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Theodoros Chiou, Copyright lessons on Machine Learning: what impact on algorithmic art?, 10 (2020) JIPITEC 398 para 1.
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%0 Journal Article %T Copyright lessons on Machine Learning: what impact on algorithmic art? %A Chiou, Theodoros %J JIPITEC %D 2020 %V 10 %N 3 %@ 2190-3387 %F chiou2020 %X Nowadays, Artificial Intelligence (AI) is described as “the new electricity”. Current algorithmic innovation allowed the development of software which enables machines to learn and to achieve autonomous decision making, with limited or no human involvement, in a vast number of applications, such as speech recognition, machine translation and algorithmic creation of works (computer generated art), on the basis of a process widely known as Machine Learning (ML). Within the ML context, machines are repeatedly trained by means of specifically designed learning algorithms that use a corpus of examples in the form of data sets as training material. Very often and, especially in the context of algorithmic creativity, the training material is mainly composed by copyrighted works, such as texts, images, paintings, musical compositions, and others. Machine Learning workflow typically involves the realization of (multiple) reproductions of any protected work used as training material. The present paper aims to assess the extent to which the use of copyrighted works for Machine Learning purposes in the field of algorithmic creativity is controlled by the monopolistic power of the copyright rightholder on that work. The answer to this question will be researched in the context of EU copyright law, by examining the content of reproduction right and exceptions possibly applicable in a typical ML workflow in the field of algorithmic art, before making an overall assessment of the current EU regulatory framework for artistic ML projects, as it is shaped after the DSM Directive 2019/790. %L 340 %K artificial intelligence %K machine learning %K text and data mining %K algorithmic art %K copyright %K copyrighted works %K Infosoc Directive %K DSM Directive %K Big Data %K reproduction right %K adaptation right %K copyright exceptions %U http://nbn-resolving.de/urn:nbn:de:0009-29-50250 %P 398-411Download
Bibtex
@Article{chiou2020,
author = "Chiou, Theodoros",
title = "Copyright lessons on Machine Learning: what impact on algorithmic art?",
journal = "JIPITEC",
year = "2020",
volume = "10",
number = "3",
pages = "398--411",
keywords = "artificial intelligence; machine learning; text and data mining; algorithmic art; copyright; copyrighted works; Infosoc Directive; DSM Directive; Big Data; reproduction right; adaptation right; copyright exceptions",
abstract = "Nowadays, Artificial Intelligence (AI) is described as ``the new electricity''. Current algorithmic innovation allowed the development of software which enables machines to learn and to achieve autonomous decision making, with limited or no human involvement, in a vast number of applications, such as speech recognition, machine translation and algorithmic creation of works (computer generated art), on the basis of a process widely known as Machine Learning (ML). Within the ML context, machines are repeatedly trained by means of specifically designed learning algorithms that use a corpus of examples in the form of data sets as training material. Very often and, especially in the context of algorithmic creativity, the training material is mainly composed by copyrighted works, such as texts, images, paintings, musical compositions, and others. Machine Learning workflow typically involves the realization of (multiple) reproductions of any protected work used as training material. The present paper aims to assess the extent to which the use of copyrighted works for Machine Learning purposes in the field of algorithmic creativity is controlled by the monopolistic power of the copyright rightholder on that work. The answer to this question will be researched in the context of EU copyright law, by examining the content of reproduction right and exceptions possibly applicable in a typical ML workflow in the field of algorithmic art, before making an overall assessment of the current EU regulatory framework for artistic ML projects, as it is shaped after the DSM Directive 2019/790.",
issn = "2190-3387",
url = "http://nbn-resolving.de/urn:nbn:de:0009-29-50250"
}
