Table Of Content© Gintarė Surblytė-Namavičienė 2020
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Gintarè Surblytė-Namavičienė - 9781788116657
Contents
1 Introduction 1
2 Digital economy: between human brains and artificial intelligence 7
3 Trade secret protection for data 55
4 Data- and algorithm-driven economy: issues for competition? 155
5 Regulation beyond competition? 234
6 Conclusions 270
Index 274
v
Gintarè Surblytė-Namavičienė - 9781788116657
Gintarè Surblytė-Namavičienė - 9781788116657
1. Introduction
It is often claimed that the Fourth Industrial Revolution is taking place.1 This
revolution has been said to be “building on the Third” and “characterized by
a fusion of technologies that is blurring the lines between the physical, digital,
and biological spheres”.2
Yet the question is, first of all, what exactly the “Fourth Industrial
Revolution” is supposed to be and whether it can, indeed, be claimed that it
is already taking place. It could be asked whether the claimed effects of such
a revolution are, in fact, comparable with those brought by inventions such as
the steam engine, electricity and computer technology. On the one hand, it is
true that the developments of artificial intelligence (AI) have been significant.
On the other hand, however, intelligent machines on a par with human beings
in terms of their intelligence and sovereignty in dealing with a wide range
of tasks, thereby making decisions on their own and maybe even capable of
autonomously (re)writing their algorithms, do not seem to have arrived yet.
Thus, it might be that the Fourth Industrial Revolution is rather the Hannibal
ante portas, and – for a good reason (?)3 – has not yet arrived.
1 See, for example, Bundesministerium für Wirtschaft und Energie, Memorandum
der Plattform Industrie 4.0, 2015 (https://w ww .bmwi .de/ Redaktion/D E/ Publikationen/
Industrie/ memorandum -plattform -industrie -4 -0 .pdf ? _ _blob = publicationFile & v = 13);
Eine europäische Studie von Roland Berger Strategy Consultants im Auftrag von
BDI (Bundesverband der Deutschen Industrie e.V.), Die digitale Transformation der
Industrie, 2015 (https://b di .eu/ media/u ser _upload/ Digitale_ Transformation. pdf).
2 Schwab, K., The Fourth Industrial Revolution: what it means, how to respond,
14 January 2016, World Economic Forum (https://w ww .weforum .org/ agenda/2 016/
01/ the -fourth -industrial- revolution- what -it -means -and -how -to -respond/ ): “The First
Industrial revolution used water and steam power to mechanize production. The Second
used electric power to create mass production. The Third used electronics and informa-
tion technology to automate production. Now a Fourth Industrial Revolution is building
on the Third, the digital revolution that has been occurring since the middle of the last
century. It is characterized by a fusion of technologies that is blurring the lines between
the physical, digital, and biological spheres.”
3 The risks associated with the developments of AI were highlighted and thus
a word of caution towards such developments was expressed, for example, by Stephen
Hawking: Hawking, S., Stephen Hawking says A.I. could be “worst event in the
history of our civilization”, 6 November 2017 (https://w ww .cnbc .com/ 2017/ 11/ 06/
stephen -hawking -ai -could -be -worst -event -in -civilization. html); Hawking, S., Stephen
Hawking warns artificial intelligence “may replace humans altogether”, 2 November
1
Gintarè Surblytė-Namavičienė - 9781788116657
2 Competition and regulation in the data economy
However, AI and related developments have already caught the attention
of the regulators and academia. For example, a debate was launched by the
European Parliament on the “European civil law rules” applicable to robotics
and artificial intelligence. On 16 February 2017 a European Parliament resolu-
tion was adopted with the recommendations of the Commission on Civil Law
Rules on Robotics.4 In academia, fears have been expressed with regard to the
danger of intelligent machines autonomously colluding on the basis of their
algorithms (the so-called “algorithmic collusion”) and thus posing risks to
competition.5 Studies have been made on whether there is a need to reconsider
some of the concepts of EU competition law owing to the novel features of the
“digital era”.6
Yet it is not a given that economic changes have actually been as revo-
lutionary as they are sometimes claimed to be. On the one hand, it is true
that doing business in an economy that has been coined the data-driven7 or
the algorithm-driven8 economy (in the following, the data economy9) may
be different. On the other hand, however, it can be questioned whether the
2017 (https://w ww .independent. co .uk/ life -style/ gadgets -and -tech/n ews/ stephen -haw
king -artificial -intelligence -fears -ai -will -replace -humans -virus -life -a8034341 .html);
Hawking, S., Stephen Hawking warns artificial intelligence could end mankind, 2
December 2014 (http://w ww .bbc .com/ news/ technology- 30290540).
