Notable Papers
This list highlights works that could inspire participants in our call for papers.
- Bommasani, Rishi, et al. “On the opportunities and risks of foundation models.” arXiv preprint arXiv:2108.07258 (2021).
- Brownlee, Alexander EI, et al. “Exploring the accuracy–energy trade-off in machine learning.” 2021 IEEE/ACM International Workshop on Genetic Improvement (GI). IEEE, 2021.
- Carlini, Nicolas, et al. “Extracting training data from diffusion models.” 32nd USENIX Security Symposium (USENIX Security 23). 2023.
- Chen, Irene Y., et al. “Ethical Machine Learning in Healthcare.” Annual Review of Biomedical Data Science 4 (2021): 123-144.
- Chiang, Wei-Lin, et al. “Chatbot Arena: An open platform for evaluating LLMs by human preference.” Forty-first International Conference on Machine Learning. 2024.
- Dwork, Cynthia, et al. “Fairness Through Awareness.” Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. 2012.
- Eyring, Veronika, et al. “Pushing the frontiers in climate modelling and analysis with machine learning.” Nature Climate Change 14.9 (2024): 916-928.
- Gebru, Timnit, et al. “Datasheets for datasets.” Communications of the ACM 64.3 (2021): 86-92.
- Henderson, Peter, et al. “Towards the systematic reporting of the energy and carbon footprints of machine learning.” Journal of Machine Learning Research 21.248 (2020): 1-43.
- Kapoor, Sayash, et al. “On the societal impact of open foundation models.” arXiv preprint arXiv:2403.07918 (2024).
- Lacoste, Alexandre, et al. “Quantifying the carbon emissions of machine learning.” arXiv preprint arXiv:1910.09700 (2019).
- Liang, Percy, et al. “Holistic evaluation of language models.” arXiv preprint arXiv:2211.09110 (2022).
- Liu, Yang, et al. “Report Cards: Qualitative Evaluation of LLMs Using Natural Language Summaries”
- Łucki, Jakub, et al. “An Adversarial Perspective on Machine Unlearning for AI Safety”
- Mitchell, Margaret, et al. “Model cards for model reporting.” Proceedings of the Conference on Fairness, Accountability, and Transparency. 2019.
- Oala, Luis, et al. “DMLR: Data-Centric Machine Learning Research–Past, Present and Future.” arXiv preprint arXiv:2311.13028 (2023).
- Obermeyer, Ziad, et al. “Dissecting racial bias in an algorithm used to manage the health of populations.” Science 366.6464 (2019): 447-453.
- Raji, Inioluwa Deborah, et al. “Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing.” Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020.
- Reddi, Vijay Janapa, et al. “MLPerf Inference Benchmark.” 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA). IEEE, 2020.
- Rolnick, David, et al. “Tackling climate change with machine learning.” ACM Computing Surveys (CSUR) 55.2 (2022): 1-96.
- Schneider, Ian, et al. “Life-Cycle Emissions of AI Hardware: A Cradle-to-Grave Approach and Generational Trends.” arXiv preprint arXiv:2402.01671 (2024).
- Schwartz, Roy, et al. “Green AI.” Communications of the ACM 63.12 (2020): 54-63.
- Strubell, Emma, et al. “Energy and Policy Considerations for Deep Learning in NLP.” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
- Wang, Keyu, et al. “Mitigating Downstream Model Risks via Model Provenance.” arXiv preprint arXiv:2401.02230 (2024).
- Weidinger, Laura, et al. “Taxonomy of risks posed by language models.” Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022.
- Wu, Carole-Jean, et al. “Sustainable AI: Environmental implications, challenges and opportunities.” Proceedings of Machine Learning and Systems 4 (2022): 795-813.
- Zhang, Tianyi, et al. “Privacy-Preserving Machine Learning: Methods and Challenges.” IEEE Transactions on Artificial Intelligence 4.3 (2023): 512-527.