[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83515-en":3,"doc-seo-83515-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83515,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","What’s a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music","Advances in generative AI are rapidly increasing the quality and commercial value of generated music, which depends on large catalogs of creators’ recordings. This raises a platform-design question: how to compensate creators when their works train generative models that produce commercial outputs. A framework links each creator’s payment to a data-attribution score, using informativeness (signal-to-noise) to select welfare-optimal royalty or fixed-fee licensing. Empirical tests show noisy attribution shifts payments toward fixed fees and reduces welfare for both creators and platforms.","arXiv :2607 .0064 1v 1 [ cs .CY] 1 Jul 2026  \nWhat’s a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music  \nLuyang Zhang 1 ∗ Xirui Jiang2 ∗ Junwei Deng3  \nBeibei Li 1 Jiaqi W. Ma3 Chris Donahue 1  \n1 Carnegie Mellon University 2University of Michigan, Ann Arbor  \n3University of Illinois Urbana-Champaign  \n{luyangz, [beibeili}@andrew.cmu.edu](beibeili}@andrew.cmu.edu)  \n[xirui@umich.edu](xirui@umich.edu)  \n{junweid2, [jiaqima}@illinois.edu](jiaqima}@illinois.edu)  \nAbstract  \nAdvances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators’recordings. This raises a central question for platform design: how should creators be compensated when their work is used to train generative AI models that in turn produce commercial outputs? We develop a framework for fairly compensating creators in generative-music markets, where each creator’s payment depends on a data-attribution score estimating their contribution to model outputs. Compared to past compensation frameworks, our framework has two unique considerations:  \n(1) attribution is traced to entire creator catalogs, not individual songs, and (2) the informativeness (signal-to-noise ratio) of the attribution score is an input to the payment mechanism. The framework yields a closed-form payment rule per creator and measures the welfare cost of inaccurate attribution for both creatorsand the platform. Whether the welfare-optimal contract is royalty-based or takes the form of fixed-fee licensing depends on how informative attribution is for that creator’s catalog. We show that better attribution translates directly into welfare gains for both creators and the platform, yet under multi-platform competition a platform only captures gains from attribution improvements when its signal becomes the most precise in the market. To ground our framework in empirical behavior, we train acoustic and symbolic music generation models and measure the informativeness of scalable attribution techniques against a leave-one-catalog-out ground truth. Our experiments reveal that noisy attribution signals push payment toward fixed-fee licensing and diminish welfare for both creators and the platform, providing an economic motivation for further research on improved attribution.  \n1 Introduction  \nGenerative music models trained on copyrighted recordings are reshaping how music is produced and consumed. As their output capabilities have advanced, AI music is crowding music streaming services [16], copyright disputes have emerged [51], and regulators have responded with disclosure mandates [23], suggesting the absence of a reliable and fair compensation rule. Training-data provenance is rarely disclosed, and licensing, where it exists, is negotiated at the catalog or genre level for flat fees that ignore two key factors: (1) that some data contributes more than others for a given generated output [38], and (2) that flat licensing fees do not grow proportionally to revenue  \n∗Equal contribution.  \nPreprint.  \nfrom generated outputs. This raises a central question: how should creators be compensated when their work trains generative AI models that are then used commercially?  \nAnswering this question requires combining two technical fields. The first is data attribution, which estimates how much each creator’s catalog contributed to a given model output. The second is payment design, which turns those estimates into contracts that handle noise, risk, and creator participation. For data attribution, machine-learning methods [27, 42, 37] can estimate each creator’s contribution, but the estimates carry noise that varies by creator and by method. Past work in data attribution for music AI [6, 12, 17] seek to measure or reduce this noise, but do not factor it into the payment mechanism. For payment design, contract theory [35] supplies general tools for payment under noisy signals, but","cbCaipMbI0fPhvOx","https://ap.wps.com/l/cbCaipMbI0fPhvOx","pdf",812903,3,1,29,"English","en",105,"# Abstract\n# Introduction\n## Attribution as the basis for compensation\n## Payment design under noisy attribution\n## Framework implications and welfare results\n## Empirical grounding with generation models","[{\"question\":\"What is the core compensation problem addressed for generative music platforms?\",\"answer\":\"How to fairly compensate music rights holders when their recordings are used to train generative AI models that later generate commercial outputs.\"},{\"question\":\"How does the proposed framework determine a creator’s payment?\",\"answer\":\"Each creator’s payment depends on a data-attribution score estimating the creator catalog’s contribution to model outputs, combined with the attribution score’s informativeness.\"},{\"question\":\"Why does attribution informativeness affect whether payments are royalty-based or fixed-fee licensing?\",\"answer\":\"The welfare-optimal contract form depends on how precise the attribution score is: royalty-based when attribution is accurate, and fixed-fee licensing when attribution is 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