README.md

# Hugging Face Ethics & Society Papers

Abid, A., Abdalla, A., Abid, A., Khan, D., Alfozan, A., & Zou, J. (2019). Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild (arXiv:1906.02569). arXiv. https://doi.org/10.48550/arXiv.1906.02569
Akiki, C., Ogundepo, O., Piktus, A., Zhang, X., Oladipo, A., Lin, J., & Potthast, M. (2023). Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face (arXiv:2302.14534). arXiv. https://doi.org/10.48550/arXiv.2302.14534
Alex, N., Lifland, E., Tunstall, L., Thakur, A., Maham, P., Riedel, C. J., Hine, E., Ashurst, C., Sedille, P., Carlier, A., Noetel, M., & Stuhlmüller, A. (2022). RAFT: A Real-World Few-Shot Text Classification Benchmark (arXiv:2109.14076). arXiv. https://doi.org/10.48550/arXiv.2109.14076
Crisan, A., Drouhard, M., Vig, J., & Rajani, N. (2022). Interactive Model Cards: A Human-Centered Approach to Model Documentation. 2022 ACM Conference on Fairness, Accountability, and Transparency, 427–439. https://doi.org/10.1145/3531146.3533108
Friedrich, F., Schramowski, P., Brack, M., Struppek, L., Hintersdorf, D., Luccioni, S., & Kersting, K. (2023). Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness (arXiv:2302.10893). arXiv. https://doi.org/10.48550/arXiv.2302.10893
Jernite, Y., Nguyen, H., Biderman, S., Rogers, A., Masoud, M., Danchev, V., Tan, S., Luccioni, A. S., Subramani, N., Dupont, G., Dodge, J., Lo, K., Talat, Z., Johnson, I., Radev, D., Nikpoor, S., Frohberg, J., Gokaslan, A., Henderson, P., … Mitchell, M. (2022). Data Governance in the Age of Large-Scale Data-Driven Language Technology. ACM Computing Surveys, 1122445.1122456. https://doi.org/10.1145/1122445.1122456
Kreutzer, J., Caswell, I., Wang, L., Wahab, A., van Esch, D., Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov, A., Sikasote, C., Setyawan, M., Sarin, S., Samb, S., Sagot, B., Rivera, C., Rios, A., Papadimitriou, I., Osei, S., Suarez, P. O., … Adeyemi, M. (2022). Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets. Transactions of the Association for Computational Linguistics, 10, 50–72. https://doi.org/10.1162/tacl_a_00447
Luccioni, A. S., Corry, F., Sridharan, H., Ananny, M., Schultz, J., & Crawford, K. (2022). A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication. 2022 ACM Conference on Fairness, Accountability, and Transparency, 199–212. https://doi.org/10.1145/3531146.3533086
Luccioni, A. S., & Hernandez-Garcia, A. (2023). Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning (arXiv:2302.08476). arXiv. https://doi.org/10.48550/arXiv.2302.08476
Luccioni, A. S., & Rolnick, D. (2022). Bugs in the Data: How ImageNet Misrepresents Biodiversity (arXiv:2208.11695). arXiv. https://doi.org/10.48550/arXiv.2208.11695
Mitchell, M., Luccioni, A. S., Lambert, N., Gerchick, M., McMillan-Major, A., Ozoani, E., Rajani, N., Thrush, T., Jernite, Y., & Kiela, D. (2022). Measuring Data (arXiv:2212.05129). arXiv. https://doi.org/10.48550/arXiv.2212.05129
Qian, R., Ross, C., Fernandes, J., Smith, E., Kiela, D., & Williams, A. (2022). Perturbation Augmentation for Fairer NLP (arXiv:2205.12586). arXiv. https://doi.org/10.48550/arXiv.2205.12586
Rajani, N., Liang, W., Chen, L., Mitchell, M., & Zou, J. (2022). SEAL: Interactive Tool for Systematic Error Analysis and Labeling (arXiv:2210.05839). arXiv. https://doi.org/10.48550/arXiv.2210.05839
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., Dey, M., Bari, M. S., Xu, C., Thakker, U., Sharma, S. S., Szczechla, E., Kim, T., Chhablani, G., Nayak, N., … Rush, A. M. (2022). Multitask Prompted Training Enables Zero-Shot Task Generalization (arXiv:2110.08207). arXiv. https://doi.org/10.48550/arXiv.2110.08207
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ilić, S., Hesslow, D., Castagné, R., Luccioni, A. S., Yvon, F., Gallé, M., Tow, J., Rush, A. M., Biderman, S., Webson, A., Ammanamanchi, P. S., Wang, T., Sagot, B., Muennighoff, N., del Moral, A. V., … Wolf, T. (2022). BLOOM: A 176B-Parameter Open-Access Multilingual Language Model. https://doi.org/10.48550/ARXIV.2211.05100
Solaiman, I. (2023). The Gradient of Generative AI Release: Methods and Considerations (arXiv:2302.04844). arXiv. https://doi.org/10.48550/arXiv.2302.04844
von Werra, L., Tunstall, L., Thakur, A., Luccioni, A. S., Thrush, T., Piktus, A., Marty, F., Rajani, N., Mustar, V., Ngo, H., Sanseviero, O., Šaško, M., Villanova, A., Lhoest, Q., Chaumond, J., Mitchell, M., Rush, A. M., Wolf, T., & Kiela, D. (2022). Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements (arXiv:2210.01970). arXiv. https://doi.org/10.48550/arXiv.2210.01970