=Paper=
{{Paper
|id=Vol-2737/long paper05
|storemode=property
|title=Ethical Justification of the Value Basis of the European Data Economy Ecosystems
|pdfUrl=https://ceur-ws.org/Vol-2737/FP_5.pdf
|volume=Vol-2737
|authors=Minna M. Rantanen,Juhani Naskali,Kai K. Kimppa.,Koskinen Jani
|dblpUrl=https://dblp.org/rec/conf/tethics/RantanenNKK20
}}
==Ethical Justification of the Value Basis of the European Data Economy Ecosystems==
https://ceur-ws.org/Vol-2737/FP_5.pdf
Proceedings of the Conference on Technology Ethics 2020 - Tethics 2020
Ethical justification of the value basis of the European
data economy ecosystems
Long paper
Minna M. Rantanen 1 [0000-0001-8832-5616], Juhani Naskali1 [0000-0002-7559-2595],
Kai K. Kimppa1 [0000-0002-7622-7629] and Jani Koskinen1 [0000-0001-8325-9277]
1University of Turku, Turku School of Economics, Turku, Finland
minna.m.rantanen@utu.fi
Abstract. Data collection from individuals has become an integral part of
society and an asset for global business. This business is realised through data
economy ecosystems, which currently are orchestrated from the viewpoint of
business. There exist initiatives that aim to change the current situation. One
critical component that is missing from data economy ecosystems and
development is the values of individuals. To achieve an ethically acceptable
data economy, we need to investigate the values of individuals whose data is
used. Likewise, those values should be contested, as all values that people may
have are not ethically acceptable or possible to implement on a societal level. In
this paper, we ethically analyse the individual values that were collected via
survey from four European countries. The analysis is based on the three main
branches of ethics: Consequentialism, Deontology and Virtue Ethics. It seems
that values that individuals have concerning fair data economy are ethically
justified and thus should be respected and implemented in policies concerning
data economy.
Keywords: Data economy, Ecosystems, Values, Ethics
1 Introduction
We live in a data-driven society, where collection and analysis of data are constant
and pervasive. There is a continuous development of data analytics and technology to
gather data, which creates new business and industrial domains as well as renews old
ones. Thus, data is essential to all aspects of the economy. Also, contemporary
business networks enable widespread use and reuse of data. Combining data from
different sources can be used to enrich the information to create new value.
This development has led to the emergence of data ecosystems, where a set of
actors is working together; directly or indirectly consuming, producing or providing
data and other related resources (Oliveira & Lóscio, 2018). Data ecosystems can have
many forms depending on what is collected, by who and to what extent. Big data,
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
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open data, governmental data, small data and personal data can all have their own
economies which can also be interconnected (Thinyane, 2017).
Especially personal data have become increasingly valuable in the 21st century. In
2011 World Economic Forum called personal data a new asset class that represents
post-industrial opportunities affecting all aspects of societies (World Economic
Forum, 2011). According to the European Commission (2019), personal data is
information that is associated with a specific individual. Thus, personal data includes
anonymous information that can lead to re-identification. This definition can also be
extended to user-generated content such as blogs, comments, photos, videos, and
behavioural data, such as search history, as well as social data, such as contacts on
social networking sites (OECD, 2013).
Benefits of personal data economies are assumed to be plentiful. Collection of
personal data can provide the businesses additional insight into their clientele and
help to provide more personalised products or services that could create more value
for the customers as well. However, to fully unlock the potential of the personal data,
there is a need for a balanced ecosystem with increased trust between individuals,
governments and the private sector. (World Economic Forum, 2011.) Unfortunately,
this building of trust has not been successful due to privacy scandals revealed in the
past few years.
In this paper, we focus on the European data economy ecosystems developed
within the European Union (EU). The EU is aiming for rebuilding the trust towards
data ecosystems through human-centric data economy (European Commission, 2020).
There is increasing interest in the human-centricity in data economy research.
However, the individuals are still often seen merely as data subjects and their active
role in the data ecosystems is rarely noted (Koskinen et al. 2019). If we genuinely
want to develop the data economies human-centric way, we should not limit ourselves
into this view. Instead, we should try to actively acknowledge the individuals and
their needs in the development of these systems and govern them accordingly. Thus,
we focus on the individuals that are part of data economy ecosystems.
