Copyright lessons on Machine Learning: what impact on algorithmic art?

• Author: Theodoros Chiou
• Position: Post-Doc Researcher at the University of Athens, School of Law (Department of Private Law) and Attorney-at-law (IPrights.GR)
• Pages: 398-411

2020

Theodoros Chiou

398

3

Copyright lessons on Machine Learning:

what impact on algorithmic art?

by Theodoros Chiou*

© 2019 Theodoros Chiou

Everybody may disseminate this ar ticle by electronic m eans and make it available for downloa d under the terms and

conditions of the Digital P eer Publishing Licence (DPPL). A copy of the license text may be obta ined at http://nbn-resolving.

de/urn:nbn:de:0009-dppl-v3-en8.

Recommended citation: Th eodoros Chiou, Copyright lessons on Machine Lea rning: what impact on algorithmic art? 10 (2019)

JIPITEC 398 para 1

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

Machine Learning workflow typically involves the re-

alization of (multiple) reproductions of any protected

work used as training material. The present paper

aims to assess the extent to which the use of copy-

righted works for Machine Learning purposes in the

field of algorithmic creativity is controlled by the mo-

nopolistic 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 al-

gorithmic 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: Nowadays, Artificial Intelligence (AI) is de-

scribed as “the new electricity”. Current algorithmic

innovation allowed the development of software

which enables machines to learn and to achieve au-

tonomous decision making, with limited or no human

involvement, in a vast number of applications, such

as speech recognition, machine translation and algo-

rithmic 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 creativ-

ity, the training material is mainly composed by copy-

righted works, such as texts, images, paintings, musi-

cal compositions, and others.

A. Introduction

1

The objective of Making machines intelligent.

Articial Intelligence may be seen from different

standpoints and receive accordingly different

interpretations. From a rather technical point of

view1, Articial intelligence is the eld of computer

* Dr. Theodoros Chiou is Post-Doc Researcher at the University

of Athens, School of Law (Department of Private Law)

and Attorney-at-law (IPrights.GR). Email: Theodoros.

chiou@iprights.gr. This paper is based on a conference

presentation delivered by the author during the 9th ICIL

Conference, “Psychological and socio-political dynamics

within the Web: new and old challenges to Information Law

and Ethics”, held in Rome, Italy, July 11-13, 2019.

1 For a different approach, see among others Stuart Russell

& Peter Norvig, Articial Intelligence: A Modern Approach (3rd

ed., Pearson 2010) 1: “the study of agents that exist in an

environment and perceive and act”.