4 European Parliament resolution of 16 February 2017 with recommendations
to the Commission on Civil Law Rules on Robotics (2015/2103(INL)) (https://w ww
.europarl .europa .eu/ doceo/ document/T A -8 -2017 -0051 _EN .html ?redirect). See also,
European Parliament, Press Release “Robots and Artificial Intelligence: MEPs Call for
EU-Wide Liability Rules”, 16 February 2017 (http://w ww .europarl .europa .eu/ news/
en/ press -room/ 20170210IPR61808/ robots -and -artificial -intelligence -meps -call -for -eu
-wide -liability- rules).
5 See, in particular, Ezrachi, A./Stucke, M.E., Artificial Intelligence & Collusion:
When Computers Inhibit Competition, (2017) University of Illinois Law Review 1775
(Oxford Legal Studies Research Paper No. 18/2015; University of Tennessee Legal
Studies Research Paper No. 267, available at SSRN: https://p apers .ssrn .com/ sol3/
papers. cfm ?abstract_ id = 2591874); Ezrachi, A./Stucke, M.E., Virtual Competition: The
Promise and Perils of the Algorithm-Driven Economy, Cambridge: Harvard University
Press 2016.
6 See the report for the European Commission by Jacques Crémer, Yves-Alexandre
de Montjoye, Heike Schweitzer, “Competition Policy for the Digital Era”, 2019 (http://
ec .europa .eu/ competition/p ublications/r eports/ kd0419345enn .pdf).
7 OECD (2015), Data-Driven Innovation: Big Data for Growth and Well-Being,
OECD Publishing, Paris.
8 Ezrachi, A./Stucke, M.E., Virtual Competition: The Promise and Perils of the
Algorithm-Driven Economy, Cambridge: Harvard University Press 2016.
9 In the following, it is the term “data economy” that is used throughout this book.
It is meant in terms of the economy, where companies do business on the basis of
employing algorithms and using data as an input for such algorithms. Thus, the term
covers both the data-driven and the algorithm-driven economy, since data and algo-
Gintarè Surblytė-Namavičienė - 9781788116657
Introduction 3
fundamental economic principles have changed in a way that would raise
entirely new legal questions, which may, in turn, beg for legal changes. The
clarification of the latter question is of utmost importance, not the least when
it comes to an assessment of any necessary regulatory changes. Therefore, this
book starts with an analysis of the notion of AI and the question whether the
impact of the data economy, from a legal point of view, has indeed been so
revolutionary that the existing legal rules have to be thought over from scratch
(Chapter 2).
For the purposes of this analysis, it may be helpful to revisit to the roots
of the classic economic theories, such as the theory of Adam Smith. Smith’s
book The Wealth of Nations was published at the dawn of the First Industrial
Revolution.10 However, this is not the reason why it should be delved into for
analysis of his economic, and to some extent moral, theory.11 The purpose of
such an analysis is, first and foremost, to clarify whether the economic prin-
ciples on which the data economy functions are in any way different from the
classic economic theory, so that the question can be answered as to whether
there is a need for drastic legal changes. The theory of Adam Smith may help
to explain some of the concepts of the data economy, such as the “privacy
paradox”, and may provide answers to widely debated questions such as the
implications of considering data as a commodity. Importantly, since one of the
most hotly debated questions in the context of the data economy has been the
“free flow of data”,12 and thus the issue of how to enable more access to data,13
analysis of the economic theory of Adam Smith may reveal the downsides of
such free flow of data by showing that it may be exactly the opposite that is
needed for the data economy to function. After all, the role of creating and
maintaining economic incentives for the operators in the markets not only in
rithms are often two sides of the same coin if we consider their broader role in the data
economy.
10 Smith, A., An Inquiry into the Nature and Causes of the Wealth of Nations,
Chicago: The University of Chicago Press 1976.
11 Smith, A., The Theory of Moral Sentiments, London: Henry G. Bohn, York
Street, Covent Garden 1853.
