Generative AI in Higher Ed

If you choose to use GenAI tools or other AI technology, it's important for you to be aware of related ethical concerns regarding privacy, intellectual property, academic honesty, bias, human labor, and environmental issues. 

Some of the ethical considerations around GenAI and academic honesty are highlighted below:

• Can I use a GenAI tool? Check each class syllabus or ask each of your instructors for guidelines and expectations around GenAI use in their class. What is allowed or required in one class may be prohibited in another class. Unauthorized use of GenAI tools could be considered cheating.
• Should I cite a GenAI tool? If your instructor allows you to use GenAI tools for an assignment, ask for instructions on how to cite or provide attribution for any quotes, paraphrasing, and/or ideas you get from them. Some instructors might expect you to provide the prompts you used, an explanation for when, where, and how you used a GenAI tool in your assignment, and/or turn in the full response(s) the AI tool generated.  
• Are these real sources? GenAI tools like ChatGPT or Gemini will sometimes make up credible-sounding citations for sources (e.g., books or articles) that don't actually exist. This phenomenon is called “hallucinating" or "fabricating." If a GenAI tool's output includes references to sources that you're planning to use or cite in your assignment, make sure you track down and read the source first! If you aren't sure how to locate the full text for any of these sources, ask a librarian for help.
• What's the "source" of my information? GenAI tools are often trained on a "black box" of information, which means we don't know which information sources have been used in its training. GenAI tools also aren't "intelligent" - they use algorithms to predict word choice. Do some lateral reading and compare the quality and accuracy of the GenAI tool's output against other more reliable, transparent, or trusted sources before "taking it at its word."  

Some of the ethical considerations around GenAI and the environment are highlighted below:

• Energy consumption: Both training GenAI models and using them in our day-to-day lives currently takes a lot of computational power. The data centers needed to train and support GenAI tools require huge amounts of electricity.
• Water use: GenAI data centers also require a lot of water to sufficiently cool hardware.
• Emissions: Extracting materials used to make hardware components and transporting these materials/products can involve dirty mining, the use of toxic chemicals, and a large carbon footprint. 

Futher Reading:

• Walthier, Cornelia C. "Generative AI's Impact on Climate Change: Benefits and Costs," Forbes. November 12, 2024. https://www.forbes.com/sites/corneliawalther/2024/11/12/generative-ais-impact-on-climate-change-benefits-and-costs/ 
• Zewe, Adam. "Explained: Generative AI’s environmental impact," MIT News. January 17, 2025. https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117  

Codes of ethics for AI frequently highlight issues of accountability, trust, transparency, fairness, and agency.
The video "AI Ethics" outlines five key principles for ethical artificial intelligence (including GenAI):

• Beneficence: AI should be developed and applied to improve the well-being of our planet and its people.
• Nonmaleficence (or "do no harm"): AI should avoid harming privacy, autonomy, or employability.
• Autonomy: Our ability to act freely and independently must be preserved and promoted, while the autonomy of machines must be restricted.
• Justice: AI must be developed, designed, and deployed in ways that promote justice, fairness, equity, and other related values.
• Explicability: To promote the other principles, we need to know the "how" and "why" of AI systems and products; this allows us to hold the correct groups responsible for the positive and negative impacts of AI. Accountability and intelligibility are key.