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 # 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
+This is an incomplete list of ethics-related papers published by researchers at Hugging Face.
+
+- Gradio: http://arxiv.org/abs/1906.02569
+- RAFT: http://arxiv.org/abs/2109.14076
+- Interactive Model Cards: http://arxiv.org/abs/2205.02894
+- Data Governance in the Age of Large-Scale Data-Driven Language Technology: http://arxiv.org/abs/2206.03216