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| Machine unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up. | '''Machine unlearning''' is a branch of machine learning focused on removing specific undesired element, such as private data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up. Early research efforts were largely motivated by Article 17 of the ], the European Union's privacy regulation commonly known as the "right to be forgotten" (RTBF), introduced in 2014. | ||
| == References == | == References == | ||
| {{Reflist}} | {{Reflist}} | ||
Revision as of 09:29, 10 December 2024
Machine unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up. Early research efforts were largely motivated by Article 17 of the GDPR, the European Union's privacy regulation commonly known as the "right to be forgotten" (RTBF), introduced in 2014.
References
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