Dilemma of Human-AI Collaborative Innovation: Exploring the Boundaries of Human-AI Reciprocity and Platform Incentive Mechanisms in Open Innovation

Artificial intelligence (AI) aids many open innovation platforms in human ideation and evaluation. While current research highlights AI's innovation potential, it has paid less attention to the ethical challenges from information flow in human-AI collaboration. This flow causes a tradeoff between generating novel ideas and ensuring their accurate evaluation free from strategic manipulation. Using an evolutionary game model for open innovation’s design and evaluation stages, this study explores HAI reciprocity’s impact on interpersonal dynamics and the interplay between platform incentives and AI. Results show constraints are crucial to sustaining positive cycles of HAI reciprocity. Unconditionally cooperative AI slightly boosts group innovation but reduces individual human performance and hinders valid evaluation. In contrast, Reputation-based AI improves both human and population performance. With fixed incentives, having more AI increases system stability and improves innovation outcomes. Platforms should integrate AI into mechanism design, rather than treating it as a technical tool.人工智能（Artificial Intelligence，AI）为诸多开放创新平台的人类创意生成与评估工作提供支撑。现有研究虽着重强调了人工智能的创新潜力，却较少关注人机协作中信息流引发的伦理挑战。此类信息流会在生成新颖创意与确保评估精准不受策略性操纵之间形成权衡。本研究针对开放创新的设计与评估阶段构建演化博弈模型，探究人机互惠（Human-AI Reciprocity）对人际动态的影响，以及平台激励机制与人工智能之间的相互作用。研究结果表明，约束机制对维持人机互惠的正向循环至关重要。无条件协作型人工智能可小幅提升群体创新水平，但会降低个体人类参与者的表现，并阻碍有效评估。与之相对，基于声誉的人工智能可同时提升个体与群体的整体表现。在激励机制固定的前提下，引入更多人工智能可提升系统稳定性并优化创新产出。平台应将人工智能融入机制设计环节，而非将其视为单纯的技术工具。