A Guide to Artificial Intelligence (AI) for Students

A 2019 article by researchers from University of Massachusetts Amherst quantifies the approximate financial and environmental costs of training a variety of AI tools, and offers their recommendations to lessen harmful global impact.  

• Fossil-fuel extraction
  • Training and running AI models, particularly large language models, requires enormous amounts of energy, often derived from fossil fuels. This contributes to greenhouse gas emissions and climate change.
• Quickly growing carbon footprint
  • Training can produce about 626,000 pounds of carbon dioxide- the equivalent of 300 round-trip flights between New York and San Francisco, or nearly 5 times the lifetime emissions of the average car. 
• Influx of carbon-dioxide emissions
  • ​​​​​​​Researchers estimated that creating GPT-3 consumed 1,287 megawatt hours of electricity and generated 552 tons of carbon dioxide equivalent- the equivalent of 123 gasoline-powered passenger vehicles driven for one year.
• Constant targeted ads increase spending (e-commerce), increase fast fashion production
  • ​​​​​​​increased consumption and waste
    • Hyper-personalization of marketing campaigns to encourage impulse buying
    • Trend prediction and virtual try-on capabilities
    • AI can help monitor supply chains, but doesn't guarantee ethical practices- the pressure to produce quickly can lead to exploitation
• E-waste produced by AI technology
  • The production and improper disposal of AI hardware generate electronic waste, which contains harmful chemicals that can contaminate the environment.
• Lack of transparency and accountability regarding environmental impact
  • ​​​​​​​“For ChatGPT’s latest model, GPT4, [OpenAI] hasn’t said anything about either how long it’s been trained, where it’s trained, or anything at all about the data they’re using...So essentially, it means it’s impossible to estimate emissions.” Dr. Sasha Luccioni, AI Ethics Researcher

  • Newer AI models are getting bigger – and more energy intensive. Bigger models require the use of more and more powerful graphics processing units (GPUs), and take longer to train – using up more resources and energy.

  • Errors in artificial intelligence algorithms and decision-making processes lead to environmental injustice and inequality

  • AI technologies may disrupt natural ecosystems, jeopardizing wildlife habitats and migration patterns

• Freshwater Usage

  • One non-peer-reviewed study, led by researchers at UC Riverside, estimates that training GPT3 in Microsoft’s state-of-the-art US data centers could potentially have consumed 700,000 liters (184,920.45 gallons) of freshwater. In the absence of accurate, public data, the researchers had to assume the “water use effectiveness”, or the ratio of energy a data center uses and the water used to keep it cooled and functioning, based on Microsoft’s self-reported average.

• Data Centers energy consumption
  • ​​​​​​​By 2040 it is expected that the emissions from the Information and Communications Technology industry as a whole will reach 14% of the global emissions.

Li, P., Yang, J., Islam, M. A., & Ren, S. (2023). Making AI less "thirsty": Uncovering and addressing the secret water footprint of AI models. (). Ithaca: Cornell University Library, arXiv.org. https://doi.org/10.48550/arxiv.2304.03271

Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. (). Ithaca: Cornell University Library, arXiv.org. https://doi.org/10.48550/arxiv.1906.02243

Zhuk A. Artificial Intelligence Impact on the Environment: Hidden Ecological Costs and Ethical-Legal Issues. Journal of Digital Technologies and Law. 2023;1(4):932-954. https://doi.org/10.21202/jdtl.2023.40

There are also AI powered tools with the potential to address several environmental challenges such as climate modeling, renewable energy optimization, sustainable agriculture, disaster prediction & response, and conservation efforts.

• Earth Insights
• Biodiversity Research
• Arctic Conservation
• Predicting Weather Disasters- flood management, storm responses
• Tracking Energy misuse/excessive use
• Ocean Cleanup
• xView2
• Climate Trace
• ClimateAI
• ClimaX
• WattTime
• Bumble Bee Watch
• Rainforest Connection
• Deltares Aquality App
• IBM Sustainability Accelerator
• CodeCarbon
• Blue River Technology
• DeepMind
• Intel Rhino Chip
• Electricity Grid Management, identifying opportunities for increased efficiency
• Satellite monitoring to track global climate change impacts and progress

TED talk by Dr. Sasha Luccioni, AI Ethics Researcher    

10/2023