AI Ethics & Sustainability
Academic Integrity in the Age of Artificial Intelligence
Academic honesty requires clear transparency in the use of AI. At New York Tech, Generative AI tools may only be used for course assignments when specifically permitted by the instructor. Consult the course syllabus or assignment instructions for specific details, and always credit your sources.
Disclosing AI Use
Acknowledgements
List the use of AI in the acknowledgments section:
“The authors acknowledge moderate use of ChatGPT (OpenAI, 2024) in reviewing initial drafts of this material and suggesting revisions. The final content was reviewed and edited by the authors, who take full responsibility for the work.”
Footnotes/Bibliography
Cite the AI like a source in footnotes, endnotes, or bibliography, using APA, MLA, or Chicago style:
“OpenAI. (2024). ChatGPT (Jan 2024 version) [Large language model]. Retrieved from https://openai.com/chatgpt.”
Methodology Section
Declare the use of AI in methodology:
“AI-assisted data analysis using Python was used to a minimal level, identifying patterns and outlier test results which were then reviewed for accuracy by the authors.”
Inline Attribution
Inline attribution within the text:
“According to a summary generated by ChatGPT (2025) and reviewed for accuracy by the author, the main themes of the series of essays on this topic…”
Ethical Issues
Artificial intelligence raises significant ethical concerns related to privacy, bias, accountability, and the potential misuse of technology. There is also additional potential for long-term economic and other issues related to the impact of AI use. This highlights the need for transparent, fair, and responsible AI systems that respect human rights and societal values.
- Job loss and economic shifts
- Over-reliance on AI and diminished human capabilities, such as creativity and critical thinking
- Digital divides and global power imbalances
- Loss of human control over AI systems
- Intellectual property and copyright concerns
- Privacy and data security risks
- Bias and fairness—AI can inherit prejudices from training data
- Misuse—AI can generate fake media and be used for disinformation
- Harmful environmental impact and excessive use of water
AI and Sustainability
Artificial intelligence has the power to drive innovation, but it also comes with an environmental cost. As AI models grow larger and more powerful, the energy required to train and run them increases dramatically. Understanding these impacts is the key to a more sustainable AI future.
By adopting thoughtful habits and supporting transparency and sustainability, individuals can help reduce the environmental footprint of AI while still benefiting from its transformative potential.
- High Energy Use – Training large AI models and then running queries against them requires as much electricity as powering a small town.
- Carbon Emissions – Data centers running on non-renewable energy sources contribute substantially to greenhouse gas emissions, accelerating climate change. One large model run can match the lifetime emissions of several cars.
- Water Usage – Many AI data centers use water-based cooling systems to keep servers from overheating. This can mean millions of gallons of water per year per data center, straining local supplies, especially in drought-prone regions.
- Compounding Effects at Scale – While one user’s AI query may have a small footprint, billions of queries daily amplify the environmental toll.
- Prompt with Intention – Plan questions carefully and avoid overly verbose outputs by including word limit constraints in your prompts to reduce unnecessary processing.
- Use AI Wisely – Reserve AI for tasks that provide real efficiency rather than for simple queries. A single ChatGPT query consumes 5x more electricity than a basic web search.
- Batch Your Work – Group related questions into a single prompt to minimize system load.
- Choose Sustainable Tools – Use smaller, less resource-intensive models and AI providers that prioritize openness about energy use and environmental impact.