大数据的快速积累赋予了算法像人一样的智慧（认知能力）和人性（情感能力）。如今面对复杂的预测和决策任务，以算法为核心的人工智能产品能以专家级，甚至超越专家级的准确性为顾客提供服务。然而，现实生活中人们时常倾向于依靠人类而不是算法的判断力。研究者将这种倾向称之为“算法厌恶”，并证实了这一现象的普遍存在。但是，引起算法厌恶的根源是什么？如何通过解决算法厌恶来提升顾客对人工智能产品的接受度？现有研究还未揭示这一现象背后的普遍规律。我们希望通过构建“顾客对人工智能产品算法厌恶的形成机理”和“顾客对人工智能产品算法厌恶的应对策略”来回答上述问题，从而为人工智能产品的设计和推广人员提供解决上述问题的理论支撑和应对策略。

The rapid accumulation of big data has endowed the algorithms with human-like intelligence (cognitive ability) and humanity (emotional ability). Nowadays, in the face of complex prediction and decision-making tasks, algorithm-centric artificial intelligence products can provide services to customers with expert-level or even beyond expert-level accuracy. However, people often tend to rely on the judgment of humans rather than algorithms. Researchers call this tendency “algorithm aversion” and have confirmed the widespread existence of this phenomenon. But what is the source of algorithm aversion? How to enhance customers’ acceptance of artificial intelligence products by decreasing algorithm aversion? Existing research has not revealed the universal law behind this phenomenon. We hope to answer these questions by constructing “the mechanism model of customers’ algorithm aversion to artificial intelligence products” and “the coping strategy model of customers’ algorithm aversion to artificial intelligence products”. This research can provide the designers and marketers of artificial intelligence products with theoretical support and strategies to solve the above questions.