[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118000-en":3,"doc-seo-118000-105":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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118000,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Copyright Law and the Lifecycle of Machine Learning Models","Machine learning relies on large data corpora to train models for diverse tasks, yet the underlying training materials—texts, images, and videos—are typically protected by copyright, making licensing and legal status central to adoption. The paper empirically examines three technological settings via case studies and maps the established AI data lifecycle (collection, organization, training, operation) for legal analysis under EU copyright law. It evaluates harmonisation of rights, exceptions, and disclosure, and finds likely outcomes could create a fully copyright-licensed environment affecting industry, innovation, and research.","IIC (2024) 55:110–138  \n[https://doi.org/10.1007/s40319-023-01419-3](https://doi.org/10.1007/s40319-023-01419-3)  \nARTICLE  \nCopyright Law and the Lifecycle of Machine Learning Models  \nMartin Kretschmer . Thomas Margoni . Pinar Oru  \nAccepted: 11 December 2023 / Published online: 1 February 2024  \n􀀂 The Author(s) 2024  \nAbstract Machine learning, a subfield of artificial intelligence (AI), relies on large corpora of data as input for learning algorithms, resulting in trained models that can perform a variety of tasks. While data or information are not subject matter within copyright law, almost all materials used to construct corpora for machine learning are protected by copyright law: texts, images, videos, and so on. There are global policy moves to address the copyright implications of machine learning, in particular in the context of so-called ‘‘foundation models’’ that underpin generative AI. This paper takes a step back, exploring empirically three technological settings through detailed case studies. We set out the established industry methodology of alifecycle of AI (collecting data, organising data, model training, model operation) to arrive at descriptions suitable for legal analysis. This will allow an assessment of the challenges for a harmonisation of rights, exceptions and disclosure under EU copyright law. The three case studies are:  \nThe research was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 870626870626 (reCreating Europe: Rethinking digital copyright law for a culturally diverse, accessible, creative Europe) . Case study 1 was developed with ESRC support for the Urban Big Data Centre (ES/L011921/1) . Pinar Oru prepared a first draft of the case studies as a postdoctoral researcher at CREATe, University of Glasgow.  \nM. Kretschmer (&)  \nProfessor of Intellectual Property Law, School of Law, University of Glasgow; Director of CREATe (UK Copyright and Creative Economy Centre), Glasgow, UK [e-mail: martin.kretschmer@glasgow.ac.uk](e-mail: martin.kretschmer@glasgow.ac.uk)  \n[T. Margoni](T. Margoni)  \nResearch Professor of Intellectual Property Law, Centre for IT and IP Law (CiTiP), Faculty of Law and Criminology, University of Leuven (KU Leuven), Leuven, Belgium  \ne-mail: [thomas.margoni@kuleuven.be](thomas.margoni@kuleuven.be)  \n[P. Oruc](P. Oruc)¸  \nLecturer in Commercial Law, University of Manchester, Manchester, UK  \ne-mail: [pinar.oruc@manchester.ac.uk](pinar.oruc@manchester.ac.uk)  \n1. Machine learning for scientific purposes, in the context of a study of regional short-term letting markets;  \n2. Natural Language Processing (NLP), in the context of large language models;  \n3. Computer vision, in the context of content moderation of images.  \nWe find that the nature and quality of data corpora at the input stage is central to the lifecycle of machine learning. Because of the uncertain legal status of data collection and processing, combined with the competitive advantage gained by firms not disclosing technological advances, the inputs of the models deployed are often unknown. Moreover, the ‘‘lawful access’’ requirement of the EU exception for text and data mining may turn the exception into a decision by rightholders to allow machine learning in the context of their decision to allow access. We assess policy interventions at EU level, seeking to clarify the legal status of input data via copyright exceptions, opt-outs or the forced disclosure of copyright materials. We find that the likely result is a fully copyright-licensed environment of machine learning that may have problematic effects for the structure of industry, innovation and scientific research.  \nKeywords Copyright 􀀂 Artificial intelligence 􀀂 Text mining 􀀂 Data mining 􀀂 EU 􀀂 Digital single market  \n1 Introduction  \nNew data analysis methods are attracting global attention. Machine learning, a subfield of Artificial Intelligence (AI), is seen as a critical technology, in which algorithms are tra","cbCaifZVwZbEBiCH","https://ap.wps.com/l/cbCaifZVwZbEBiCH","pdf",457947,1,29,"English","en",105,"# Introduction\n# Machine Learning for Scientific Purposes\n# Natural Language Processing in Large Language Models\n# Computer Vision for Content Moderation","[{\"question\":\"How does copyright law affect the machine learning lifecycle described in the paper?\",\"answer\":\"Copyright law directly impacts processes involved in data scraping, mining, and learning because training corpora may include works protected by copyright and can trigger reproduction and adaptation rights.\"},{\"question\":\"What role does “lawful access” play in EU text and data mining exceptions?\",\"answer\":\"The paper explains that the lawful access requirement can effectively shift the exception toward decisions by rightsholders about whether machine learning is permitted.\"},{\"question\":\"Which three technological settings are analyzed in the case studies?\",\"answer\":\"The paper examines machine learning for scientific purposes, natural language processing in large language models, and computer vision for image content moderation.\"}]","Copyright Law and the Lifecycle of Machine Learning Models | 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