# Machine Unlearning: AI and the Art of Forgetting

I am writing on an open problem in artificial intelligence (AI) and machine learning (ML) called machine unlearning. The problem is about how to remove the effect of the data from the model and put it in a state as if the data never has been seen by the model. This is just a definition not a solution.

## Introduction

**Machine unlearning** refers to the process of removing the influence or effect of a specific subset of training data from a machine learning model after the model has been trained.
The goal is to adjust the model such that it is as if the removed data had never been included in the training process, without requiring a complete retraining of the model from scratch.

This is an informal definition of machine unlearning. Let’s define the problem formally.

## Mathematical Formalization

Given a machine learning model $f_\theta$ with parameters $\theta$ trained on a dataset $D$, the model is trained to minimize a loss function $L(\theta; D)$. For example, in supervised learning, this is typically done by finding:

\[\theta^* = \arg \min_{\theta} L(\theta; D)\]where $\theta^*$ represents the optimized model parameters after training on the full dataset $D$.

Now, suppose we want to **unlearn** a specific subset of the dataset $D_u \subset D$.
The goal of machine unlearning is to find a new set of model parameters $\theta’$ such that:

Here, $\theta’$ represents the model parameters after removing the influence of the subset $D_u$. In other words, the aim is to adjust the model to reflect the state it would be in had it been trained only on the remaining data $D \setminus D_u$.

## Challenges

**Computational Complexity**: Exact unlearning is often infeasible for large datasets due to the high cost of retraining. Efficient approximate methods must balance computational cost and accuracy.**Model Architecture**: Different types of models (e.g., neural networks, decision trees, ensemble models) have varying levels of complexity in implementing unlearning.**Verification**: Proving that a model has “forgotten” the data subset $D_u$ is challenging, especially in approximate methods where perfect unlearning is not guaranteed.

## Applications

**Privacy**: Removing sensitive information from a model to comply with privacy regulations (e.g., GDPR).**Data Drift**: Adapting models to changing data distributions by unlearning outdated patterns.**Bias Correction**: Adjusting models to reduce bias introduced by specific subsets of data.**Ethical AI**: Ensuring that models do not perpetuate harmful biases or misinformation.- What else?

I love the idea of machine unlearning but I love the following general problem more.

## General Interesting Problem: Model Editing

Can we find a way to edit the model in a way that we can *remove the effect* of the data or *add new data* without retraining the model from scratch?
Can we guide the believes/knowledge of a generative lanugage model arbitrarily?

It is an interesting problem to think about.

## Important Note

If you have a non-trivial solution, plese let me know. I am interested to know about it. 😜

– Ali