Document Actions

Citation and metadata

Recommended citation

Theodoros Chiou, Copyright lessons on Machine Learning: what impact on algorithmic art?, 10 (2020) JIPITEC 398 para 1.

Download Citation

Endnote

%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 398-411

Download

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"
}

Download

RIS

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.
ER

Download

Mods

<mods>
  <titleInfo>
    <title>Copyright lessons on Machine Learning: what impact on algorithmic art?</title>
  </titleInfo>
  <name type="personal">
    <namePart type="family">Chiou</namePart>
    <namePart type="given">Theodoros</namePart>
  </name>
  <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>
  <subject>
    <topic>artificial intelligence</topic>
    <topic>machine learning</topic>
    <topic>text and data mining</topic>
    <topic>algorithmic art</topic>
    <topic>copyright</topic>
    <topic>copyrighted works</topic>
    <topic>Infosoc Directive</topic>
    <topic>DSM Directive</topic>
    <topic>Big Data</topic>
    <topic>reproduction right</topic>
    <topic>adaptation right</topic>
    <topic>copyright exceptions</topic>
  </subject>
  <classification authority="ddc">340</classification>
  <relatedItem type="host">
    <genre authority="marcgt">periodical</genre>
    <genre>academic journal</genre>
    <titleInfo>
      <title>JIPITEC</title>
    </titleInfo>
    <part>
      <detail type="volume">
        <number>10</number>
      </detail>
      <detail type="issue">
        <number>3</number>
      </detail>
      <date>2020</date>
      <extent unit="page">
        <start>398</start>
        <end>411</end>
      </extent>
    </part>
  </relatedItem>
  <identifier type="issn">2190-3387</identifier>
  <identifier type="urn">urn:nbn:de:0009-29-50250</identifier>
  <identifier type="uri">http://nbn-resolving.de/urn:nbn:de:0009-29-50250</identifier>
  <identifier type="citekey">chiou2020</identifier>
</mods>
Download

Full Metadata

JIPITEC – Journal of Intellectual Property, Information Technology and E-Commerce Law
Article search
Extended article search
Newsletter
Subscribe to our newsletter
Follow Us
twitter