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| '''Machine unlearning''' is a branch of ] focused on removing specific undesired element, such as private data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up. | '''Machine unlearning''' is a branch of ] focused on removing specific undesired element, such as private data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up. | ||
| == History == | == History == | ||
| 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. |
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. | ||
| ==Present== | |||
| Following the deployment of ]s, unlearning is driven by more than just user privacy. The focus has shifted from training small networks on face images to large models trained on data that included also harmful content which needs to be "erased" or forgotten. | |||
| GDPR could not foresee that invention of ]s would make data erasure a challenging task. This challenge later spurred research into “data deletion” and “machine unlearning” and the focus has shifted to unlearning copyrighted material, harmful content, dangerous abilities, and misinformation. | |||
| == References == | == References == | ||
Revision as of 10:41, 10 December 2024
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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unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up.
History
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.
Present
GDPR could not foresee that invention of large language models would make data erasure a challenging task. This challenge later spurred research into “data deletion” and “machine unlearning” and the focus has shifted to unlearning copyrighted material, harmful content, dangerous abilities, and misinformation.
References
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