Summary of Towards Environmentally Equitable Ai, by Mohammad Hajiesmaili and Shaolei Ren and Ramesh K. Sitaraman and Adam Wierman
Towards Environmentally Equitable AI
by Mohammad Hajiesmaili, Shaolei Ren, Ramesh K. Sitaraman, Adam Wierman
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a framework for managing artificial intelligence (AI) systems to prioritize environmental equity and reduce their environmental impact. The current approach to deploying AI workloads can lead to significant variations in environmental impact across different regions, exacerbating environmental inequities and creating unintended consequences. To address this issue, the authors advocate for a more sustainable and equitable approach to AI management, incorporating geographical load balancing and algorithmic challenges. The paper concludes by highlighting future directions to further mitigate AI’s environmental inequity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is getting bigger and using lots of energy! As we make more powerful machines, we’re also making a big impact on the environment. Some places are getting hit harder than others, which isn’t fair. In this paper, some smart people suggest ways to make sure we’re not being too hard on one place or another. They want to share the environmental cost fairly across different regions. It’s an important problem that needs solving! |
