Lindell Library: Student Guide to AI: AI & Ethics

Ethical Considerations

Ethical Implications
Environmental Factors

According to the 2023 UNESCO's "Chat GPT and Artificial Intelligence in Higher Education Quick Start Guide", the main challenges and implications of ChatGPT in higher education are:

Academic integrity
- ChatGPT raises academic integrity concerns in higher education due to potential plagiarism and cheating. Reliable ChatGPT detection tools have yet to be developed.
Lack of regulation ChatGPT
- ChatGPT's unregulated development raises concerns. Over 1,000 academics and leaders call for a pause to investigate risks and develop shared protocols.
Privacy concerns
- In April 2023, Italy became the first country to block ChatGPT over privacy concerns and ethical issues regarding data collection and age verification, setting a precedent for AI-related data practices.
Cognitive bias
- ChatGPT lacks ethical principles and can't differentiate between truth and bias or truth and fiction ("hallucination"). Critical analysis and cross-referencing with other sources are crucial when using its results.
Accessibility
- Two main accessibility concerns for ChatGPT are restricted availability due to government regulations and uneven internet access, raising issues of equity and regional disparities in AI education and development.
Commercialization
- ChatGPT offers both free and subscription options. Careful regulation is necessary for AI tools run by profit-driven companies, which may lack openness and use data for commercial purposes in higher education settings.

Adapted from AI Literacy and Critical Thinking, Macalester College Library.

As the use of generative AI has increased, the effects of this technology on the environment have grown. The full extent of generative AI's energy consumption is not completely known yet. Obtaining clear data is a challenge as the AI industry isn’t transparent, and energy use varies wildly by model size, location, and what stage it’s in (training an AI model uses much more energy than prompting a query).

The conversation is further complicated because what data is available can sometimes be manipulated by people who are hoping to prove a certain viewpoint. Another complication is that AI is used to create energy efficiencies and in the future might significantly contribute to energy and climate innovations. How might that be factored into environmental impact?