Currently, data economy ecosystems are based on the institutional values of
platform orchestrators, such as companies and governments. Since individuals should
also be acknowledged, we should consider their values and aim for value congruence
in data economy ecosystems. However, not all values are moral values or equally
important. As Nietzsche (1913) argues, we should not take (moral) values as granted
but be critical towards them and think about why they are good. Therefore, we should
not take the values of Europeans as given. In other words, we need to justify the
values that should be applied in data economies ecosystems. Thus, the research
question of this paper is:
RQ: Are the values of the European individuals ethically justified to be the basis of
the European data economy ecosystem?
We answer this research question by analysing values that Europeans have
expressed towards fair data economy from an ethical perspective. This research
continues the work of Rantanen (2019). She noted that the Finnish, the French, the
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German and the Dutch seem to have somewhat consistent values in this context
(Rantanen, 2019). We analyse these values through three major ethical branches:
Utilitarianism, Deontology and Virtue Ethics. The aim is to find out which of the
values are ethically justifiable and whether they are instrumental or intrinsic in
reaching a good data economy ecosystem. This analysis paves the way for the
practical implementation of values in the human-centric data economy. It also
contributes to the emerging field of data economy ecosystems from ethical and
societal perspectives.
The paper is structured as follows: the next section handles the background for data
economy ecosystem and the values behind them. In section three, we analyse human
values implicated by Rantanen (2019) through the three main ethical theories. In
section four, we compare and discuss the results of the analyses. Finally, we conclude
in section five with the results of the complete analysis and future research.
2 Background
2.1 Data economy ecosystems
Focus on this paper is on data economy ecosystems which use personal data. Data
economy or data ecosystems as research field are still in their infancy, and thus, there
is no consensus about the terms. Hence, we need to define what we are discussing in
this paper. A data economy can be interpreted as an institution of data resource
management. The European Commission (European Commission, 2017) describes
data economy as something that is characterised by an ecosystem of different types of
market players collaborating to ensure that data is accessible and usable to extract
value from data. This definition is a rather abstract way of describing data economy
and its ecosystems.
An ecosystem is a metaphor used to describe complex systems that are ever-
chancing. Oliveira et al. (2019) define data ecosystems as “socio-technical complex
networks in which actors interact and collaborate with each other to find, archive,
publish, consume, or reuse data as well as to foster innovation, create value, and
support new businesses.” (Oliveira et al., 2019, p. 1.). This definition extends the
previous definition by acknowledging the socio-technical nature of these systems and
being more detailed with the actions that are done with and to data.
As it might be noted, there is not much difference between terms data economy
and data ecosystem. If a data economy is seen as something that is characterised by
the ecosystem of collaborating market players and a data ecosystem is a socio-
technical network of actors, then it seems arbitrary to separate these concepts. It is
more accurate to talk about data economy ecosystems when we are talking about
socio-technical system around a data economy and its value creation processes as a
whole. It must be noted that a data economy ecosystem also incorporates official and
unofficial rules that direct the actions in it (Koskinen et al., 2019). Thus, the data
economy ecosystems have also a normative side that regulates the action in them.
Personal data is one of data types collected, stored, traded and analysed in data
economy ecosystems. Personal data is information that is associated with a particular
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individual (European Commission, 2019). In a data economy or data ecosystem
literature, the individuals whose data is in concern are often called “data subjects”. A
data subject is any person whose personal data is being collected, held or processed
(EU GDPR Compliant, 2020). Although this term is precise in a legal sense, it does
make the individuals seem like passive instances that are mere resources of the
personal data. However, this is not the case, since personal data economy ecosystems
also require cooperation from individuals. For example, giving consent to use
personal data and disclosing correct information depends on the individuals and their
willingness. Individuals are also consumers of the products and services affected by
data analysis. For instance, Aguilera et al. (2017) call individuals prosumers of data
economy – hybrids of producer and consumer. Thus, it should be acknowledged that
individuals are not mere data cattle, but active actors of the data economy ecosystems.
Lately, personal data and ways that they have been used in the data economy
ecosystems have provoked a lot of discussion and distrust. Cases such as the
Cambridge Analytica scandal have shown the dark side of current practices in data
economy ecosystems. Micro-targeting voters based on personal data analytics has also
showcased how personal data economy ecosystems can be used to shape whole
societies (Papakyriakopoulos et al., 2018). Thus, critical issues of data economy
ecosystems are not just about the individuals but also about societies.
These discussions have raised awareness both about the value of personal data as
well as ethical issues related to the data economies. People have declared that they do
not trust tech companies with their data. However, for instance, in the case of
Facebook, this distrust has not made people vanish from the service en masse.
