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Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions

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arxiv 2307.03941 v4 pith:AFU23EWE submitted 2023-07-08 cs.CY cs.AIcs.CL

Dawen Zhang , Pamela Finckenberg-Broman , Thong Hoang , Shidong Pan , Zhenchang Xing , Mark Staples , Xiwei Xu This is my paper · ORCID
classification cs.CYcs.AIcs.CL
keywords rightrtbfchallengesorganizationsdataenginesforgottengoogle
verification ladder T0 review T1 audit T2 compute T3 formal
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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. v AEPD, Mario Costeja Gonz\'alez, and was later included as the Right to Erasure under the General Data Protection Regulation (GDPR) of European Union to allow individuals the right to request personal data be deleted by organizations. Specifically for search engines, individuals can send requests to organizations to exclude their information from the query results. It was a significant emergent right as the result of the evolution of technology. With the recent development of Large Language Models (LLMs) and their use in chatbots, LLM-enabled software systems have become popular. But they are not excluded from the RTBF. Compared with the indexing approach used by search engines, LLMs store, and process information in a completely different way. This poses new challenges for compliance with the RTBF. In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of differential privacy, machine unlearning, model editing, and guardrails. With the rapid advancement of AI and the increasing need of regulating this powerful technology, learning from the case of RTBF can provide valuable lessons for technical practitioners, legal experts, organizations, and authorities.

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Cited by 4 Pith papers

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  1. User Privacy and Large Language Models: An Analysis of Frontier Developers' Privacy Policies

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    All six leading U.S. AI chatbot developers, as of May 2025, appear to train their models on users' chat data by default, often without clear opt-out options.

  2. LLM Unlearning Should Be Form-Independent

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    Existing LLM unlearning is form-dependent; the new ORT benchmark measures this, and the training-free ROCR edit reduces it by redirecting concept representations.

  3. When unlearning is free: leveraging low influence points to reduce computational costs

    cs.LG 2025-12 conditional novelty 5.0 of 10

    Low-influence training points can be dropped from forget/retain sets before unlearning, cutting runtime up to ~50% with little measured loss in accuracy or MIA-based privacy.

  4. GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface

    cs.CL 2025-07 conditional novelty 5.0 of 10

    A single 205M-parameter encoder model unifies named entity recognition, text classification, and hierarchical structured extraction through declarative schemas.

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