Vershachi Unlearning: A Framework for Machine Unlearning

In the contemporary landscape of the digital world where industry relies on the technology of artificial intelligence which fundamentally depends on the concepts of machine learning. Machine learning is a field where it utilizes the immense amount of data and then feeds this data into a structure called models. This data “trains” this model.

Abundant data is used to train these models, for this data to be as accurate as it can be optimally. However, reliance on this abundant data exposes us to a significant risk to user privacy which is a matter of concern. It directly challenges the existence of “right to be forgotten”.

There is an intricate relation between the model and the data with which it is trained. Traditional data management systems can easily erase user information from databases, but the scenario becomes considerably complex with machine learning models. This gives rise to the whole new concept called machine unlearning.

This project addresses this challenge by developing a standalone tool and API specifically designed to facilitate the forgetting of data by machine learning models. Our objective is to pioneer a practical approach to enhance user privacy in the context of machine learning technologies. By creating an efficient and reliable solution, we aim to bridge the gap between data privacy rights and the intricate workings of machine learning models.

Through this endeavor, we contribute to the evolving discourse on privacy, data security, and ethical AI practices in the digital age.