How to Cite:
Alif Mohamed Khan, "Auditing Algorithmic Environmental Impact of Training Enormous AI Models" American International Journal of Computer Science and Technology, Vol. 1, No. 1, pp. 1-6, 2025.

Abstract:
The rapid advancement and deployment of large-scale AI models have brought unprecedented capabilities but also significant environmental concerns. Training these enormous models requires substantial computational resources, resulting in considerable energy consumption and carbon emissions. This paper presents a comprehensive framework for auditing the algorithmic environmental impact of training large AI models. We review current measurement methodologies, propose standardized auditing practices, and analyze case studies of prominent models to highlight the environmental costs associated with their training. Furthermore, we discuss strategies to mitigate these impacts through algorithmic optimizations, hardware improvements, and policy interventions. Our work aims to foster transparency and responsibility in AI research, encouraging the community to prioritize sustainability alongside innovation.

Keywords: Environmental Impact, AI Model Training, Carbon Footprint, Algorithmic Auditing, Energy Consumption, Large-Scale AI Models, Sustainable AI, Computational Resources, Lifecycle Assessment, Model Optimization.

References:
1. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650.
2. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., ... & Dean, J. (2021). Carbon Emissions and Large Neural Network Training. arXiv preprint arXiv:2104.10350.
3. Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. Journal of Machine Learning Research, 21(248), 1–43.
4. Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the Carbon Emissions of Machine Learning. arXiv preprint arXiv:1910.09700.
5. Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54–63.
6. Wu, M., Dholakia, A., & Taylor, S. (2021). A Survey of Model Compression and Acceleration for Deep Neural Networks. Neurocomputing, 465, 113–141.
7. Li, T., & Li, J. (2021). A Comprehensive Survey on Efficient Transformer Models for Natural Language Processing. IEEE Transactions on Neural Networks and Learning Systems.
8. Strubell, E., Ganesh, A., & McCallum, A. (2020). Energy and Policy Considerations for Deep Learning in NLP. Association for Computational Linguistics.
9. Bender, E. M., & Gebru, T. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623.
10. Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2020). Green Algorithms: Quantifying the Carbon Footprint of Computation. Communications of the ACM, 63(2), 54–61.
11. Henderson, P., et al. (2020). Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning. Journal of Machine Learning Research, 21(248), 1–43.
12. Jia, R., et al. (2022). Efficient Training of Large-Scale Transformers: A Survey. IEEE Transactions on Big Data.
13. Li, X., et al. (2021). A Survey on Energy Efficient Deep Learning. IEEE Transactions on Sustainable Computing.
14. Schwartz, R., et al. (2020). Green AI. Communications of the ACM, 63(12), 54–63.
15. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and Policy Considerations for Deep Learning in NLP. Proceedings of ACL.