12 Communication from the Commission to the European Parliament, the Council,
the European Economic and Social Committee and the Committee of the Regions
“A Digital Single Market Strategy for Europe”, Brussels, 6.5.2015, COM(2015) 192
final.
13 See, for example, Drexl, J., Designing Competitive Markets for Industrial Data
– Between Propertisation and Access (31 October 2016). Max Planck Institute for
Innovation & Competition Research Paper No. 16-13. Available at SSRN: https:// papers
.ssrn .com/ sol3/ papers .cfm ?abstract_ id = 2862975; Drexl, J., Data Access and Control in
the Era of Connected Devices, Study on Behalf of the European Consumer Organisation
BEUC (https://w ww .ip .mpg .de/ fileadmin/i pmpg/c ontent/a ktuelles/a us _der _forschung/
beuc -x -2018 -121 _data _access _and _control _in _the _area _of _connected_ devices .pdf).
Gintarè Surblytė-Namavičienė - 9781788116657
4 Competition and regulation in the data economy
terms of doing their business, but also in terms of innovating, should not be
undermined. This may be even more important in light of an assumption that
a large part of current competition in the data economy might be so-called
Schumpeterian competition in terms of dynamic competition that takes place
for future (emerging) AI markets. Thus, the purpose of delving into the eco-
nomic theory of Adam Smith is also to better spotlight the issues of the data
economy that may, indeed, be problematic, but which, owing to the attention
of the regulators and academia sometimes being distracted by a focus on spec-
ulations related to artificial intelligence, risk remaining unexamined.
One of the most pressing issues in light of the development of AI technology
is how to strike a balance between the interests that might be at stake, namely,
creating incentives for AI developers, while at the same time protecting con-
sumers who might also be data subjects. In light of this, the following issues
beg for deeper analysis: trade secret protection for data that is a crucial input
for AI, the reach of competition law in the algorithm-driven markets and
the potential risks of so-called “algorithmic manipulation”, which may raise
a need to fill in potential legal gaps.
In fact, as regards the creation of economic incentives and solving the
question of access to data in the business-to-business relationship, it is crucial
to clarify the scope of legal protection that may be available for data and data
sets. In this regard, trade secret protection comes into play (Chapter 3). Trade
secret law protects information that is secret, has commercial value because
it is kept secret and is subject to reasonable steps of the trade secret holder to
protect its secrecy. Owing to the rather broad subject-matter of trade secrets,
this might cover data (data sets) and algorithms. Should this be the case, trade
secret protection may turn out to play a crucial role in the data economy.
On the one hand, the beauty of trade secret protection may be the fact that
it does not grant exclusive rights to trade secret holders. On the other hand,
such protection may also raise legal questions, not least when it comes to the
intersection of trade secret protection and the protection of personal data. If
not only non-personal data may be eligible for trade secret protection, but also
personal data, tensions may arise, for example, when it comes to the exercise
of (at least some of) the rights of the data subjects enshrined in the EU General
Data Protection Regulation (GDPR).14 Thereby, it is crucial to clarify to what
extent the GDPR would be applicable in such cases and how the potential
tensions between trade secret and personal data protection should be solved.
14 Regulation (EU) 2016/679 of the European Parliament and of the Council of
27 April 2016 on the protection of natural persons with regard to the processing of per-
sonal data and on the free movement of such data, and repealing Directive 95/46/EC
(General Data Protection Regulation), OJ [2016] L 119/1.
Gintarè Surblytė-Namavičienė - 9781788116657
Introduction 5
Moreover, although trade secrets can be reverse engineered, the effects of
reverse engineering for the data economy need to be analysed deeper, bearing
in mind the legitimacy of contractual restrictions to reverse engineer under
the EU Trade Secrets Directive.15 Also, the fact that industrial and humanoid
robots may be eligible for trade mark protection under the Nice classification16
raises the need to delve deeper into the intersection of trade secret protection
and functional signs.
Furthermore, bearing in mind that, owing to the role of data, particularly for
developing AI on the basis of “machine learning”, companies compete fiercely
over data, risks may arise of potential restrictions on competition. Although
the issues related to “algorithmic collusion” may still be rather speculative, this
fact does not yet mean that the increasing use of algorithms by business does
not pose any risks to competition. In fact, real-life cases, where algorithms
were used for restricting competition, have already been dealt with by the
European Commission.17 Indeed, the use of pricing algorithms by companies
may need to be scrutinized more closely by competition authorities. After all,
such algorithms may increase the likelihood of algorithmic price adjustments.
