Can Copyright be Reduced to Privacy?

Recent advancements in Machine Learning have sparked a wave of new possibilities and applications that could potentially transform various aspects of our daily lives and revolutionize numerous professions through automation. However, training such algorithms relies heavily on extensive content, either annotated or generated by individuals who may be impacted by these algorithms. Consequently, the identification and determination of when and how content can be used within this framework without infringing upon individuals' legal rights have become a pressing challenge.