Facebook’s robust marketplace with little to no competition undoubtedly plays its part
(see, e.g. Härkönen et al., 2019), but not rebuilding the trust makes their position
more easily disturbed if and when more trustworthy companies enter the markets.
The EU has been aiming to rebuild trust through human-centric data economy,
where individuals (data subjects) have more power over their personal data (European
Commission, 2020). However, the research on this field seems scarce and superficial.
Ethical issues of data ecosystem governance have been handled, but mainly as
mentions (Rantanen et al., 2019). Hence it seems that there is a need for a more
holistic approach that acknowledges the individuals as well as ethical aspects of data
economy ecosystems.
2.2 Values as the basis of human-centric data economy
Technology is never value-free (Kling, 1984; Nissenbaum, 2001). Values direct our
actions and decisions on an individual level. Thus, values also affect the development
of artefacts such as technology. Values build into the technology are often related to
reliability, efficiency and correctness, although there are plenty of stakeholders whose
values could and should be considered (Friedman et al., 2017). Thus, if we want to
design and govern human-centric data economy ecosystems, we should consider the
values of the human-beings as a basis of these ecosystems instead of the traditional
values of institutions or businesses.
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Values have been studied in the context of technology and information system both
in theory and practice (see, e.g. Brey, 2017; Rose et al., 2015), but there is still little
knowledge about the values of individuals who are not employees. Thus, in the
context of data economy ecosystems, we should study the neglected stakeholder
group of individuals who are both consumers of services and producers of data. We
should aim to find the value basis for data economy ecosystems that is in congruence
with their values to assure that a data economy ecosystem is indeed fair to all.
In this paper, we continue the work done by Rantanen (2019). She studied the
values of the Finns, the French, the German and the Dutch in the context of a fair data
economy. She noted that European individuals expressed rather similar values
towards fair data economy in seven different themes. The seventh theme “Negative
attitudes towards data economy and data sharing” did not provide enough information
about values to be analysed further. (Rantanen, 2019.) These values and themes that
they are related to are presented in Table 1.
Table 1 Value themes and basic values of Europeans (Rantanen, 2019)
It must be noted that the values presented are based on Schwartz’s theory of basic
values. Schwartz (2012) has found that ten universal values can be found in any
culture. These values can be divided into four categories: conservation, self-
enhancement, openness to change and self-transcendence. (Schwartz, 2012.)
Rantanen (2019) noted that all value categories Schwartz’s (2012) theory are
represented in values of Europeans in the data economy context. However, values of
tradition, achievement and stimulation were not identified from the answers. Based on
these results, it seems that power and self-direction are the most common values. In
addition to the basic values, there were clear indications of individuals valuing
autonomy, privacy and justice as part of a fair data economy. (Rantanen, 2019.)
It must be acknowledged that despite the existence of universal values there are
and will be differences between values of different cultures and even differences
between individuals’ values and the cultural values of their society (see, e.g.
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Schwartz, 1999). There are several reasons why we study values from the individual
level and aim to generalise them into the “cultural values” of the European data
economy. First, we limit our view on Europe, because here the development of own
data economy ecosystem is under development and here the cultural values, in
general, are rather close to each other. Likewise, the European approach – which
emphasises individuals rights1 – can be seen as a needed counterforce for the
American and Asian model for the data economy. Second, individual values in regard
of the fair data economy ecosystem are rather similar to each other (Rantanen, 2019),
which can indicate that we could reach a common and justifiable value basis of data
economy ecosystems in Europe.
Additionally, values are often in close connection to each other, and it is possible
that some values are instrumental to other or values. For instance, transparency can be
seen as an instrumental value since it makes other values possible in the context of
data economy ecosystems. (Rantanen, 2019.) Likewise, values can create tensions
that should be taken into account (Friedman, 2017). For instance, enforcing security
in technology can lead to the diminishing of ease-of-use or vice versa. Thus, there is a
need to find a delicate balance between different values.
Naturally, the values implemented in technology should be good (see Brey, 2017).
In order to find out which values are good, some justification is necessary because not
all values are moral values. Moral values can be described as values that have some
“oughtness” in them (Kant, 1788; Rokeach, 1973). For example, universalism and
benevolence are often considered as moral values. However, the moral nature of
values, such as power, depends on the context and how the values affect actions. In
the next section, we focus on the ethical justification of values by briefly analysing
the values found by Rantanen (2019) through three major ethical theories. Each of
these theories has a different view on what is good, and even high-level analysis
should show whether or not these values can be justified to be the basis of European
data economies.
