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Filtered Union Bibliography

This automatically-generated file contains references from the main union bibliography that have been filtered for a single tag. Do not edit this file; instead, please update the main bibliography and tag references appropriately to have them show up here. Thank you!

The papers are listed in the same order as the main bibliography; e.g., by year of publication / release; then by surname / name of the first author.

preprint

  • Morand, C., Névéol, A., & Ligozat, A. L. (2025). Does Efficiency Lead to Green Machine Learning Model Training? Analyzing Historical Trends in Impacts from Hardware, Algorithmic and Carbon Optimizations. [paper] Environmental Impact preprint

  • Kantharuban, A., Milbauer, J., Strubell, E., & Neubig, G. (2024). Stereotype or personalization? user identity biases chatbot recommendations [paper] Biases preprint

  • Karamolegkou, A., Hansen, S. S., Christopoulou, A., Stamatiou, F., Lauscher, A., & Søgaard, A. (2024). Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statements. [paper] General Resources preprint

  • Li, P., Yang, J., Islam, M. A., & Ren, S. (2023). Making ai less" thirsty": Uncovering and addressing the secret water footprint of ai models. [paper] preprint Environmental Impact

  • Luccioni, S., Jernite, Y. & Strubell, E. (2024). Power Hungry Processing: Watts Driving the Cost of AI Deployment? In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24). Association for Computing Machinery, New York, NY, USA, 85–99. [paper] preprint Environmental Impact

  • Birhane, A., Prabhu, V. U., & Kahembwe, E. (2021). Multimodal datasets: misogyny, pornography, and malignant stereotypes. arXiv preprint arXiv:2110.01963. [paper] Data preprint

  • Anthony, L. F. W., Kanding, B., & Selvan, R. (2020). Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv preprint arXiv:2007.03051. [paper] Environmental Impact preprint

  • Raji, I. D., & Yang, J. (2019). About ML: Annotation and benchmarking on understanding and transparency of machine learning lifecycles. arXiv preprint arXiv:1912.06166. [paper] Data preprint

  • Holland, S., Hosny, A., Newman, S., Joseph, J., & Chmielinski, K. (2018). The dataset nutrition label: A framework to drive higher data quality standards. arXiv preprint arXiv:1805.03677. [paper] Data preprint