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<p>Machine learning (ML) has become pervasive, impacting various sectors, including education. While ML offers immense potential for personalized learning, intelligent tutoring systems, and efficient administrative tasks, it raises ethical concerns. This research article adopts an agnostic approach to examine the ethical dimensions of ML in education, focusing on issues like algorithmic bias, transparency, accountability, privacy, and the digital divide. The ethical aspects are multifaceted and require a nuanced understanding, particularly when adopting an agnostic approach that prioritizes fairness, accountability, and transparency. The study emphasizes the importance of ethical considerations in ML development and deployment within educational contexts, advocating for a balanced approach that harnesses the benefits of ML while mitigating potential risks (UNESCO, in: Artificial intelligence and education: guidance for policy-makers, UNESCO, Paris, 2021). The article concludes by emphasizing the need for ongoing dialogue, collaboration, and education to ensure that ML in education is used ethically and responsibly, fostering equitable and inclusive learning environments for all students.</p>

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Ethical aspects in machine learning: an agnostic approach

  • 期刊论文

  • 发表日期：

    2025年3月18日

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  • Tulika Rai

摘要

Machine learning (ML) has become pervasive, impacting various sectors, including education. While ML offers immense potential for personalized learning, intelligent tutoring systems, and efficient administrative tasks, it raises ethical concerns. This research article adopts an agnostic approach to examine the ethical dimensions of ML in education, focusing on issues like algorithmic bias, transparency, accountability, privacy, and the digital divide. The ethical aspects are multifaceted and require a nuanced understanding, particularly when adopting an agnostic approach that prioritizes fairness, accountability, and transparency. The study emphasizes the importance of ethical considerations in ML development and deployment within educational contexts, advocating for a balanced approach that harnesses the benefits of ML while mitigating potential risks (UNESCO, in: Artificial intelligence and education: guidance for policy-makers, UNESCO, Paris, 2021). The article concludes by emphasizing the need for ongoing dialogue, collaboration, and education to ensure that ML in education is used ethically and responsibly, fostering equitable and inclusive learning environments for all students.