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dc.contributor.authorJaman, Layan
dc.contributor.authorAlsharabi, Reem
dc.contributor.authorElKafrawy, Passent
dc.date.accessioned2024-06-09T06:07:19Z
dc.date.available2024-06-09T06:07:19Z
dc.date.issued2024-01-16
dc.identifier.citationL. Jaman, R. Alsharabi and P. M. ElKafrawy, "Machine Unlearning: An Overview of the Paradigm Shift in the Evolution of AI," 2024 21st Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, 2024, pp. 25-29, doi: 10.1109/LT60077.2024.10469232.en_US
dc.identifier.doidoi: 10.1109/LT60077.2024.10469232en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1703
dc.description.abstractThe rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledge for smart systems. However, the concept of machine unlearning has emerged as a transformative paradigm shift in the field of AI, due to the amount of false information that have been learned over the past. Machine unlearning refers to the ability of AI systems to reverse or discard previously acquired knowledge or patterns, enabling them to adapt and refine their understanding in response to changing circumstances or new insights. This paper explores the concept of machine unlearning, its implications, methods, challenges, and potential applications. The paper begins by providing an overview of the traditional learning-based approaches in AI and the limitations they impose on system adaptability and agility. It then delves into the concept of machine unlearning, discussing various techniques and algorithms employed to remove or modify learned knowledge from AI models or datasets.en_US
dc.description.sponsorshipEffat Universityen_US
dc.publisherIEEEen_US
dc.subjectmachine unlearning , artificial intelligence , data deletion , differential privacy , adaptive algorithmsen_US
dc.titleMachine Unlearning: An Overview of the Paradigm Shift in the Evolution of AIen_US
refterms.dateFOA2024-06-09T06:07:20Z
dc.contributor.researcherDepartment Collaborationen_US
dc.contributor.labVirtual Reality Laben_US
dc.subject.KSAICTen_US
dc.contributor.ugstudent2en_US
dc.contributor.alumnae0en_US
dc.title.projectData mining an NLPen_US
dc.source.indexScopusen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.pgstudent0en_US
dc.contributor.firstauthorJaman, Layan
dc.conference.locationJeddah, KSAen_US
dc.conference.name2024 21st Learning and Technology Conference (L&T),en_US
dc.conference.date2024-01-15


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