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