Unveiling the Complexities of Machine Unlearning: A Comprehensive Review

Generative artificial intelligence (AI) training often involves using large amounts of copyrighted data, which raises concerns about fair use and intellectual property rights. To address these issues, the article proposes a framework for balancing fair use through standardization and transparency in generative AI training.
Standardization is crucial for ensuring that AI models are trained on high-quality, standardized data. This can help to reduce bias and improve the accuracy of the models. Standardization also facilitates the development of interoperable AI systems, which can enhance innovation and collaboration in the field.
Transparency is essential for ensuring that AI developers understand how their models are trained and how they use copyrighted data. Transparent AI systems allow data subjects to make informed decisions about their personal data and intellectual property rights. Transparency also helps to build trust between AI developers, data subjects, and society as a whole.
The article highlights the importance of striking a balance between fair use and intellectual property rights in generative AI training. Fair use provisions allow for limited use of copyrighted material without obtaining permission from the copyright holder. However, these provisions must be applied responsibly to avoid harming the rights of creators and intellectual property owners.
The article concludes by emphasizing the need for further research on standardization and transparency in generative AI training. By developing effective standards and ensuring transparency throughout the development process, we can promote responsible and ethical use of copyrighted data in AI training, while also fostering innovation and collaboration in the field.
In summary, this article discusses the importance of standardization and transparency in generative AI training to balance fair use with intellectual property rights. It highlights the need for responsible application of fair use provisions to avoid harming creators and intellectual property owners, while promoting innovation and collaboration in the field.