3 Analysis through three major ethical theories
As noted in the introduction, values can be ethically justified or not. Thus, to know
what values are worth supporting, we need to conduct an ethical analysis of the values
to justify them. We will analyse the values from following three ethical branches:
Consequentialism, Deontology and Virtue Ethics which are the “big three” ethical
theories and thus provide a proper basis for ethical analysis.
1
See European Commission (2007). “Communication from the commission to the
european parliament, the council, the european economic and social committee and
the committee of the regions "building a european data economy" COM/2017/09 final
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3.1 Consequentialism
Consequentialism defines good through the consequences of actions, and
Utilitarianism specifically states the greatest good to be that which brings the greatest
happiness to the community (Bentham, 1829). While there are various formulations
and tweaks of Bentham’s Greatest Happiness Principle, the original gives a simple yet
effective framework for a high-level examination of basic values. Consequentialism,
by its nature, does not work in absolutes – good and bad are quantifiable, and as such
it will provide an understandable way to compare differences between values.
While a data economy that leverages user data for profit without regard to harm or
consent is not inherently wrong under Utilitarianism (nothing is), any suffering or
unhappiness caused by such a system weighs ethically against it. An analysis of the
major themes in Table 2 includes the possibilities of happiness and unhappiness (here
meant as anything opposed to happiness) acting on such values provides.
Table 2 Utilitarian analysis of the themes of values
Theme of values Utilitarian analysis
User’s control over data and data sharing + personal agency leads to happiness
through individual needs
Transparency and being informed + makes regulation possible
+ feeling of security
Security + decreases the possibility of unhappiness
due to data misuse
+ decreases unhappiness due to breaches
Trust and fairness + equality and fairness directly contribute to
subjective happiness
+ inner changes lead to values in other
themes
Compensation or benefits for users + direct happiness in small amounts, indirect
consequences challenging to assess
Supervision and rules + bolsters security and trust, but no direct
effect on happiness
Negative attitudes towards data economy + no data collection would make breaches of
trust impossible
- no data collection would also prevent
positive benefits and advancements
Under Utilitarianism, all values are considered instrumental to the Greatest
Happiness Principle. As might be noted from the table, all themes seem to have the
potential to create happiness, excluding the possibility that data is not collected at all.
Thus, it seems that from a Utilitarian perspective, the values of individuals are
ethically justifiable. Some of the major themes overlap (e.g. user control and
transparency) and others have relational dependencies (e.g. transparency and
supervision), which makes exact analysis difficult. When one value is necessary for
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another, the secondary value’s positive consequences can, to a degree at least, be
added for the benefit of the primary value.
Through a high-level analysis, it would seem like Security has the largest
possibility for reducing unhappiness caused by data misuse and data breaches. But
other values such as transparency, and supervision and rules support security, which
adds to their consequentialist importance. No data collection at all would lead to no
misuse of any kind, but it would also prevent data economy ecosystems from creating
any benefits.
Notably, the analysed values do not include values that might be guiding the
corporations taking part in the data economy, such as capital gains and job creation.
However, the happiness created by corporate gains can be seen to be included in
compensation and benefits, to some degree. And money itself is not happiness, so
these positive consequences are diluted to whatever instrumental value wealth has in
connection to happiness. Likewise, though the user happiness brought by
compensation and benefits to users might be direct, it seems instrumental and
relatively small – quickly offset by any major unhappiness due to data misuse and
data breaches.
This high-level analysis would suggest that the most important themes are those of
security, and its requirements: transparency, supervision and rules, and user control.
While trust and fairness might not have direct short-term consequences, they clearly
contribute in a major way to the fulfilment of other values. They will possibly be a
practical requirement for the required changes in corporate culture, work methods,
etc. for actualising other values. Thus, values presented by Rantanen (2019) seem to
be justifiable from the Utilitarian perspective, and some themes could produce or
preserve more happiness than others.
3.2 Deontological ethics
Deontology defines good through the intent of the actions; whether we would follow
the general ethical rule voluntarily, and keeping in mind that humanity (or rationality)
in rational beings is valuable in itself (Kant, 1785, 1788). While others, most notably
Rawls (1999) have reformulated deontological ethics to some extent, the original idea
of a categorical imperative, a universal law through which other rational beings are
treated ethically has remained the carrying force of the theory. According to
deontological ethics, all actions should be based on these rules, and the actors follow
them autonomously, and as consequences are never entirely predictable, this ought to
be the basis of right actions, not the consequences (which, of course, are still
meaningful).
