HCCD-DS v2.0: A Transparent Synthetic Benchmark Dataset for Human–AI Decision Support Under Contextual Uncertainty

HCCD-DS v2.0 is a transparent and configurable synthetic benchmark dataset for studying human–AI decision support under contextual uncertainty. This release extends the original HCCD-DS benchmark by incorporating continuous user expertise, sequential trust adaptation, individual risk tolerance, explanation-sensitive decision behavior, and domain-specific profiles for healthcare, cybersecurity, and Internet of Things (IoT) scenarios. The dataset contains 15,000 synthetic decision instances split into training and test subsets. Each instance links contextual risk, data completeness, system confidence, explanation metadata, simulated human accept/override behavior, override rationale, and final decision outcome. The revised generation framework is designed to support reproducible research on trust calibration, explanation-aware interaction policies, human intervention timing, and learn-to-defer algorithms. This v2.0 release includes the regenerated dataset files, data generation scripts, analysis scripts, machine learning benchmark code, updated figures, benchmark results, and a changelog documenting the changes from the original release. The benchmark is fully synthetic and should not be interpreted as a replacement for real human–AI interaction logs. Instead, it is intended as a controlled, transparent, and reusable testbed for evaluating interaction-aware decision support methods under explicitly stated behavioral assumptions.HCCD-DS v2.0是一款透明可配置的合成基准数据集，用于研究上下文不确定性场景下的人机决策支持。本次版本在原始HCCD-DS基准数据集的基础上进行了扩展，新增了持续用户专业水平、序贯信任自适应、个体风险容忍度、对解释敏感的决策行为，以及面向医疗健康、网络安全与物联网（Internet of Things, IoT）场景的领域专属画像。 该数据集共包含15000条合成决策样本，划分为训练子集与测试子集。每条样本均关联上下文风险、数据完备性、系统置信度、解释元数据、模拟人类接受/否决行为、否决动因与最终决策结果。经修订的生成框架旨在支持可复现研究，研究方向涵盖信任校准、感知解释的交互策略、人类干预时机与延迟决策算法。 本次v2.0版本包含重新生成的数据集文件、数据生成脚本、分析脚本、机器学习基准代码、更新后的图表、基准测试结果，以及记录原始版本变更内容的变更日志。该基准数据集完全由合成生成，不得被视为真实人机交互日志的替代品。相反，其定位为一个可控、透明且可复用的测试平台，用于在明确指定的行为假设下，评估感知交互的决策支持方法。