  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.
