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

TL;DR

This paper discusses the shift towards deleting information in the digital age, emphasizing the importance of Machine Unlearning for privacy and IP protection, and exploring its potential and challenges.

Contribution

It introduces Machine Unlearning as a novel approach to remove specific data from models, addressing privacy and IP concerns in the context of a delete culture.

Findings

Machine Unlearning can potentially enable models to forget specific data.

MU offers a solution to privacy and IP issues in machine learning.

Ethical risks of MU require systematic study.

Abstract

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.