arXiv Machine Learning

What's a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music

arXiv:2607. 00641v1 Announce Type: cross Abstract: Advances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators' recordings.

arXiv AI
Jun 11

Market Design for AI: Beyond the Copyright Binary

arXiv:2606. 12260v1 Announce Type: cross Abstract: How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality content creation?

By Yan Dai, Maryam Farboodi, Negin Golrezaei, Sepehr Shahshahani
arXiv AI
Jul 21

Content Creation with Spillovers: An Incentive Design Approach

arXiv:2603. 14372v2 Announce Type: replace Abstract: The rise of AI amplifies the economic phenomenon of \emph{positive spillovers}: when creators contribute content that can be reused and adapted by LLMs, one creator's effort may improve the content quality of others through recombination.

By Sagi Ohayon, Boaz Taitler, Omer Ben-Porat
arXiv AI
Aug 10

MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms

arXiv:2603. 02184v2 Announce Type: replace-cross Abstract: Multi-attribution learning (MAL), which enhances model performance by learning from conversion labels yielded by multiple attribution mechanisms, has emerged as a promising learning paradigm for conversion rate (CVR) prediction.

By Jinqi Wu, Sishuo Chen, Zhangming Chan, Yong Bai, Lei Zhang, Sheng Chen, Chenghuan Hou, Xiang-Rong Sheng, Han Zhu, Jian Xu, Bo Zheng, Chaoyou Fu
arXiv Machine Learning
Jun 2

SurrogateSHAP: Training-Free Contributor Attribution for Text-to-Image (T2I) Models

arXiv:2601. 22276v2 Announce Type: replace Abstract: As Text-to-Image (T2I) diffusion models are increasingly used in real-world creative workflows, a principled framework for valuing contributors who provide a collection of data is essential for fair compensation and sustainable data marketplaces.

By Mingyu Lu, Soham Gadgil, Chris Lin, Chanwoo Kim, Su-In Lee