Research Guides: Generative Artificial Intelligence (GenAI) in Higher Education: Ethics & Misinformation

Limitations and Ethical Questions

Opinion: Is it time to start considering personhood rights for AI chatbots?
Noam Chomsky: The False Promise of ChatGPT "We know from the science of linguistics and the philosophy of knowledge that AI minds differ profoundly from how humans reason and use language. These differences place significant limitations on what these programs can do, encoding them with ineradicable defects."
Conversation Starter: Teaching Philosophy in an Age of Large Language Models "Over the past few years we have seen some startling progress from Large Language Models (LLMs) like GPT-3, and some of those paying attention to these developments, such as philosopher John Symons (University of Kansas), believe that they pose an imminent threat to teaching and learning (for those who missed its inclusion in the Heap of Links earlier this summer, you can read Professor Symons’ thoughts on this here). In the following guest post, Benjamin Mitchell-Yellin (Sam Houston State University) responds to Professor Symons, offering his own view of the dangers LLMs pose as well as strategies teachers could employ to minimize them."
Did Artificial Intelligence Just Get Too Smart?
The Risk of Compounding Inequality
Positive Friction – An Interview with Sharon Strover
What is the most pressing ethical concern in AI technology currently facing society? University of Texas Moody College of Communication professor, Sharon Strover, is particularly concerned about the way in which our personal data is "gathered, used and reused, sold and resold.”
Worries about developments in AI
Understanding artificial intelligence ethics and safety
From the Alan Turing Institute: A guide for the responsible design and implementation of AI systems in the public sector
The mounting human and environmental costs of generative AI
Inside the post-ChatGPT scramble to create AI essay detectors
Will they cause more problems than they solve?

Concerns of Inaccuracy and Misinformation

"Imagine that you want to know what’s the best cuisine in the world. If you ask someone this question twice, a minute apart, their answers both times should be the same.

"If you ask a model the same question twice, its answer can change. If the model thinks that Vietnamese cuisine has a 70% chance of being the best cuisine and Italian cuisine has a 30% chance, it’ll answer “Vietnamese” 70% of the time, and “Italian” 30%.

"This probabilistic nature makes AI great for creative tasks. ...However, this probabilistic nature also causes inconsistency and hallucinations. It’s fatal for tasks that depend on factuality."

- Chip Huyen, "Sampling for Text Generation," Jan 16, 2024