{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T20:14:22Z","timestamp":1771964062134,"version":"3.50.1"},"reference-count":137,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2025,12,30]]},"abstract":"<jats:p>Generative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent controversies of AI models generating near-replicas of copyrighted material highlight the need to adapt current legal frameworks and develop technical methods to mitigate copyright infringement risks. This task requires understanding the intersection between computational concepts such as large-scale data scraping and probabilistic content generation, legal definitions of originality and fair use, and economic impacts on intellectual property (IP) rights holders. However, most existing research on copyright in AI takes a purely computer science or law-based approach, leaving a gap in coordinating these approaches that only multidisciplinary e!orts can e!ectively address. To bridge this gap, our survey adopts a comprehensive approach synthesizing insights from law, policy, economics, and computer science. It begins by discussing the foundational goals and considerations that should be applied to copyright in generative AI, followed by methods for detecting and assessing potential violations in AI system outputs. Next, it explores various regulatory options influenced by legal, policy, and economic frameworks to manage and mitigate copyright concerns associated with generative AI and reconcile the interests of IP rights holders with that of generative AI producers. The discussion then introduces techniques to safeguard individual creative works from unauthorized replication, such as watermarking and cryptographic protections. Finally, it describes advanced training strategies designed to prevent AI models from reproducing protected content. In doing so, we highlight key opportunities for action and o!er actionable strategies that creators, developers, and policymakers can use in navigating the evolving copyright landscape.<\/jats:p>","DOI":"10.1145\/3787470.3787472","type":"journal-article","created":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:46:21Z","timestamp":1767228381000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI"],"prefix":"10.1145","volume":"27","author":[{"given":"Archer","family":"Amon","sequence":"first","affiliation":[{"name":"Florida International University, Miami, FL, USA"}]},{"given":"Zhipeng","family":"Yin","sequence":"additional","affiliation":[{"name":"Florida International University, Miami, FL, USA"}]},{"given":"Zichong","family":"Wang","sequence":"additional","affiliation":[{"name":"Florida International University, Miami, FL, USA"}]},{"given":"Avash","family":"Palikhe","sequence":"additional","affiliation":[{"name":"Florida International University, Miami, FL, USA"}]},{"given":"Tongjia","family":"Yu","sequence":"additional","affiliation":[{"name":"Goldman Sachs, New York, NY, USA"}]},{"given":"Wenbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Florida International University, Miami, FL, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection","author":"Hossein Aboutalebi","year":"2024","unstructured":"Hossein Aboutalebi et al. 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