Hybrid artifact concept proposal for intellectual products supported by generative adversarial networks

As AI technologies continue to rapidly develop and be utilized in various fields like transportation, healthcare, education, and art, there has been a growing need for a legal framework that can keep up with the pace of technological advances and the resulting legal issues. Significant developments in AI for the purpose of creating visual products and art have highlighted the need to examine the processes involved. This thesis examines the technical stages of digital art created using deep convolutional generative adversarial networks, a deep learning algorithm, within the context of intellectual property law. A subset of 6,989 abstract and portrait images from the Wiki-Art dataset were used. The results showed that the number of images in the dataset and the originality of the outputs were affected, and the results were dependent on the datasets used. The creation process of conventional art and digital art were compared. As a result of the research, a new concept was proposed that could be accepted by interdisciplinary fields, which combines law and technology, called a "hybrid artifact".