| dc.description.abstract |
Generally when users share information about themselves on some online platforms, they knowingly or unknowingly allow this data to be used by the companies behind these companies for various purposes including selling this information to advertisers as well as using it to better enrich their predictive models. In the event of the user changing their minds on allowing such data about them to be able to be used by the companies, it becomes a strenuous task for the companies to get rid of the influence of this collected data, especially when it has been used to train their machine learning models. Recent legislations by governing bodies, like the European Union, grant people the right to choose where data about them may be used, including a right to have their data and its resulting influence be completely removed from a company’s databases and machine learning models. To be able to do this at scale new machine unlearning solutions need to be invented. In this paper, we look at some of these early models of machine unlearning strategies that have been proposed. |
en_US |