论文标题
专家对人工智能教育中公平性的挑战和需求的看法
Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education
论文作者
论文摘要
近年来，人们对人工智能（AI）如何支持智能教育应用的科学和工程进行了令人兴奋的讨论。该领域的许多研究都提出了可操作的数据挖掘管道和由学习相关数据驱动的机器学习模型。但是，这些管道和模型扩大某些类别学生不公平的潜力是受到越来越多的关注。如果AI应用程序要对教育产生积极影响，那么他们的设计在每个步骤都认为公平至关重要。通过对去年在顶级教育会议上发表研究的专家（研究人员和从业人员）的匿名调查和访谈，我们对整个基于AI的教育系统的发展进行了公平性的挑战和需求进行了首次针对专家驱动的系统调查。我们确定了对实践中教育技术专家所面临的挑战和需求的共同和不同的看法，这使社区对引发该主题的疑问的主要问题有清晰的了解。基于这些发现，我们强调了方向，这些方向将促进正在进行的对教育更公平的AI的研究。
In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can support the science and engineering of intelligent educational applications. Many studies in the field are proposing actionable data mining pipelines and machine-learning models driven by learning-related data. The potential of these pipelines and models to amplify unfairness for certain categories of students is however receiving increasing attention. If AI applications are to have a positive impact on education, it is crucial that their design considers fairness at every step. Through anonymous surveys and interviews with experts (researchers and practitioners) who have published their research at top-tier educational conferences in the last year, we conducted the first expert-driven systematic investigation on the challenges and needs for addressing fairness throughout the development of educational systems based on AI. We identified common and diverging views about the challenges and the needs faced by educational technologies experts in practice, that lead the community to have a clear understanding on the main questions raising doubts in this topic. Based on these findings, we highlighted directions that will facilitate the ongoing research towards fairer AI for education.