Should algorithms act as the multipliers of the price fixed by, for example,
a vertical agreement, horizontal price adjustments may follow with a result
of higher prices in the markets. Thus, a need arises to scrutinize whether and
how EU competition law may tackle competition law problems related to
algorithms (Chapter 4).
EU competition law has tools for dealing with algorithmic price adjustments.
Article 101 of the Treaty on the Functioning of the European Union (TFEU)
condemns primarily anti-competitive agreements and concerted practices,
whereas Article 102 TFEU prohibits abuse of a dominant position, including
cases when such a dominant position is collective. The question is, however,
how these legal tools have been interpreted, in particular in the case-law of the
15 Directive (EU) 2016/943 of the European Parliament and of the Council of
8 June 2016 on the protection of undisclosed know-how and business information
(trade secrets) against their unlawful acquisition, use and disclosure, OJ [2016] L 157/1.
16 The Nice classification, which was established by the Nice Agreement (1957),
is an international classification of goods and services applied for the registration of
marks (see http://w ww .wipo .int/c lassifications/n ice/e n).
17 Commission decision of 24.7.2018 relating to proceedings under Article 101
of the Treaty on the Functioning of the European Union, Case AT.40465 – ASUS;
Commission decision of 24.7.2018 relating to proceedings under Article 101 of
the Treaty on the Functioning of the European Union, Case AT.40469 – Denon &
Marantz; Commission decision of 24.7.2018 relating to proceedings under Article 101
of the Treaty on the Functioning of the European Union, Case AT.40182 – Pioneer;
Commission decision of 24.7.2018 relating to proceedings under Article 101 of the
Treaty on the Functioning of the European Union, Case AT.40181 – Philips.
Gintarè Surblytė-Namavičienė - 9781788116657
6 Competition and regulation in the data economy
European Court of Justice (ECJ). Traditionally, parallel behaviour has been
allowed under EU competition law. Algorithmic price adjustments may often
be the result of such parallel behaviour. However, the guidance provided by
the ECJ in the E-Turas case18 seems to have broadened the EU competition
law’s concept of a concerted practice. In light of the use of pricing algorithms,
the need thereby arises to analyse whether the concept of a concerted practice
may capture the cases of algorithmic price adjustments and what implications
the E-Turas case may have for the data economy.
Finally, it cannot be ruled out that algorithms may be used as a tool for
manipulation. When it comes to the data economy, the question thus is whether
there is a legal gap in this regard and whether it is therefore this area where any
regulatory rules may be mostly needed for tackling the problem of algorithmic
manipulation in the data economy (Chapter 5). In this regard, legal rules that
restrict market abuses in the financial sector, including the prohibition of an
algorithmic manipulation, may stand as an example.19 Furthermore, bearing
in mind the rise of private power and an increasing asymmetry of information
between private companies and consumers, the question is whether a more
robust state control of terms and conditions in the data economy may be
needed, first and foremost, for the sake of consumer protection.
The importance of addressing the aforementioned issues in a timely manner
should not be underestimated. Technological developments are progressing
rapidly and the need for regulation may be immediate. Thereby, shifting the
focus from speculations and loose terms such as artificial intelligence may be
crucial in order not to lose sight of the problems that may need to be tackled
for the overall benefits of a society that may be on the brink of the Fourth
Industrial Revolution.
18 ECJ, Case C-74/14, “Eturasˮ UAB and Others v. Lietuvos Respublikos
konkurencijos taryba, 21 January 2016, ECLI:E U: C: 2016: 42.
19 Directive 2014/65/EU of the European Parliament and of the Council of 15 May
2014 on markets in financial instruments and amending Directive 2002/92/EC and
Directive 2011/61/EU (recast), OJ [2014] L 173/349; Regulation (EU) No. 596/2014 of
the European Parliament and of the Council of 16 April 2014 on market abuse (market
abuse regulation) and repealing Directive 2003/6/EC of the European Parliament and of
the Council and Commission Directives 2003/124/EC, 2003/125/EC and 2004/72/EC;
Directive 2014/57/EU of the European Parliament and of the Council of 16 April 2014
on criminal sanctions for market abuse, OJ [2014] L 173/179.
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