[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120162-en":3,"doc-seo-120162-105":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":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},120162,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","How Artificial Intelligence Learns: Legal Aspects of Using Data in Machine Learning","Artificial intelligence systems that learn from data require careful legal and quality-aware training, especially when using large datasets containing personal and non-personal information. The document examines how concepts such as data protection, intellectual property, and data justice interact with data quality, fair collection and processing, and non-discrimination. It reviews relevant EU legislative and initiative frameworks, moving from GDPR and IP rules to data governance, the Data Act, open data, and the Artificial Intelligence Act, highlighting a techno-procedural regulatory approach.","Nadia Maccabiani  \nUniversity of Brescia, Italy [nadia.maccabiani@unibs.it](nadia.maccabiani@unibs.it)[ ](nadia.maccabiani@unibs.it)[ORCID: 0000-0003-1183-0854](ORCID: 0000-0003-1183-0854)  \nAnna Podolska  \nUniversity of Gdańsk, Poland [anna.podolska@ug.edu.pl](anna.podolska@ug.edu.pl)[ ](anna.podolska@ug.edu.pl)[ORCID: 0000-0002-5380-9570](ORCID: 0000-0002-5380-9570)  \nEwelina Szatkowska  \nUniversity of Gdańsk, Poland [ewelina.szatkowska@prawo.ug.edu.pl](ewelina.szatkowska@prawo.ug.edu.pl)[ ](ewelina.szatkowska@prawo.ug.edu.pl)[ORCID: 0000-0001-6449-4204](ORCID: 0000-0001-6449-4204)  \n[https://doi.org/10.26881/gsp.2024.4.03](https://doi.org/10.26881/gsp.2024.4.03)  \nHow Artificial Intelligence Learns: Legal Aspects of Using Data in Machine Learning  \nIntroduction  \n“AI is a collection of technologies that combine data, algorithms and computing power.”1 A subset of artificial intelligence (AI) is machine learning, which uses large data sets (personal and non-personal) to find patterns and correlations from which it makes predictions and decisions. In this model (which also includes generative AI such as Chat GPT or Midjourney), AI must be properly trained. Training consists primarily of feeding the system through appropriate datasets, i.e. datasets that are sufficiently diverse, relevant, and representative (also in terms of gender, ethnicity, or age), free of errors, complete in view of the intended purpose of the system, and able to be used legally. Due to the implications of the deployment of data in the creation and use of new technologies, not only does the technical concept of data quality or the legal protection of personal data and intellectual property come into play, but also the more comprehensive concept of data justice.  \nQuestions of data justice have been dealt with by social scientists, from the seminal work of Jeffrey Alan Johnson on open data and information justice2 to the framework for data justice advocated by Linnet Taylor3 and other distinct strands of  \n1 White Paper on Artificial Intelligence – A European approach to excellence and trust, COM/2020/65 final. For an explication of Artificial Intelligence systems and Machine Learning models, when“a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems,” see S. Russel, P. Norvig, Artificial intelligence –A Modern Approach, Hoboken 2021, p. 651.  \n2 J.A. Johnson, From open data to information justice,“Ethics and Information Technology” 2014, vol. 16, no. 4, p. 263 et seq.  \n3 L. Taylor, What is data justice? The case for connecting digital rights and freedoms globally Big,“Data & Society” 2017, vol. 4, no. 2, p. 1 et seq.  \n46 Nadia Maccabiani, Anna Podolska, Ewelina Szatkowska  \nresearch.4 Our goal is not to provide an overview of the various approaches to data justice among social scientists and philosophers. Instead, our objective is to take stock of this ongoing debate in order to highlight certain legal issues that involve aspects encompassed within the multifaceted concept of data justice. More specifically, our focus will be placed on the legal aspects that have recently been addressed by different pieces of EU legislation or EU initiatives. In this regard, the EU legislator has demonstrated an awareness of and an attempt to address concerns related to data ownership, data openness, data re-use, fair data collection and processing, data quality, and non-discrimination: all issues that are explored by researchers in the field of data justice. The EU legislator has done so through various initiatives, ranging from the individual perspective of the GDPR5 and intellectual property provisions, to more recent and collective approaches, in the EU Strategy on Data and the EU Directive on Open Data,6 the EU Regulation on Data Governance,7 the Data Act,8 the Digital Services and Market Acts,9 and the Artificial Intelligence Act (AIA).10 In these act","cbCaim4u3B0T8g6a","https://ap.wps.com/l/cbCaim4u3B0T8g6a","pdf",128562,1,21,"English","en",105,"# Introduction\n## Training, dataset quality, and legal use of data\n## Data justice and its social-scientific foundations\n## EU legal and policy frameworks on data","[{\"question\":\"Why must AI training datasets be legally usable in machine learning?\",\"answer\":\"AI models are trained on datasets, so the datasets must be appropriate for the intended purpose, error-free and complete, and used in a way that complies with legal requirements for personal data protection and intellectual property.\"},{\"question\":\"How does the document connect data justice to legal aspects of machine learning?\",\"answer\":\"It positions data justice as a broader concept that includes concerns such as fair data collection and processing, data quality, non-discrimination, and rights-related issues, then focuses on the legal issues reflected in EU rules and initiatives.\"},{\"question\":\"Which EU instruments are highlighted as addressing data-related legal concerns?\",\"answer\":\"The document references GDPR and intellectual property provisions, then broader initiatives such as the EU Strategy on Data and the Open Data Directive, alongside the Data Governance Regulation, the Data Act, the Digital Services and Market Acts, and the Artificial Intelligence Act.\"}]","How Artificial Intelligence Learns: Legal Aspects of Using Data in Machine Learning | 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must AI training datasets be legally usable in machine learning?","Question",{"text":75,"@type":76},"AI models are trained on datasets, so the datasets must be appropriate for the intended purpose, error-free and complete, and used in a way that complies with legal requirements for personal data protection and intellectual property.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the document connect data justice to legal aspects of machine learning?",{"text":80,"@type":76},"It positions data justice as a broader concept that includes concerns such as fair data collection and processing, data quality, non-discrimination, and rights-related issues, then focuses on the legal issues reflected in EU rules and initiatives.",{"name":82,"@type":73,"acceptedAnswer":83},"Which EU instruments are highlighted as addressing data-related legal concerns?",{"text":84,"@type":76},"The document references GDPR and intellectual property provisions, then broader initiatives such as the EU 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