Human-AI cooperative classification (HAI-CC) approaches aim to develop hybrid intelligent systems that enhance decision-making in various high-stakes real-world scenarios by leveraging both human expertise and AI capabilities. Current HAI-CC methods primarily focus on learning-to-defer (L2D), where decisions are deferred to human experts, and learning-to-complement (L2C), where AI and human experts make predictions cooperatively. However, a notable research gap remains in effectively exploring both L2D and L2C under diverse expert knowledge to improve decision-making, particularly when constrained by the cooperation cost required to achieve a target probability for AI-only selection (i.e., coverage). In this paper, we address this research gap by proposing the Coverage-constrained Learning to Defer and Complement with Specific Experts (CL2DC) method. CL2DC makes final decisions through either AI prediction alone or by deferring to or complementing a specific expert, depending on the input data. Furthermore, we propose a coverage-constrained optimisation to control the cooperation cost, ensuring it approximates a target probability for AI-only selection. This approach enables an effective assessment of system performance within a specified budget. Also, CL2DC is designed to address scenarios where training sets contain multiple noisy-label annotations without any clean-label references. Comprehensive evaluations on both synthetic and real-world datasets demonstrate that CL2DC achieves superior performance compared to state-of-the-art HAI-CC methods.

翻译：人机协同分类方法旨在开发混合智能系统，通过结合人类专业知识与人工智能能力，提升各类高风险现实场景中的决策水平。当前的人机协同分类方法主要聚焦于学习延迟（即决策交由人类专家处理）与学习互补（即人工智能与人类专家协同进行预测）两种范式。然而，现有研究在如何基于多样化专家知识有效协同运用这两种范式以改进决策方面仍存在明显空白，尤其是在需要满足人工智能独立决策的目标概率（即覆盖度）所对应的协同成本约束下。本文提出覆盖约束的特定专家延迟与互补学习方法，以填补这一研究空白。该方法根据输入数据的特点，通过三种方式作出最终决策：完全依赖人工智能预测、延迟至特定专家处理，或与特定专家协同互补。此外，我们提出一种覆盖约束优化机制来控制协同成本，确保人工智能独立决策的概率逼近预设目标值，从而能够在指定预算范围内有效评估系统性能。该方法还能处理训练集中仅包含多噪声标签标注而缺乏洁净标签参照的场景。在合成数据集与真实数据集上的综合实验表明，本方法相较现有人机协同分类技术取得了更优越的性能。