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|Instruction Step Text=This step explores the environmental impacts of generative AI across its lifecycle: from training, deployment, and inference to hardware and infrastructure. | |Instruction Step Text=This step explores the environmental impacts of generative AI across its lifecycle: from training, deployment, and inference to hardware and infrastructure. | ||
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| + | |Instruction Step Title=Making AI More Sustainable: What Are the Options? | ||
| + | |Instruction Step Text=In this step you’ll learn about the energy and resource demands of generative AI, as well as realistic strategies that reduce environmental impacts while maintaining performance. | ||
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Latest revision as of 09:29, 23 September 2025
ModulesThe Environmental Cost of AI: What Can we do about it?
By the end of this micromodule, participants will be able to:
- Identify key environmental impacts of AI technologies
- Explain how AI’s infrastructure contributes to climate and environmental pressures
- Reflect on trade-offs and future governance needs regarding AI deployment
What is this about?
The Environmental Footprint of AI: Understanding the Trade-offs
In this activity, you’ll explore the environmental impacts associated with AI technologies. Concerns include electricity use, water consumption, rare earth mining, and e-waste. Possible solutions and governance ideas are also explored. Interactive exercises to test your understanding are also included.
The Environmental Footprint of AI: Understanding the Trade-offs
Understanding the Systemic Impacts of Generative AI
This step explores the environmental impacts of generative AI across its lifecycle: from training, deployment, and inference to hardware and infrastructure.
Making AI More Sustainable: What Are the Options?
In this step you’ll learn about the energy and resource demands of generative AI, as well as realistic strategies that reduce environmental impacts while maintaining performance.
