[论文解读] FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications

Zhipeng Yin, Sribala Vidyadhari Chinta+3 more|arXiv (Cornell University)|Jul 26, 2024

Ethics and Social Impacts of AI被引用 36

一句话总结

对教育AI中的公平、偏见和伦理的全面综述，概述偏见类型、缓解策略、公平性指标和监管考量。

ABSTRACT

The integration of AI in education holds immense potential for personalizing learning experiences and transforming instructional practices. However, AI systems can inadvertently encode and amplify biases present in educational data, leading to unfair or discriminatory outcomes. As researchers have sought to understand and mitigate these biases, a growing body of work has emerged examining fairness in educational AI. These studies, though expanding rapidly, remain fragmented due to differing assumptions, methodologies, and application contexts. Moreover, existing surveys either focus on algorithmic fairness without an educational setting or emphasize educational methods while overlooking fairness. To this end, this survey provides a comprehensive systematic review of algorithmic fairness within educational AI, explicitly bridging the gap between technical fairness research and educational applications. We integrate multiple dimensions, including bias sources, fairness definitions, mitigation strategies, evaluation resources, and ethical considerations, into a harmonized, education-centered framework. In addition, we explicitly examine practical challenges such as censored or partially observed learning outcomes and the persistent difficulty in quantifying and managing the trade-off between fairness and predictive utility, enhancing the applicability of fairness frameworks to real-world educational AI systems. Finally, we outline an emerging pathway toward fair AI-driven education and by situating these technologies and practical insights within broader educational and ethical contexts, this review establishes a comprehensive foundation for advancing fairness, accountability, and inclusivity in the field of AI education.

研究动机与目标

• 识别并分类教育AI中的主要偏见形式（与数据相关、算法、用户交互相关）。
• 总结现有的教育AI偏见缓解技术和公平干预措施。
• 回顾用于评估教育AI系统的公平性概念与指标。
• 讨论塑造教育领域公平AI的伦理原则、透明性和监管框架。
• 突出在教育AI研究中常用的数据集与工具及其偏见。

提出的方法

• 在IEEE Xplore、ACM DL、PubMed、Scopus和Google Scholar的文献综述，进行系统筛选与数据提取。
• 将偏见分为数据相关、算法相关和用户交互相关类别，并给出案例研究示例。
• 呈现公平性概念（个体公平与群体公平）及相应的评估指标。
• 讨论引导教育AI的伦理框架与监管考量。

实验结果

研究问题

• RQ1 在教育AI应用中观察到的主要偏见类型有哪些？
• RQ2 存在哪些方法和指标可用于缓解偏见并评估教育AI的公平性？
• RQ3 伦理考虑和监管框架如何影响教育AI的开发和部署？
• RQ4 在教育AI系统中实现公平与准确之间存在哪些实际挑战与权衡？

主要发现

• 本综述将数据相关、算法相关和用户交互偏见视为教育AI公平性的基本障碍。
• 概述了偏见缓解技术与公平干预的预处理、处理中和后处理阶段。
• 讨论了公平性概念（个体与群体）及用于评估教育AI系统的相应指标。
• 强调在公平性与准确性之间的平衡，以及多样化数据集和协作方法的必要性。
• 强调伦理考量和监管框架对塑造教育领域公平AI使用的重要性。