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TY - JOUR AU - Chiou, Theodoros PY - 2020 DA - 2020// TI - Copyright lessons on Machine Learning: what impact on algorithmic art? JO - JIPITEC SP - 398 EP - 411 VL - 10 IS - 3 KW - artificial intelligence KW - machine learning KW - text and data mining KW - algorithmic art KW - copyright KW - copyrighted works KW - Infosoc Directive KW - DSM Directive KW - Big Data KW - reproduction right KW - adaptation right KW - copyright exceptions AB - Nowadays, Artificial Intelligence (AI) is described as “the new electricity”. Current algorithmic innovation allowed the development of software which enables machines to learn and to achieve autonomous decision making, with limited or no human involvement, in a vast number of applications, such as speech recognition, machine translation and algorithmic creation of works (computer generated art), on the basis of a process widely known as Machine Learning (ML). Within the ML context, machines are repeatedly trained by means of specifically designed learning algorithms that use a corpus of examples in the form of data sets as training material. Very often and, especially in the context of algorithmic creativity, the training material is mainly composed by copyrighted works, such as texts, images, paintings, musical compositions, and others. Machine Learning workflow typically involves the realization of (multiple) reproductions of any protected work used as training material. The present paper aims to assess the extent to which the use of copyrighted works for Machine Learning purposes in the field of algorithmic creativity is controlled by the monopolistic power of the copyright rightholder on that work. The answer to this question will be researched in the context of EU copyright law, by examining the content of reproduction right and exceptions possibly applicable in a typical ML workflow in the field of algorithmic art, before making an overall assessment of the current EU regulatory framework for artistic ML projects, as it is shaped after the DSM Directive 2019/790. SN - 2190-3387 UR - http://nbn-resolving.de/urn:nbn:de:0009-29-50250 ID - chiou2020 ER -Download
Wordbib
<?xml version="1.0" encoding="UTF-8"?> <b:Sources SelectedStyle="" xmlns:b="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" > <b:Source> <b:Tag>chiou2020</b:Tag> <b:SourceType>ArticleInAPeriodical</b:SourceType> <b:Year>2020</b:Year> <b:PeriodicalTitle>JIPITEC</b:PeriodicalTitle> <b:Volume>10</b:Volume> <b:Issue>3</b:Issue> <b:Url>http://nbn-resolving.de/urn:nbn:de:0009-29-50250</b:Url> <b:Pages>398-411</b:Pages> <b:Author> <b:Author><b:NameList> <b:Person><b:Last>Chiou</b:Last><b:First>Theodoros</b:First></b:Person> </b:NameList></b:Author> </b:Author> <b:Title>Copyright lessons on Machine Learning: what impact on algorithmic art?</b:Title> <b:Comments>Nowadays, Artificial Intelligence (AI) is described as “the new electricity”. Current algorithmic innovation allowed the development of software which enables machines to learn and to achieve autonomous decision making, with limited or no human involvement, in a vast number of applications, such as speech recognition, machine translation and algorithmic creation of works (computer generated art), on the basis of a process widely known as Machine Learning (ML). Within the ML context, machines are repeatedly trained by means of specifically designed learning algorithms that use a corpus of examples in the form of data sets as training material. Very often and, especially in the context of algorithmic creativity, the training material is mainly composed by copyrighted works, such as texts, images, paintings, musical compositions, and others. Machine Learning workflow typically involves the realization of (multiple) reproductions of any protected work used as training material. The present paper aims to assess the extent to which the use of copyrighted works for Machine Learning purposes in the field of algorithmic creativity is controlled by the monopolistic power of the copyright rightholder on that work. The answer to this question will be researched in the context of EU copyright law, by examining the content of reproduction right and exceptions possibly applicable in a typical ML workflow in the field of algorithmic art, before making an overall assessment of the current EU regulatory framework for artistic ML projects, as it is shaped after the DSM Directive 2019/790.</b:Comments> </b:Source> </b:Sources>Download
ISI
PT Journal AU Chiou, T TI Copyright lessons on Machine Learning: what impact on algorithmic art? SO JIPITEC PY 2020 BP 398 EP 411 VL 10 IS 3 DE artificial intelligence; machine learning; text and data mining; algorithmic art; copyright; copyrighted works; Infosoc Directive; DSM Directive; Big Data; reproduction right; adaptation right; copyright exceptions AB Nowadays, Artificial Intelligence (AI) is described as “the new electricity”. Current algorithmic innovation allowed the development of software which enables machines to learn and to achieve autonomous decision making, with limited or no human involvement, in a vast number of applications, such as speech recognition, machine translation and algorithmic creation of works (computer generated art), on the basis of a process widely known as Machine Learning (ML). Within the ML context, machines are repeatedly trained by means of specifically designed learning algorithms that use a corpus of examples in the form of data sets as training material. Very often and, especially in the context of algorithmic creativity, the