喻园管理论坛2024年第83期(总第1015期)

演讲主题:Cultural Antecedents of Trust in AI: The Role of Power Distance Beliefs

主讲人:Abhishek Sheetal 香港理工大学助理教授

主持人:白芸 人力资源与组织科学系讲师

活动时间:2024年10月12日（周六）16:00-17:30

活动地点:管院大楼105室

主讲人简介：

Abhishek Sheetal利用先进的机器学习技术开创非常规解决方案和理念。他因其首篇研究论文而受到赞誉，该论文曾在《纽约时报》上发表，研究内容涉及组织和社会层面的多样性、平等和包容性挑战。希塔尔创新的混合方法将自动化机器学习与传统实验程序相结合，以解决日常问题。他的开创性工作还质疑了传统的WEIRD（西方、教育、工业化、富裕和民主）Anglo-Saxon认识论视角，为该领域提供了新的视角。在进入学术界之前，Sheetal是一位创业技术领袖，曾创办初创企业，并在多家科技公司担任重要职务。他的影响力贡献包括参与了USB和以太网等常见消费技术的核心团队，突显了他对科技行业的深远社会影响。

活动简介：

An international survey of employees, managers, and citizens found that Asian countries, such as Japan and Korea, exhibit the lowest levels of openness to AI innovations, whereas Western countries, such as the US and Australia, exhibit moderate levels of trust in AI. A notable exception to this pattern is China, an Asian country that has the highest level of trust in and openness to AI among all major economies. However, there is virtually no management research explaining these large cross-cultural differences in trust in AI. To efficiently identify cultural values underlying trust in AI, I trained a deep learning model to predict country-level trust in AI using 554 values from 153,828 individuals. The model had high predictive validity (r = 0.88 between actual and predicted trust in AI scores). According to a feature importance analysis, respect for authority (a key component of the cultural value power distance) is the top predictor of trust in AI. Four correlational and experimental studies confirmed that power distance is associated with greater trust and support for AI. This research illustrates that an abductive, data-driven approach complements the traditional deductive and inductive methods of hypothesis generation that are common in the field. This study illustrates machine learning-based quantitative grounded theory (MLQGT), a new framework that I developed that integrates machine learning and theory development within management.