FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications

• URL: http://arxiv.org/abs/2407.18745v1
• Date: Fri, 26 Jul 2024 13:59:20 GMT
• Title: FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications
• Authors: Sribala Vidyadhari Chinta, Zichong Wang, Zhipeng Yin, Nhat Hoang, Matthew Gonzalez, Tai Le Quy, Wenbin Zhang,
• Abstract summary: The integration of Artificial Intelligence into education has transformative potential, providing tailored learning experiences and creative instructional approaches. However, the inherent biases in AI algorithms hinder this improvement by unintentionally perpetuating prejudice against specific demographics. This survey delves deeply into the developing topic of algorithmic fairness in educational contexts. It identifies the common forms of biases, such as data-related, algorithmic, and user-interaction, that fundamentally undermine the accomplishment of fairness in AI teaching aids.
• Score: 2.612585751318055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of Artificial Intelligence (AI) into education has transformative potential, providing tailored learning experiences and creative instructional approaches. However, the inherent biases in AI algorithms hinder this improvement by unintentionally perpetuating prejudice against specific demographics, especially in human-centered applications like education. This survey delves deeply into the developing topic of algorithmic fairness in educational contexts, providing a comprehensive evaluation of the diverse literature on fairness, bias, and ethics in AI-driven educational applications. It identifies the common forms of biases, such as data-related, algorithmic, and user-interaction, that fundamentally undermine the accomplishment of fairness in AI teaching aids. By outlining existing techniques for mitigating these biases, ranging from varied data gathering to algorithmic fairness interventions, the survey emphasizes the critical role of ethical considerations and legal frameworks in shaping a more equitable educational environment. Furthermore, it guides readers through the complexities of fairness measurements, methods, and datasets, shedding light on the way to bias reduction. Despite these gains, this survey highlights long-standing issues, such as achieving a balance between fairness and accuracy, as well as the need for diverse datasets. Overcoming these challenges and ensuring the ethical and fair use of AI's promise in education call for a collaborative, interdisciplinary approach.

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