Deontology is divided into categorical imperatives and rules derived from those.
None-the-less, the rules themselves are always intrinsic, such as “lying is wrong”, or
“murder is wrong”, as they need to be universal, treat humanity in a person as a value
in itself and be followed autonomously, after understanding the right thing to do.
According to Deontology, there can, of course, be other values, which are not moral.
Kant, for example, handles these in his various writings (e.g. Kant, 1785, 1788), but
these values are always superseded by moral values.
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Data economy, which uses user data for profit without regard to harm or consent is
inherently wrong according to Deontology. The targets of the use are not considered
as meaningful and valuable beings in themselves, but only as a means to an end:
profit; which itself is only of instrumental value, not intrinsically valuable. The
categorical imperative needs to be applied to each situation to find any definitive
answer. As the themes of values all are situational, they are analysed further in Table
3.
Table 3 Deontological analysis of the themes of values
Theme of values Deontological analysis
User’s control over data and data sharing The users ought to by default have control
over their data to respect their autonomy and
to respect them as persons with individual
agendas.
Transparency and being informed To be able to make ethical deductions based
on what the data is being used, the users
should have access to how their data is used,
lest they are treated merely as a means to an
end.
Security In a perfect world, all would act according
to the categorical imperatives. However, in
this world, many make decisions
heteronomously, due to external pressures,
and thus security must be looked after.
Trust and fairness Especially according to Rawls, fairness is a
central value in society and should be one of
the carrying forces in any situation.
Compensation or benefits for users When compensation or benefits to users are
applicable through profit or other benefits to
the handler of the data, it is fair to
compensate the targets; however, this is
situational, the categorical imperative needs
to be applied to find a definitive answer.
Supervision and rules Again, due to the heteronomousness of the
world, this is necessary – although we all
should make autonomous decisions, in an
imperfect world this is not always possible.
Negative attitudes towards data economy And again, these negative attitudes,
according to Deontology follow from the
imperfect world and its heteronomousness;
as we cannot trust all actors in the data
economy to function autonomously
according to the categorical imperative(s),
these are unfortunately justified.
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Thus, in a deontological framework, it is hard – if not impossible – to say which of
the previous values would be more important than others; in all of them the
categorical imperative ought to be followed when needed. Maybe the compensation
or benefit value can be, at least at times, not according to the categorical imperative,
as long as the previously pointed out rules on what is right and what is wrong are
followed. However, it seems that all the themes are justifiable from the deontological
perspective. The categorical imperative should be followed, and it is not in
contradiction with the needs of individuals expressed in Rantanen (2019).
3.3 Virtue Ethics
Unlike a Consequentialist and Deontological approach, Virtue Ethics is not focusing
on outcome or duty. Virtue Ethics is based on that we should cultivate and practice
virtues (making good choices based on those virtues) in our life to achieve ethical,
good life. Aristotle used term telos that means the main purpose of people –that is a
good life in contexts of ethics. Aristotle stated that the highest good to be aimed is the
Eudaimonia that is practising such character traits that are needed to have a life at its
best (Aristotle et al., 1976).
Alisdair MacIntyre is a philosopher that can be seen as one of the most influential
virtue ethicists alive. McIntyre’s main claim is that we have lost the moral language
and are focusing on wrong issues when creating the rules or looking for consequences
of our actions (MacIntyre, 1981). By him, we should instead focus on our own
character and personhood – when we develop ourselves as a person, the ethical life
will follow. Even if this view can be criticised, it is a relevant approach as it sets
people as actors in such a position that they cannot exploit or use ethics as a mere
tool. Virtue Ethics emphasises the self-investigation that seeks to bring forth the good
characters and drop the bad ones.
However, MacIntyre sees the self-investigation and development of character are
not done in isolation but in a societal environment as we live amongst others, and our
actions will affect others. In this paper, we look at Aristotelian Virtue Ethics from the
perspective of MacIntyre instead of Nietzsche. MacIntyre argues that to see ethicality,
a conception of rational enquiry needs to be embodied in a tradition of society (see
Korkut, 2012). Data economy and its rules cannot be based on individual values of
everybody, as data economy ecosystems are based on cooperation in a network, not
on individualism that Nietzsche emphasises. The value basis has to be based on some
kind of consensus which must be contested and rationally argued2 . Thus, we see that
Nietzschean approach of values would lead the infertile outcome as the pure
individualism is not offering a plausible way to organise ethical data economy
ecosystems. After all, data economy ecosystems are based on an interaction between
different parties at largest on the global level.
