A Geospatial Perspective on Data Ownership, the Right to be Forgotten, Copyrights, and Plagiarism in Generative AI
Yaron Kanza, Balachander Krishnamurthy, Divesh Srivastava · 2024
Ethical use of data in generative AI is a growing concern. Large generative AI models are trained on pervasive data sets from a variety of sources, where the data records are fused into the models and become inseparable from them. This raises questions regarding data ownership and the use of personal data, artwork, and copyrighted content in generative AI models. Should people and businesses be allowed to request the removal of their data from a model, even if the data were collected in public places? Should the use of data in generative AI vary across different places based on local copyright laws? How should local laws and regulations regarding data misuse and harmful content be enforced? Can people and organizations verify that their data records have been removed from models or are being used properly? In this paper we discuss the geospatial aspects of data ownership in large generative AI models. We present a vision of generative AI applications that are aware of data ownership and location provenance, based on spatio-temporal features of the data and the usage. These aspects of location-aware AI governance could mitigate some of the risks associated with generative AI and support ethical use of it, in both local and global applications.