Update papers.bib

_bibliography/papers.bib

@@ -8,9 +8,9 @@ @article{AI_Water_CACM_2024
   title={Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models},
   author={Pengfei Li and Jianyi Yang and Mohammad A. Islam and Shaolei Ren},
   abstract={The growing carbon footprint of artificial intelligence (AI) models, especially large ones such as GPT-3, has been undergoing public scrutiny. Unfortunately, however, the equally important and enormous water (withdrawal and consumption) footprint of AI models has remained under the radar. For example, training GPT-3 in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater, but such information has been kept a secret. More critically, the global AI demand might be accountable for 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027, which is more than the total annual water withdrawal of 4 -- 6 Denmark or half of the United Kingdom. This is very concerning, as freshwater scarcity has become one of the most pressing challenges shared by all of us in the wake of the rapidly growing population, depleting water resources, and aging water infrastructures. To respond to the global water challenges, AI models can, and also must, take social responsibility and lead by example by addressing their own water footprint. In this paper, we provide a principled methodology to estimate the water footprint of AI models, and also discuss the unique spatial-temporal diversities of AI models' runtime water efficiency. Finally, we highlight the necessity of holistically addressing water footprint along with carbon footprint to enable truly sustainable AI.},
-  journal={Communications of the ACM (accepted)},
+  journal={Communications of the ACM},
   month = {},
-  year={2024},
+  year={2024 (accepted)},
   url={https://arxiv.org/abs/2304.03271},
   html={https://arxiv.org/abs/2304.03271},
   bibtex_show = {true},
@@ -139,11 +139,11 @@ @article{SafePlay_Games_Learning_NeurIPS_2024
 @article{Environmentally_Equitable_AI_CACM_2024,
   abbr={CACM},
   title={Towards Environmentally Equitable AI},
-  author={Mohammad Hajiesmaili and Shaolei Ren and Ramesh Sitaraman and Adam Wierman},
+  author={Mohammad Hajiesmaili* and Shaolei Ren* and Ramesh Sitaraman* and Adam Wierman*},
   abstract={The skyrocketing demand for artificial intelligence (AI) has created an enormous appetite for globally deployed power-hungry servers. As a result, the environmental footprint of AI systems has come under increasing scrutiny. More crucially, the current way that we exploit AI workloads’ flexibility and manage AI systems can lead to wildly different environmental impacts across locations, increasingly raising environmental inequity concerns and creating unintended sociotechnical consequences. In this paper, we advocate environmental equity as a priority for the management of future AI systems, advancing the boundaries of existing resource management for sustainable AI and also adding a unique dimension to AI fairness. Concretely, we uncover the potential of equity-aware geographical load balancing to fairly re-distribute the environmental cost across different regions, followed by algorithmic challenges. We conclude by discussing a few future directions to exploit the full potential of system management approaches to mitigate AI's environmental inequity.},
-  journal={Communications of the ACM (accepted)},
+  journal={Communications of the ACM},
   month = {},
-  year={2024},
+  year={2024  (*equal contribution, accepted)},
   url={https://arxiv.org/abs/2307.05494},
   html={https://arxiv.org/abs/2307.05494},
   bibtex_show = {true},