2
Actually, this is very Habermasian approach that is visible in Discourse ethics.
However, we do not go further in this approach in this paper as it would lead us to the
endless road.
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Also, Aristotle devoted special attention to virtue-friendship – amongst friendships
– which emphasises the critical views of ethical arguments and thus helps one to
develop own virtues further (see Cooper, 1977). This is especially relevant in a data
economy that is based on the use of data from “everybody”.
The Virtue Ethics gives a proper perspective for the values as it reveals that those
are actually based on Consequentialist and Deontological logic. There is an aim to
prevent or advance some outcomes or stating rule to be followed. As McIntyre
demands the virtues should be visible in practice or those lose their inner value for
individual and become mere tools of gaining benefits from outside – values become
empty shells. Thus, we want to look Aristotelian virtuous that could be behind of
values. In Table 4. we present the values of individuals and those related to the
Aristotelian virtues: Courage, Temperance, Liberality, Magnificence, Magnanimity
(Pride), Ambitiousness (Honor), Gentleness, Friendliness, Truthfulness, Wittiness,
Modesty and Justice. Notably, these virtues are not “extreme”. As an example,
courage does not mean to be temerarious. Likewise, it is not cowardness. It means to
have the courage to do what a nobleman do even we are feeling fear.
Table 4 Virtues in the themes of values
Theme Virtue connected to the value
User’s control over data and data sharing Justice, Liberality
Transparency and being informed Justice, Truthfulness
Security Truthfulness
Trust and Fairness Justice, Truthfulness
Compensation or benefits for users Justice, Liberality
Supervision and rule Justice
Negative attitudes Truthfulness
It seems that an analysis of values is not so easy to conduct as virtues are a
character of a person. The values presented in this paper are based on opinions on
what is expected from a fair data economy. Thus, the link between those and virtues is
somewhat artificial. However, the oversimplified table above presents that
Truthfulness, Justice and Liberality are virtues that should be highlighted as those
seem to be issues that can be connected with the data economy. Nevertheless, if we
take the virtues ethics approach to data economy ecosystems, we should keep in mind
other virtues as well.
The lack of virtues and values in current data economy which is based on
exploitation on individuals (Couldry & Mejias, 2019) without truly informing them
(Lahtiranta et al., 2017) reveals the current problem. We have already entered into an
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era of new colonialism: data colonialism, which has normalised the exploitation of
human’s trough personal data (Couldry & Mejias, 2019). The virtues – the lack of it –
behind the current data economy is well visible form virtue ethical standpoint that
underlines the need for it as one ethical cornerstone.
4 Towards the ethically justified value basis
All three main ethical approaches have different approaches to ethical evaluation, yet
it seems that there are many similarities in them, but also some slight contradictions.
All three offer some justification for the value themes of Rantanen (2019), with minor
differences in the most important value to be addressed. The simplified summary of
the analyses of the value themes is presented in Table 5.
Table 5 Summary of the analyses
Consequentialism Deontology Virtue Ethics
User’s control Fulfilment of the needs Respecting user’s Justice,
over data and through agency leads to autonomy and them as Liberality
data sharing happiness. persons.
Transparency Makes regulation Information needed in Justice,
and being possible and adds to the ethical deduction, if Truthfulness
informed feeling of security. denied people are
treated merely as means.
Security Decreases the possibility A must, since not all act Truthfulness
of unhappiness caused by according to the
data misuse and breaches. categorical imperatives.
Trust and Directly influence Fairness is a central Justice,
Fairness subjective happiness that value of society and Truthfulness
can affect other values. should always be taken
into account.
Compensation Directly increase Fair to compensate Justice,
or benefits for happiness in small when possible, but due Liberality
users amounts, indirect to different situations,
consequences challenging there is a need to apply
to assess. the categorical
imperative to find a
definite answer.
Supervision and Reinforce security and A necessity, although Justice
rules trust but do not create we all should be able to
happiness directly. make autonomous
decisions.
Negative No data collection would Justified reactions. Truthfulness
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attitudes make breaches
impossible but also
prevent positive benefits.
These analyses indicate that some core values connect all the value themes and
main approaches. First intrinsic and repetitive value justified is autonomy. Autonomy
is most clearly presented in Deontological ethics. This is obvious since it is one of the
core values of the whole theory. Nevertheless, also the Utilitarian approach highlights
agency as a source of happiness, whereas it is inbuilt in the Virtue Ethics approach.
