=Paper=
{{Paper
|id=Vol-3582/FP_08
|storemode=property
|title=Balancing Innovation and Responsibility with Citizen Data: A Case of Wellness Records
|pdfUrl=https://ceur-ws.org/Vol-3582/FP_08.pdf
|volume=Vol-3582
|authors=Sami Hyrynsalmi,Hannu Vilpponen,Mika Grundström
|dblpUrl=https://dblp.org/rec/conf/tethics/HyrynsalmiVG23
}}
==Balancing Innovation and Responsibility with Citizen Data: A Case of Wellness Records==
https://ceur-ws.org/Vol-3582/FP_08.pdf
Balancing Innovation and Responsibility With
Citizen Data: A Case of Wellness Records
Sami Hyrynsalmi1 , Hannu Vilpponen2 and Mika Grundström3
1
LUT University, Lahti, Finland
2
University of Jyväskylä, Finland
3
University of Vaasa, Finland
Abstract
In the age of the data and artificial intelligence, the data itself has become as the fuel of new innovations,
improvements and prosperity. However, in the domain of healthcare and citizen-based personal data,
the regulation and freedom of usage have been naturally remarkable more controlled than in the other
domains. This study takes a look on the domain where the information considered is personal data,
yet its secondary usage is not regulated as strictly as, for instance, personal health records. The case of
wellness records is used as an exemplar case as it falls between these ends. This study contributes to this
discussion of ethical data economy by emphasizing the point of cultivating or preventing innovations in
the usage of data. While a majority of the extant literature focuses on the aspects of control and freedom,
this study emphasizes also the potential for new innovations that the data could offer. Yet, this needs
careful balancing between innovativeness and responsibilities.
Keywords
Data economy, Fairness, Innovation, Personal Data, Responsibility
1. Introduction
The modern time in the business world is often described as the era of data. Here, data has been
argued to became as the fuel, the new oil, of new economic growth and prosperity [1]. Data
collected from various sources can be used to create completely new products and services,
or improve the efficiency of the current ones. Therefore, the possibility to use data in various
industrial as well as in governmental domains have gained lot of interest previously. Thus, the
new era ‘data citizens’ are producing data that has became a powerful tool both both in politics
as well as in the industrial markets [2].
For the sake of discussion in this paper, we simplifying the discussion by categorizing data into
two main groups: (i) personal data, that is data consisting of, or containing identifiable personal
information [3]; and (ii) non-personal data. In the former group, there are various subcategories
ranging from the patient health records to the GPS locations of a vehicle transported by a
identifiable person. In the latter group, there are all data which cannot be linked to a person, or
does not contain any person specific attributes. For instance, a longitudinal data of a machine’s
vibration does not contain any personal information.
Conference on Technology Ethics - Tethics, October 18–19, 2023, Turku, Finland
Orcid 0000-0002-5073-3750 (S. Hyrynsalmi)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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While this categorization oversimplifies the rich variety of data which is collected and used in
the society, it helps to illustrate ethical questions related to the usage of data, especially that kind
of a data which is collected from or contains person related attributes. As stated, the data has
been seen as the fuel of innovations for the new era and In the European Union (EU), the General
Data Protection Regulation (GDPR) [3] is a comprehensive privacy regulation for personal data,
which was adopted by EU in 2016 and went into effect on 2018. The GDPR includes strict rules
for data controlling and processing, and imposes severe penalties for non-compliance of the
regulation, including fines of up to 4% of global revenue of the organization or €20 million
(whichever is greater).
In Finland, an EU county, the use of customer and patient data — which can be generally
considered as the arch-type of the personal data — is a strictly regulated activity [4]. However,
the public sector has invested in the development of artificial intelligence and data analytics to
make it possible better public services and experimented with the use of artificial intelligence
to find potential risk factors for exclusion. Examples of this are the city of Espoo’s artificial
intelligence experiment [5], where data from the public sector was combined and extensively
analyzed with the help of artificial intelligence, so it was possible to predict risk factors related
to child welfare clients. Another example comes from the South Karelia hospital district, where
machine learning methods were developed to predict the social exclusion of young adults [6].
