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README.md

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 # Hugging Face Ethics & Society Papers
 
-- [1] A. Abid, A. Abdalla, A. Abid, D. Khan, A. Alfozan, and J. Zou, “Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild.” arXiv, Jun. 06, 2019. doi: 10.48550/arXiv.1906.02569.
-- [2] N. Alex et al., “RAFT: A Real-World Few-Shot Text Classification Benchmark.” arXiv, Jan. 18, 2022. doi: 10.48550/arXiv.2109.14076.
-- [3] A. Crisan, M. Drouhard, J. Vig, and N. Rajani, “Interactive Model Cards: A Human-Centered Approach to Model Documentation,” in 2022 ACM Conference on Fairness, Accountability, and Transparency, Jun. 2022, pp. 427–439. doi: 10.1145/3531146.3533108.
-- [4] Y. Jernite et al., “Data Governance in the Age of Large-Scale Data-Driven Language Technology,” ACM Comput. Surv., p. 1122445.1122456, Mar. 2022, doi: 10.1145/1122445.1122456.
-- [5] J. Kreutzer et al., “Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets,” Transactions of the Association for Computational Linguistics, vol. 10, pp. 50–72, Jan. 2022, doi: 10.1162/tacl_a_00447.
-- [6] A. S. Luccioni, F. Corry, H. Sridharan, M. Ananny, J. Schultz, and K. Crawford, “A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication,” in 2022 ACM Conference on Fairness, Accountability, and Transparency, New York, NY, USA, Jun. 2022, pp. 199–212. doi: 10.1145/3531146.3533086.
-- [7] A. S. Luccioni and D. Rolnick, “Bugs in the Data: How ImageNet Misrepresents Biodiversity.” arXiv, Aug. 24, 2022. doi: 10.48550/arXiv.2208.11695.
-- [8] M. Mitchell et al., “Measuring Data.” arXiv, Dec. 09, 2022. doi: 10.48550/arXiv.2212.05129.
-- [9] R. Qian, C. Ross, J. Fernandes, E. Smith, D. Kiela, and A. Williams, “Perturbation Augmentation for Fairer NLP.” arXiv, Oct. 12, 2022. doi: 10.48550/arXiv.2205.12586.
-- [10] N. Rajani, W. Liang, L. Chen, M. Mitchell, and J. Zou, “SEAL : Interactive Tool for Systematic Error Analysis and Labeling.” arXiv, Oct. 11, 2022. doi: 10.48550/arXiv.2210.05839.
-- [11] V. Sanh et al., “Multitask Prompted Training Enables Zero-Shot Task Generalization.” arXiv, Mar. 17, 2022. doi: 10.48550/arXiv.2110.08207.
-- [12] T. L. Scao et al., “BLOOM: A 176B-Parameter Open-Access Multilingual Language Model,” 2022, doi: 10.48550/ARXIV.2211.05100.
-- [13] L. von Werra et al., “Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements.” arXiv, Oct. 06, 2022. doi: 10.48550/arXiv.2210.01970.
-- [14] C. Akiki et al., “Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face.” arXiv, Feb. 28, 2023. doi: 10.48550/arXiv.2302.14534.
-- [15] F. Friedrich et al., “Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness.” arXiv, Feb. 07, 2023. doi: 10.48550/arXiv.2302.10893.
-- [16] A. S. Luccioni and A. Hernandez-Garcia, “Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning.” arXiv, Feb. 16, 2023. doi: 10.48550/arXiv.2302.08476.
-- [17] I. Solaiman, “The Gradient of Generative AI Release: Methods and Considerations.” arXiv, Feb. 05, 2023. doi: 10.48550/arXiv.2302.04844.
+[1] I. Solaiman, “The Gradient of Generative AI Release: Methods and Considerations.” arXiv, Feb. 05, 2023. doi: 10.48550/arXiv.2302.04844.