Contribution: This paper identifies four critical ethical considerations for implementing generative AI tools to provide automated feedback to students. Background: Providing rich feedback to students is essential for supporting student learning. Recent advances in generative AI, particularly with large language models (LLMs), provide the opportunity to deliver repeatable, scalable and instant automatically generated feedback to students, making abundant a previously scarce and expensive learning resource. Such an approach is feasible from a technical perspective due to these recent advances in Artificial Intelligence (AI) and Natural Language Processing (NLP); while the potential upside is a strong motivator, doing so introduces a range of potential ethical issues that must be considered as we apply these technologies. Intended Outcomes: The goal of this work is to enable the use of AI systems to automate mundane assessment and feedback tasks, without introducing a "tyranny of the majority", where the needs of minorities in the long tail are overlooked because they are difficult to automate. Application Design: This paper applies an extant ethical framework used for AI and machine learning to the specific challenge of providing automated feedback to student engineers. The task is considered from both a development and maintenance perspective, considering how automated feedback tools will evolve and be used over time. Findings: This paper identifies four key ethical considerations for the implementation of automated feedback for students: Participation, Development, Impact on Learning and Evolution over Time.

翻译：贡献：本文提出了在实施生成式人工智能工具为学生提供自动化反馈时需考虑的四个关键伦理考量。背景：为学生提供丰富的反馈对于支持学生学习至关重要。生成式人工智能（尤其是大型语言模型）的最新进展，使得向学生提供可重复、可扩展且即时自动生成的反馈成为可能，从而使这一曾经稀缺且昂贵的学习资源变得充裕。从技术角度看，由于人工智能和自然语言处理领域的最新进展，这种方法具有可行性；尽管其潜在优势是强大的推动力，但在应用这些技术时，必须考虑由此引发的一系列潜在伦理问题。预期成果：本工作的目标是使人工智能系统能够自动化常规的评估与反馈任务，同时避免引入"多数暴政"——即因难以实现自动化而忽视长尾分布中少数群体的需求。应用设计：本文将现有的人工智能与机器学习伦理框架应用于向工科学生提供自动化反馈这一具体挑战。任务考量兼顾开发与维护视角，探讨自动化反馈工具将如何随时间演变并被使用。研究发现：本文确定了实施学生自动化反馈的四个关键伦理考量：参与性、开发过程、对学习的影响以及随时间演变。