Thus, it is justified that the users should have control over their data and data sharing
and a possibility to make autonomous decisions. Therefore, autonomy should be part
of the value basis of the data economy ecosystems.
Second repetitive and intrinsic justified value is justice. It is one of the virtues and
at the core of Deontological ethics as something that should always be taken into
account. It can also be argued, that virtue of Liberality is in this context intertwined
with justice. In the Utilitarian analysis, justice is more implied, but present since its
effect on the subjective happiness if it is not realised in some of the themes such as
“Trust and fairness” or “Compensation and benefits to users”. Thus, it can be argued
that justice is a cross-cutting intrinsic value in several themes and should be included
in the value basis of the data economy ecosystems.
Finally, security is the third repetitive value in all analyses. From the Utilitarian
perspective, it is also a moral value since it prevents unhappiness but from other too
perspectives is a necessity followed form imperfect or unvirtuous world. However, as
a perfect world is a utopia, the security should be acknowledged in all data economy
ecosystems in order to protect the privacy of the individuals and data in general. But
as security is something that is used to protect, it could also be interpreted as an
instrument for some other values such as privacy or responsibility. However, we leave
this discussion to another time and paper, since security is one of the basic values.
These three values are made possible by other value themes presented.
“Transparency and being informed” and “Supervision and rules” are instrumental to
autonomy, justice and security. Thus, there needs to be transparency and truthful
information in order to autonomy, justice, and privacy to be actualised in data
economy ecosystems. To conclude: the European human-centric data economy
ecosystem should be based on the ethically justified values of autonomy, justice and
security which are made possible with instrumental values of transparency, honesty
and supervision.
5 Conclusions and future research
These brief analyses show that there is a need to ethically evaluate the value basis of
the data economy ecosystem, but also demonstrates how challenging it is to justify
abstract personal values. Nevertheless, these analyses show that all value themes of
the Europeans personal values presented by Rantanen (2019) are meaningful, and
values behind them ethically justified. Intrinsic values of autonomy, justice and
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security and instrumental values of transparency, honesty and supervision should be
included in the value basis of the European human-centric data economy ecosystem.
Whilst treating people as data objects is wrong, we do not want either a purely
individualistic approach, which has its own problems. Instead, we need real change
towards a model where there exist real possibilities to influence and value basis that
can be commonly accepted and is ethically justifiable. Then we may gain data
economy that is not based on exploitation but mutual benefits of different
stakeholders
Naturally, due to the immaturity of human-centric data economy ecosystems as a
research field, there remains a lot of possibilities for future research. First, how these
values can be actualised in data economy ecosystems should be studied. Only then we
can make more accurate evaluations from any ethical approach. Second, there should
be more empirical research concerning the values of the individuals in the context of
data economy ecosystems. Only then we can understand their role in the data
economy ecosystems better. Third, all ethical approaches should be further discussed
and analysed more closely in order to have a strong and solid philosophical basis for
data economy ecosystems.
References
Aguilera, U., Peña, O., Belmonte, O., & López-de-Ipiña, D. (2017). Citizen-centric
data services for smarter cities. Future Generation Computer Systems, 76,
234–247. https://doi.org/10.1016/j.future.2016.10.031
Aristotle, Thompson, J. A. K., & Treddennick, H. (1976). The ethics of Aristotle: The
Nicomachean ethics. Penguin.
Bentham, J. (1829). Article on Utilitarianism: Long Version Marginals. Deontology:
Together With a Table of the Springs of Action; and the Article on
Utilitarianism, 309.
Brey, P. (2017). The strategic role of technology in a good society. Technology in
Society, 52, 39–45.
Cooper, J. M. (1977). Aristotle on the Forms of Friendship. Review of Metaphysics,
30(4), 619–648. https://doi.org/revmetaph197730450
Couldry, N., & Mejias, U. A. (2019). Data Colonialism: Rethinking Big Data’s
Relation to the Contemporary Subject. Television & New Media, 20(4), 336–
349. https://doi.org/10.1177/1527476418796632
EU GDPR Compliant. (2020). What Is a Data Subject?
https://eugdprcompliant.com/what-is-data-subject/
European Commission. (2017). COMMUNICATION FROM THE COMMISSION TO
THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN
ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF
THE REGIONS ‘BUILDING A EUROPEAN DATA ECONOMY’
(COM(2017) 9 final). European Commission.
