Is All This Ethical?

As generative AI tools like , , and  become more advanced, they are finding their way into universities, playing increasingly visible roles in research, teaching, and administration. The possibilities are game changing, but the implementation also raises essential ethical considerations. Here's a closer look at what these tools offer and the concerns they bring to the academic landscape.

Potential Applications

Generative AI tools can be valuable assets in an academic setting. They can assist with:

  • Research: Analyzing and summarizing vast amounts of information.
  • Creative Activities: Writing copy, creating images, and even more complex media is now possible, and used widely on campus.
  • Teaching: Creating personalized learning materials, exams, rubrics, and other resources. Also, it's possible to use generative AI to evaluate and respond to student work.
  • Administration: Automating routine tasks to enhance efficiency.

These tools are trained on extensive datasets, including books, articles, code repositories, and other forms of text. For instance, if a tool is trained on computer science texts, it might generate code that mirrors what is found in those books.

Ethical Concerns

While promising, the use of generative AI in universities necessitates careful ethical reflection. Some of the key issues include:

  • Transparency: Universities must be open about their AI usage. Faculty and staff need to know if their work is assessed by AI, and they must be aware of the tools' limitations.
  • Fairness: It's vital that these tools are used equitably, without leading to discrimination or unfair advantages.
  • Privacy: Protecting student data is paramount. 
  • Intellectual Property: The ownership of data used and created in our interactions with these AI tools is a critical consideration. If we are not careful, we can turn over our own IP to train AIs in the future. 

Additional Considerations

Besides the above, we must also weigh other factors:

  • Potential for Bias: So far, generative AI tools have mirrored and amplified human bias. While this is a problem that developers are working on, it's important that human users regularly check for bias in any output created using generative AI tools.
  • Lack of Transparency: Large Language Models (LLMs) are complex and not fully understood, making it challenging to predict their use and potential consequences.
  • Environmental Impact - AIs are very resource-hungry tools, requiring massive data centers with a large amount of heat management to keep the servers cool. That heat pushed out into the environment has a noticable impact on the surrounding environment. However, new models and designs are promosing much more efficient AI tools (such as the DeepSeek model released in early 2025), which may have the impact of mitigating a lot of that long-term impact.

Generative AI tools hold considerable promise for universities, opening new avenues in research, education, and retention efforts. But to harness these opportunities responsibly, it's important to carefully address the accompanying ethical questions.

Find out more: For more information on the university's response to the challenges presented by AI, and how to operated safely and ethically within the university systems, check out the ITS pages . 

 

The content on this page was initially generated using assistance from ChatGPT, Bing, and Bard. It was further updated on 2/12/25 without the use of AI tools.