In the rapidly advancing field of AI, using proprietary data to train sophisticated models has ignited a complex debate at the intersection of technology, law, and ethics. This thought-provoking session at the forefront of legal technology aims to unravel the intricate web of ethical and regulatory implications surrounding the utilization of owned data in AI development. Join us as we delve into the legal frameworks that govern data use, explore the ethical quandaries posed by data ownership, and examine the responsibilities of AI developers in this dynamic landscape. Discussion topics include: 

• The complex regulatory environment and managing the risks associated with deploying AI models trained on proprietary data
• Intellectual property rights as they pertain to datasets and the implications of using proprietary data in AI training
• Strategies for maintaining transparency in AI training processes and ensuring accountability for the outcomes of AI systems
• Inherent biases in owned datasets and the ethical duty to mitigate discriminatory outcomes in AI applications