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The Right to be Forgotten (RTBF) was first established as the result of the ruling of Google Spain SL, Google Inc.
Google Spain SL, Google Inc. v. Agencia Española de Protección de Datos (AEPD), Mario Costeja González (2014)
2014
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Lindsay, D.: The ‘right to be forgotten’by search engines under data privacy law: A legal analysis of the costeja ruling. Journal of Media Law 6
2014
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Cao, Y., Yang, J.: Towards making systems forget with machine unlearning. In: 2015 IEEE Symposium on Security and Privacy, pp. 463–480 (2015). IEEE
2015
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Saffarizadeh, K., Boodraj, M., Alashoor, T.M., et al
2017
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Esposito, E.: Algorithmic memory and the right to be forgotten on the web. Big Data & Society 4
2017
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2018
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Villaronga, E.F., Kieseberg, P., Li, T.: Humans forget, machines remember: Artificial intelligence and the right to be forgotten. Computer Law & Security Review 34
2018
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Bertram, T., Bursztein, E., Caro, S., Chao, H., Feman, R.C., Fleischer, P., Gustafsson, A., Hemerly, J., Hibbert, C., Invernizzi, L., Donnelly, L.K., Ketover, J., Laefer, J., Nicholas, P., Niu, Y., Obhi, H., Price, D., Strait, A., Thomas, K., Verney, A.: Five years of the right to be forgotten. In: Proceedings of the Conference on Computer and Communications Security (2019)
2019
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Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al
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2019
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Al-Rubaie, M., Chang, J.M.: Privacy-preserving machine learning: Threats and solutions. IEEE Security & Privacy 17
2019
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Werro, F.: The right to be forgotten a comparative study of the emergent right’s evolution and application in europe, the americas, and asia (2020)
2020
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al
2020
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Floridi, L., Chiriatti, M.: Gpt-3: Its nature, scope, limits, and consequences. Minds and Machines 30
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Maynez, J., Narayan, S., Bohnet, B., Mcdonald, R.T.: On faithfulness and factuality in abstractive summarization. In: Proceedings of The 58th Annual Meeting of the Association for Computational Linguistics (ACL) (2020)
2020
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Guo, C., Goldstein, T., Hannun, A., Van Der Maaten, L.: Certified data removal from machine learning models. In: Proceedings of the 37th International Conference on Machine Learning, pp. 3832–3842 (2020)
2020
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Mitchell, E., Lin, C., Bosselut, A., Manning, C.D., Finn, C.: Memory-based model editing at scale. In: International Conference on Machine Learning, pp. 15817–15831 (2022). PMLR
2022
Later among the works it cites.
2023
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OpenAI: GPT-4 Technical Report (2023)
2023
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Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., Wallace, E.: Extracting training data from diffusion models. In: 32nd USENIX Security Symposium (USENIX Security 23), pp. 5253–5270 (2023)
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Liu, N.F., Zhang, T., Liang, P.: Evaluating Verifiability in Generative Search Engines (2023)
2023
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2020
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Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T.B., Song, D., Erlingsson, U., et al
2021
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Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C.A., Jia, H., Travers, A., Zhang, B., Lie, D., Papernot, N.: Machine unlearning. In: 2021 IEEE Symposium on Security and Privacy (SP), pp. 141–159 (2021). IEEE
2021
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Graves, L., Nagisetty, V., Ganesh, V.: Amnesiac machine learning. Proceedings of the AAAI Conference on Artificial Intelligence 35
2021
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2021
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Dang, Q.-V.: Right to be forgotten in the age of machine learning. In: Advances in Digital Science: ICADS 2021, pp. 403–411 (2021). Springer
2021
Cited alongside, same era.
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A.M., Pillai, T.S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., Fiedel, N.: PaLM: Scaling Language Modeling with Pathways (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Closest in time.
2023
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Yue, X., Inan, H., Li, X., Kumar, G., McAnallen, J., Shajari, H., Sun, H., Levitan, D., Sim, R.: Synthetic text generation with differential privacy: A simple and practical recipe. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1321–1342. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-long.74 . https://aclanthology.org/2023.acl-long.74
2023
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Koch, K., Soll, M.: No matter how you slice it: Machine unlearning with SISA comes at the expense of minority classes. In: First IEEE Conference on Secure and Trustworthy Machine Learning (2023). https://openreview.net/forum?id=RBX1H-SGdT
2023
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Hu, H., Wang, S., Chang, J., Zhong, H., Sun, R., Hao, S., Zhu, H., Xue, M.: A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services (2023)
2023
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2023
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Zhang, D., Pan, S., Hoang, T., Xing, Z., Staples, M., Xu, X., Yao, L., Lu, Q., Zhu, L.: To be forgotten or to be fair: Unveiling fairness implications of machine unlearning methods. AI and Ethics, 1–11 (2024)
2024
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