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| ==Present== | ==Present== | ||
| GDPR |
The GDPR did not anticipate that the development of ]s would make data erasure a complex task. This issue has since led to research on "data deletion" and "machine unlearning," with a growing focus on removing copyrighted material, harmful content, dangerous capabilities, and misinformation. | ||
| == References == | == References == | ||
Revision as of 10:42, 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unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, outdated information, copyrighted material, harmful content, dangerous abilities, or misinformation, without needing to rebuild models from the ground up.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
The GDPR did not anticipate that the development of large language models would make data erasure a complex task. This issue has since led to research on "data deletion" and "machine unlearning," with a growing focus on removing copyrighted material, harmful content, dangerous capabilities, and misinformation.
References
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