Rapid advancements in Generative AI (Gen-AI) have brought forth an array of applications for generating text, images, video, audio, and music. While these technologies offer unprecedented creative opportunities, they also raise important questions about the dependency of generative models on prior human creative works and the value of individual training data points on generative model outputs. Understanding this value is crucial for addressing the ethical questions surrounding fair compensation for contributors of copyrighted data.
As the complexities of the space grow, so does the ethical responsibility associated with it. It is vital to have a strong framework that assigns influence to specific data points based on the output the model generates. This framework should not only be technically sound but also easy to comprehend and implement. Moreover, it should be adaptable regardless of the architecture of the model or generative medium and be readily accepted by industry stakeholders, including copyright holders, policy makers, and model developers.
You can read the entire SOMMS.AI white paper here.