的协同决策途径，将多样化的人机能力整合于统一框架之中。