# Position: Bridge the Gaps between Machine Unlearning and AI Regulation arXiv: https://arxiv.org/abs/2502.12430 ยท HTML: https://mlsanity.com/paper/2502.12430/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Machine unlearning, one of 12 directions in LLM safety and security. - Builds on 65 of its 129 references that are in the map. - First posted on arXiv 18 Feb 2025. ## Placed in - LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/) ## Builds on (most cited first, 30 of 65) - [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022) - [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019) - [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023) - [BBQ: A Hand-Built Bias Benchmark for Question Answering](https://mlsanity.com/paper/2110.08193/) (2021) - [Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks](https://mlsanity.com/paper/1911.04933/) (2019) - [TOFU: A Task of Fictitious Unlearning for LLMs](https://mlsanity.com/paper/2401.06121/) (2024) - [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019) - [Amnesiac Machine Learning](https://mlsanity.com/paper/2010.10981/) (2020) - [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020) - [Towards Unbounded Machine Unlearning](https://mlsanity.com/paper/2302.09880/) (2023) - [MUSE: Machine Unlearning Six-Way Evaluation for Language Models](https://mlsanity.com/paper/2407.06460/) (2024) - [Unlearn What You Want to Forget: Efficient Unlearning for LLMs](https://mlsanity.com/paper/2310.20150/) (2023) - [When Machine Unlearning Jeopardizes Privacy](https://mlsanity.com/paper/2005.02205/) (2020) - [On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning](https://mlsanity.com/paper/2110.11891/) (2021) - [The Privacy Onion Effect: Memorization is Relative](https://mlsanity.com/paper/2206.10469/) (2022) - [An Adversarial Perspective on Machine Unlearning for AI Safety](https://mlsanity.com/paper/2409.18025/) (2024) - [Machine Unlearning of Pre-trained Large Language Models](https://mlsanity.com/paper/2402.15159/) (2024) - [Machine Unlearning: Solutions and Challenges](https://mlsanity.com/paper/2308.07061/) (2023) - [What makes unlearning hard and what to do about it](https://mlsanity.com/paper/2406.01257/) (2024) - [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023) - [Towards Adversarial Evaluations for Inexact Machine Unlearning](https://mlsanity.com/paper/2201.06640/) (2022) - [Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy](https://mlsanity.com/paper/2403.01218/) (2024) - [Fast Model Debias with Machine Unlearning](https://mlsanity.com/paper/2310.12560/) (2023) - [Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient](https://mlsanity.com/paper/2405.15304/) (2024) - [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023) - [UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI](https://mlsanity.com/paper/2407.00106/) (2024) - [Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition](https://mlsanity.com/paper/2406.09073/) (2024) - [Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks](https://mlsanity.com/paper/2110.06904/) (2021) ## Built on by (most cited first, 7 of 7) - [Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models](https://mlsanity.com/paper/2601.06162/) (2026) - [Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking](https://mlsanity.com/paper/2601.06163/) (2026) - [Computational Compliance for AI Regulation: Blueprint for a New Research Domain](https://mlsanity.com/paper/2601.04474/) (2026) - [No Concept Escapes the Audit: Auditing-Aware Unlearning for Verifiable Concept Erasure in Diffusion Models](https://mlsanity.com/paper/2610.05401/) (2026) - [Certified Per-Instance Unlearning Using Individual Sensitivity Bounds](https://mlsanity.com/paper/2602.15602/) (2026) - [An Investigation of the NeurIPS and ICML 2025 Position Tracks](https://mlsanity.com/paper/2608.16894/) (2026) - [Counterfactual Certification for Sequential Unlearning with Long-Run Performance Guarantees](https://mlsanity.com/paper/2610.17993/) (2026) ## Abstract (arXiv) The ''right to be forgotten'' and the data privacy laws that encode it have motivated machine unlearning since its earliest days. Now, some argue that an inbound wave of artificial intelligence regulations -- like the European Union's Artificial Intelligence Act (AIA) -- may offer important new use cases for machine unlearning. However, this position paper argues, this opportunity will only be realized if researchers proactively bridge the (sometimes sizable) gaps between machine unlearning's state of the art and its potential applications to AI regulation. To demonstrate this point, we use the AIA as our primary case study. Specifically, we deliver a ``state of the union'' as regards machine unlearning's current potential (or, in many cases, lack thereof) for aiding compliance with various provisions of the AIA. This starts with a precise cataloging of the potential applications of machine unlearning to AIA compliance. For each, we flag the technical gaps that exist between the potential application and the state of the art of machine unlearning. Finally, we end with a call to action: for machine learning researchers to solve the open technical questions that could unlock machine unlearning's potential to assist compliance with the AIA -- and other AI regulations like it.