Can Artificial Intelligence Improve Gender Equality? Evidence from a Natural Experiment
中文摘要
教育领域的性别歧视阻碍了女性在诸多领域的代表性。在教师的性别构成和观念变化缓慢的情况下，我们如何营造性别中立的学习环境？人工智能 (AI) 的最新进展为实现这一目标提供了一条途径，因为工程师可以使 AI 教练保持性别中立，且不将性别相关信息作为输入。我们利用一项自然实验的数据来检验 AI 训练的有效性，在该自然实验中，此类 AI 教练在一个由男性主导的策略棋盘游戏中取代了部分人类教师。AI 的引入改善了男孩和女孩的教学成效，并缩小了既有的性别差距。问卷调查结果显示，AI 的信息优势、友好的外观和互动特性帮助学生学得更快；课堂录像则表明，AI 教练不带歧视的情绪状态可以解释性别平等状况的改善。我们展示了 AI 在类似情境中改善学习成效、促进多元、公平与包容的潜力。本文由 Elena Katok 接收，刊于“人—算法连接”专刊。资助：D. Huang 衷心感谢中国国家自然科学基金 [资助号 72503232、71988101 和 T2293771] 提供的资助；C. Lin 衷心感谢中国国家自然科学基金 [资助号 72192841] 以及中国香港特别行政区研究资助局 [项目编号 T35/710/20R] 提供的资助。补充材料：在线附录和数据文件可在 https://doi.org/10.1287/mnsc.2022.02787 获取。
Abstract
Gender discrimination in education hinders women’s representation in various fields. How can we create a gender-neutral learning environment when teachers’ gender composition and mindset are slow to change? Recent development in artificial intelligence (AI) provides a way to achieve this goal as engineers can make AI trainers gender neutral and not take gender-related information as input. We use data from a natural experiment in which such AI trainers replace some human teachers for a male-dominated strategic board game to test the effectiveness of AI training. The introduction of AI improves teaching outcomes for boys and girls and reduces the preexisting gender gap. Survey responses indicate that AI’s information advantage, friendly appearance, and interactive features helped students to learn faster, and class recordings suggest that AI trainers’ nondiscriminatory emotional status can explain the improvement in gender equality. We demonstrate AI’s potential in improving learning outcomes and promoting diversity, equity, and inclusion in analogous settings. This paper was accepted by Elena Katok, Special Issue on the Human-Algorithm Connection. Funding: D. Huang gratefully acknowledges financial support from the National Natural Science Foundation of China [Grants 72503232, 71988101, and T2293771]. C. Lin gratefully acknowledges financial support from the National Natural Science Foundation of China [Grant 72192841] and the Research Grants Council of the Hong Kong Special Administration Region, China [Project No. T35/710/20R]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02787 .