European Commission. (2020). COMMUNICATION FROM THE COMMISSION TO
THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN
ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF
83
Proceedings of the Conference on Technology Ethics 2020 - Tethics 2020
THE REGIONS - A European strategy for data (COM(2020) 66 final).
https://op.europa.eu/s/n7iZ
Friedman, B., Hendry, D. G., & Borning, A. (2017). A survey of value sensitive
design methods. Foundations and Trends in Human-Computer Interaction,
11(2), 63-125.
Härkönen, H., Naskali, J., & Kimppa, K. (2019). Hub Companies Shaping the Future:
The Ethicality and Corporate Social Responsibility of Platform Economy
Giants. Proceedings of the 2nd ACM SIGSOFT International Workshop on
Software-Intensive Business: Start-Ups, Platforms, and Ecosystems, 48–53.
https://doi.org/10.1145/3340481.3342738
Kant, I. (1785). Groundwork on the metaphysics of morals (Various translations
used).
Kant, I. (1788). The critique of practical reason (Various translations used).
Kling, R. (1984). Assimilating social values in computer-based technologies. In
Telecommunications Policy (Vol. 8, Issue 2, pp. 127–147).
http://dx.doi.org/10.1016/0308-5961(84)90032-6
Korkut, B. (2012). MacIntyre’s Nietzsche or Nietzschean MacIntyre? Philosophy &
Social Criticism, 38(2), 199–214.
https://doi.org/10.1177/0191453711427258
Koskinen, J., Knaapi-Junnila, S., & Rantanen, M. M. (2019). What if we had fair –
people-centred – data economy ecosystems? Proceedings of IEEE Smart
World Conference 2019.
Lahtiranta, J., Hyrynsalmi, S., & Koskinen, J. (2017). The false prometheus:
Customer choice, smart devices, and trust. ACM SIGCAS Computers and
Society, 47(3), 86–97.
MacIntyre, A. (1981). After virtue (Various translations used).
Nietzsche, F. (1913). The Genealogy of Morals The Complete Works, Volume
Thirteen, edited by Dr. Oscar Levy (8th ed.). T.N. Foulis.
Nissenbaum, H. (2001). How computer systems embody values. In Computer (Vol.
34, Issue 3, pp. 120–119).
OECD. (2013). Exploring the Economics of Personal Data. 220.
https://doi.org/10.1787/5k486qtxldmq-en
Oliveira, M. I. S., & Lóscio, B. F. (2018). What is a Data Ecosystem? Proceedings of
the 19th Annual International Conference on Digital Government Research:
Governance in the Data Age, 74:1–74:9.
https://doi.org/10.1145/3209281.3209335
Papakyriakopoulos, O., Hegelich, S., Shahrezaye, M., & Serrano, J. C. M. (2018).
Social media and microtargeting: Political data processing and the
consequences for Germany. Big Data & Society, 5(2), 2053951718811844.
https://doi.org/10.1177/2053951718811844
Rantanen, M. M. (2019). Towards Ethical Guidelines for Fair Data Economy –
Thematic Analysis of Values of Europeans. Proceedings of the Third
Seminar on Technology Ethics 2019, CEUR-WS, 43–54.
Rantanen, M. M., Hyrynsalmi, S., & Hyrynsalmi, S. M. (2019). Towards Ethical Data
Ecosystems: A Literature Study. IEEE ICE/ITMC At: Nice, France, 2019.,
1–9.
84
Proceedings of the Conference on Technology Ethics 2020 - Tethics 2020
Rawls, J. (1999). A Theory of Justice (Revised edition.). Belknap Press of Harvard
University Press.
Rokeach, M. (1973). The Nature of Human Values. Free Press.
Rose, J., Persson, J. S., Heeager, L. T., & Irani, Z. C. I. S. J. R. E. R. (2015).
Managing e-Government: Value positions and relationships. In Information
Systems Journal (Vol. 25, Issue 5, pp. 531–571).
https://doi.org/10.1111/isj.12052
Schwartz, S. H. (2012). An Overview of the Schwartz Theory of Basic Values. In
Online Readings in Psychology and Culture (Vol. 2, Issue 1).
Thinyane, M. (2017). Small data and sustainable development—Individuals at the
center of data-driven societies. 2017 ITU Kaleidoscope: Challenges for a
Data-Driven Society (ITU K), 1–8.
World Economic Forum. (2011). Personal Data: The Emergence of a New Asset
Class.
85