As AI models increasingly rely on existing music to learn and create, the industry faces a crucial challenge: how to ensure that the artists, songwriters, and producers whose work fuels these systems are fairly recognized and compensated. This panel brings together experts from the music, tech, and rights sectors to discuss the path from training input to equitable attribution and revenue sharing. How can metadata, licensing frameworks, and attribution technologies help ensure transparency and accountability across the value chain? If a model was trained on a hit song by Taylor Swift as well as songs by lesser-known artists, or users make references to certain songs in their prompts; should all artists be compensated equally? A one-size-fits-all licensing fee may not be fair. For the music industry, attribution technology can help answer questions like: •Which songs in the training data most influenced this output? •How strong was each influence, and how should we quantify it? •What specific musical elements were borrowed or transformed? Moving beyond theory, panelists will explore practical models for responsible AI licensing incl. attribution— aiming to build a music ecosystem where innovation and fairness can truly coexist.