# Machine Unlearning: its nature, scope, and importance for a "delete culture"

arXiv: https://arxiv.org/abs/2305.15242 · HTML: https://mlsanity.com/paper/2305.15242/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Machine unlearning, one of 12 directions in LLM safety and security.
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 24 May 2023.

## Placed in

- LLM safety and security / [Machine unlearning](https://mlsanity.com/safety/machine-unlearning/)

## Builds on (most cited first, 14 of 14)

- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Remember What You Want to Forget: Algorithms for Machine Unlearning](https://mlsanity.com/paper/2103.03279/) (2021)
- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [Machine Unlearning of Features and Labels](https://mlsanity.com/paper/2108.11577/) (2021)
- [Fast Yet Effective Machine Unlearning](https://mlsanity.com/paper/2111.08947/) (2021)
- [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)
- [Backdoor Defense with Machine Unlearning](https://mlsanity.com/paper/2201.09538/) (2022)
- [Hard to Forget: Poisoning Attacks on Certified Machine Unlearning](https://mlsanity.com/paper/2109.08266/) (2021)
- [Certifiable Machine Unlearning for Linear Models](https://mlsanity.com/paper/2106.15093/) (2021)
- [Coded Machine Unlearning](https://mlsanity.com/paper/2012.15721/) (2020)
- [Verifiable and Provably Secure Machine Unlearning](https://mlsanity.com/paper/2210.09126/) (2022)
- [To Be Forgotten or To Be Fair: Unveiling Fairness Implications of Machine Unlearning Methods](https://mlsanity.com/paper/2302.03350/) (2023)
- [An Introduction to Machine Unlearning](https://mlsanity.com/paper/2209.00939/) (2022)

## Built on by (most cited first, 5 of 5)

- [Generative AI in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity](https://mlsanity.com/paper/2401.07348/) (2024)
- [Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research](https://mlsanity.com/paper/2412.06966/) (2024)
- [Position: Bridge the Gaps between Machine Unlearning and AI Regulation](https://mlsanity.com/paper/2502.12430/) (2025)
- [Eternal Sunshine of the Mechanical Mind: The Irreconcilability of Machine Learning and the Right to be Forgotten](https://mlsanity.com/paper/2403.05592/) (2024)
- [Protecting the Neural Networks against FGSM Attack Using Machine Unlearning](https://mlsanity.com/paper/2511.01377/) (2025)

## Abstract (arXiv)

The article explores the cultural shift from recording to deleting information in the digital age and its implications on privacy, intellectual property (IP), and Large Language Models like ChatGPT. It begins by defining a delete culture where information, in principle legal, is made unavailable or inaccessible because unacceptable or undesirable, especially but not only due to its potential to infringe on privacy or IP. Then it focuses on two strategies in this context: deleting, to make information unavailable; and blocking, to make it inaccessible. The article argues that both strategies have significant implications, particularly for machine learning (ML) models where information is not easily made unavailable. However, the emerging research area of Machine Unlearning (MU) is highlighted as a potential solution. MU, still in its infancy, seeks to remove specific data points from ML models, effectively making them 'forget' completely specific information. If successful, MU could provide a feasible means to manage the overabundance of information and ensure a better protection of privacy and IP. However, potential ethical risks, such as misuse, overuse, and underuse of MU, should be systematically studied to devise appropriate policies.