In this case, the legislation on the secondary and combined use of healthcare data was followed,
which only allows the use of de-identified, pseudonymized data.
As these examples illustrate well, the data science and artificial intelligence tools can be
used for the greater good of the society. However, not all such initiatives have ended well. For
instance, toeslagenaffaire refers to a Dutch scandal caused by a self-learning algorithm, which
falsely claimed several citizens for child care fraud, ultimately leading to several suicides due to
the collection of claimed fraudulent allowances [7]. Similar reports from the use of algorithms
for automatically detecting welfare frauds and their ultimate consequences have been reported
also from USA [8].
In Finland, the secondary use — i.e., the use of a data in the purpose where it was not initially
collected [9] — of health and social care data is regulated by law, with interpretation rules and
regulations regarding the use of social and health care registers and documents for research
purposes [10]. Citizens can enter their wellbeing data in Kanta PHR — a national personal
health records (PHR) service of Finland — using wellbeing applications and measuring devices
developed for such purpose. Currently the wellbeing data is for your personal use only. In the
future, the Client Data Act will enable you to give your consent to sharing your wellbeing data
with healthcare and social welfare professionals [11].
Although the person has comprehensive well-being information in the Kanta PHR system, the
person cannot share it with service providers, which makes it difficult to develop new services
that utilize well-being information. Furthermore, typically in the practice, difficulty here is the
location of the data in different data reserves and systems. Although the organizations use the
same information systems, the information systems are customized for each organization, which
means variation in data structures and interface [12]. These, together with overall innovation
climate, might prevent or hamper the born of new innovations. While the ethical discussion
on the era of data is diverse, the focus has mainly been on the rights of a citizen. This study
contributes to this discussion on the possibilities, as well as the hindrances, of the data usage by
95
emphasizing the role of innovations and innovation climate for the creation of new services
and goods.
2. Background
2.1. Innovation
For over a hundred years, initiated by the work of renowned economist Joseph A. Schumpeter [c.f.
13], innovations have been seen as one the critical aspects of economics growth and change [14].
Schumpeter argued that technological innovations often create ‘temporary monopolies’ which
are remarkable profitable for their owners. However, this advantage would ultimately diminish
as the competitors and imitators enter the market. [14]
International Organization for Standardization (ISO) defines in the their standard [15] ‘in-
novation’ as a ”new or changed entity, realizing or redistributing value”. This definition makes
a different between an idea or invention, and an innovation – the latter emphasized the eco-
nomical or societal impact of the new idea. However, there are various different views what
constitutes an innovation and what not. For instance, Edison et al. [16] found in their literature
review 41 different definitions of innovations.
However, to follow an old adage, innovations ‘does not grow on trees’. It is argued that it
requires more than just resources and discussions to became an innovative company, often
emphasizing such concepts that innovation climate and culture [17]. As illustrated by Ahmed
[17], an anti-pattern for innovation and creativity is an organization where the importance of
innovations are discussed among the top management, yet the only visible effort is infrequent
investments in research and development work. Contrawise, Agile software development
methods have been argued to drive innovativeness of an organization—compared against the
use of plan-driven methods—[18], which might be linked to the elevated climate of innovation
in the agile teams [19].
On a large picture, the model by Tidd and Bessant [20] identifies five constructs of a company
driving for its innovation activities: (i) strategy, (ii) process, (iii) organization, (iv) learning, and
(v) networking. While there are some empirical support for the T&B model [c.f. 21], it remains
overtly abstract for understanding the forces creating an innovative organization. Therefore, in
the following we focus on an organization’s internal attributes thriving for innovations, namely
in innovation climate and culture.
