How to unlearn a learned Machine Learning model ?

TL;DR

This paper introduces an algorithm for unlearning specific data from trained machine learning models, providing a mathematical framework and metrics to evaluate the effectiveness of the unlearning process.

Contribution

It presents a novel algorithm for unlearning in machine learning models along with theoretical analysis and evaluation metrics.

Findings

The algorithm effectively removes influence of undesired data.

Mathematical theory underpinning the unlearning process is established.

Metrics for assessing unlearning success are proposed.

Abstract

In contemporary times, machine learning (ML) has sparked a remarkable revolution across numerous domains, surpassing even the loftiest of human expectations. However, despite the astounding progress made by ML, the need to regulate its outputs and capabilities has become imperative. A viable approach to address this concern is by exerting control over the data used for its training, more precisely, by unlearning the model from undesired data. In this article, I will present an elegant algorithm for unlearning a machine learning model and visualize its abilities. Additionally, I will elucidate the underlying mathematical theory and establish specific metrics to evaluate both the unlearned model's performance on desired data and its level of ignorance regarding unwanted data.