training material is mainly composed by copyrighted works, such as texts, images, paintings, musical compositions, and others. Machine Learning workflow typically involves the realization of (multiple) reproductions of any protected work used as training material. The present paper aims to assess the extent to which the use of copyrighted works for Machine Learning purposes in the field of algorithmic creativity is controlled by the monopolistic power of the copyright rightholder on that work. The answer to this question will be researched in the context of EU copyright law, by examining the content of reproduction right and exceptions possibly applicable in a typical ML workflow in the field of algorithmic art, before making an overall assessment of the current EU regulatory framework for artistic ML projects, as it is shaped after the DSM Directive 2019/790. ERDownload
Mods
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<title>Copyright lessons on Machine Learning: what impact on algorithmic art?</title>
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<name type="personal">
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<abstract>Nowadays, Artificial Intelligence (AI) is described as “the new electricity”. Current algorithmic innovation allowed the development of software which enables machines to learn and to achieve autonomous decision making, with limited or no human involvement, in a vast number of applications, such as speech recognition, machine translation and algorithmic creation of works (computer generated art), on the basis of a process widely known as Machine Learning (ML). Within the ML context, machines are repeatedly trained by means of specifically designed learning algorithms that use a corpus of examples in the form of data sets as training material. Very often and, especially in the context of algorithmic creativity, the training material is mainly composed by copyrighted works, such as texts, images, paintings, musical compositions, and others. Machine Learning workflow typically involves the realization of (multiple) reproductions of any protected work used as training material. The present paper aims to assess the extent to which the use of copyrighted works for Machine Learning purposes in the field of algorithmic creativity is controlled by the monopolistic power of the copyright rightholder on that work. The answer to this question will be researched in the context of EU copyright law, by examining the content of reproduction right and exceptions possibly applicable in a typical ML workflow in the field of algorithmic art, before making an overall assessment of the current EU regulatory framework for artistic ML projects, as it is shaped after the DSM Directive 2019/790.</abstract>
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Full Metadata
| Bibliographic Citation | Journal of intellectual property, information technology and electronic commerce law 10 (2020) 3 |
|---|---|
| Title |
Copyright lessons on Machine Learning: what impact on algorithmic art? (eng) |
| Author | Theodoros Chiou |
| Language | eng |
| Abstract | Nowadays, Artificial Intelligence (AI) is described as “the new electricity”. Current algorithmic innovation allowed the development of software which enables machines to learn and to achieve autonomous decision making, with limited or no human involvement, in a vast number of applications, such as speech recognition, machine translation and algorithmic creation of works (computer generated art), on the basis of a process widely known as Machine Learning (ML). Within the ML context, machines are repeatedly trained by means of specifically designed learning algorithms that use a corpus of examples in the form of data sets as training material. Very often and, especially in the context of algorithmic creativity, the training material is mainly composed by copyrighted works, such as texts, images, paintings, musical compositions, and others. Machine Learning workflow typically involves the realization of (multiple) reproductions of any protected work used as training material. The present paper aims to assess the extent to which the use of copyrighted works for Machine Learning purposes in the field of algorithmic creativity is controlled by the monopolistic power of the copyright rightholder on that work. The answer to this question will be researched in the context of EU copyright law, by examining the content of reproduction right and exceptions possibly applicable in a typical ML workflow in the field of algorithmic art, before making an overall assessment of the current EU regulatory framework for artistic ML projects, as it is shaped after the DSM Directive 2019/790. |
| Subject | artificial intelligence, machine learning, text and data mining, algorithmic art, copyright, copyrighted works, Infosoc Directive, DSM Directive, Big Data, reproduction right, adaptation right, copyright exceptions |
| DDC | 340 |
| Rights | DPPL |
| URN: | urn:nbn:de:0009-29-50250 |
