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An Economic Approach to Regulating Algorithms. NBER Working Paper Series (2020). https:\/\/api.semanticscholar.org\/CorpusID:214775707","DOI":"10.3386\/w27111"},{"key":"e_1_3_2_1_111_1","volume-title":"Red-Teaming the Stable Diffusion Safety Filter. ArXiv abs\/2210.04610","author":"Rando Javier","year":"2022","unstructured":"Javier Rando, Daniel Paleka, David Lindner, Lennard Heim, and Florian Tram\u00e8r. 2022. Red-Teaming the Stable Diffusion Safety Filter. ArXiv abs\/2210.04610 (2022). https:\/\/api.semanticscholar.org\/CorpusID:252780252"},{"key":"e_1_3_2_1_112_1","volume-title":"Auditing Black-Box Prediction Models for Data Minimization Compliance. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems","author":"Rastegarpanah Bashir","year":"2021","unstructured":"Bashir Rastegarpanah, Krishna\u00a0P. Gummadi, and Mark Crovella. 2021. Auditing Black-Box Prediction Models for Data Minimization Compliance. 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Auditing Algorithms : Research Methods for Detecting Discrimination on Internet Platforms. https:\/\/api.semanticscholar.org\/CorpusID:15686114"},{"key":"e_1_3_2_1_115_1","volume-title":"Muse: Machine unlearning six-way evaluation for language models. arXiv preprint arXiv:2407.06460","author":"Shi Weijia","year":"2024","unstructured":"Weijia Shi, Jaechan Lee, Yangsibo Huang, Sadhika Malladi, Jieyu Zhao, Ari Holtzman, Daogao Liu, Luke Zettlemoyer, Noah\u00a0A Smith, and Chiyuan Zhang. 2024. Muse: Machine unlearning six-way evaluation for language models. arXiv preprint arXiv:2407.06460 (2024)."},{"key":"e_1_3_2_1_116_1","doi-asserted-by":"crossref","unstructured":"Latanya Sweeney. 2013. Discrimination in Online Ad Delivery. http:\/\/ssrn.com\/abstract=2208240.","DOI":"10.2139\/ssrn.2208240"},{"key":"e_1_3_2_1_117_1","unstructured":"Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna Dumitru Erhan Ian Goodfellow and Rob Fergus. 2013. 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