While there are — again — multiple different definitions for an ‘innovation climate’ [22],
it is often defined as a shared perception of the extent which a team’s (or an organization’s)
processes encourage and support innovation [23, 24]. Ekvall [25] has studied the different
dimensions of innovation climate and listed high-level factors that enhance favorable environ-
ment for the innovations. In his model, the factors constituting towards innovation climate
factors [25]: (i) challenge, (ii) freedom, (iii) idea support, (iv) trust/openness, (v) dynamis-
m/liveliness, (vi) playfulness/humor, (vii) debates, (viii) conflicts, (ix) risk taking, and (x) idea
time.
In addition to the innovation climate literature, the research stream focusing on innovation
culture has been identifying factors enhancing innovativeness. While innovation climate
describes how the members of an organization perceive it by different processes, practices
96
Intention for innovation
Infrastructure for innovation
Innovation Culture Performance Outcomes
Market orientation for innovation
Implementation context for innovation
Figure 1: The dimensions of Innovation Culture according to Dobni (adopted [26]).
and rewards — or otherwise stated, what are the real priorities of the organization towards
innovation. Innovation culture then again reflects on the deeply held values and beliefs towards
innovation in the organization. Thus, innovation climate is to some extent observable by the
different practices and rewards, innovation culture reveals how the members of organization are
reacting to different incentives as they aim to act according to the organization’s culture. [17]
For instance, the model by Dobni [26], c.f. Figure 1, illustrates four different dimensions of
innovation culture. Constructs, building up the different dimensions of the model, contain such
aspects as organizational constituency and learning. Whereas the innovation climate factors by
Ekvall [25] emphasize humane aspects such as trust, debates and humor, the innovation culture
model by Dobni [26] aims to measure more deeply hold beliefs of the organization. Finally,
as have been discussed in the context of organizational culture and organization climate [27],
while the borders of these two concepts are clear in the conceptual level, in practical level the
measures might often be mixed.
To summarize, the innovativeness of a company is built—and respectively destroyed—in
multiple levels, starting from the deeply hold beliefs (innovation culture), to the perceived
practices and rewards (innovation climate), as well as the strategy and innovation supporting
processes of an organization (T&B model). Merely held belief that an organization is against of
benefiting personal data for new innovations due to, e.g., the GDPR legislation and possible
fines, might stifle the born of new ideas. Yet, as discussed in the introduction, there are positive
examples [5, 6] from the innovative usage of personal data to generate value for all.
2.2. Related work
Prior works have covered multiple aspects related to the usage of personal data. For instance,
emerging efforts by the companies — such as MyLife Digital, MyData Global or DigiMe, which
are also called as a form of data activism [28] — empower individuals to control how their
personal data is potentially used [29, 30]. In these models, one challenge is related to the
accountability of data usage. Over the time, situations change and previously given consent to
transfer data to the services is no longer necessarily valid, in which case a mechanism is needed
to remove data from the services.
97
This domain’s ethical discussion, in the extant literature, has mainly been focused on the
fair and people-centered design and use of the data. Couldry and Mejias [31] even compared
the current practice in the data industry to historical colonialism. In similar vein, Sadowski
[32] argues that data should be considered as a capital which is produced by etrcation. Thus,
for instance Koskinen et al. [33] argue that in the creation of new kinds of data ecosystem, a
human-centric approach should be utilized. A similar call has been presented for biomedical
data [34]. In their work, Knaapi-Junnila et al. [35] requested of taking the citizens into the
discussion of the data’s usage. Koskinen et al. [36] further formulated this approach into a
discourse ethic -based tool.
In study by Rantanen [37], control over the data — among with the transparency and security
— was found as one of a European basic values in the data economy. In their further work,
Rantanen et al. [38] argue that these European basic values in the data economy are ethically
justifiable.
3. Analysis
This study is building up an argument that the current actions inside the EU, as well as in
Finland, are holding up the innovations in the usage of personal data. That is, the overall
actions are likely hindering the capabilities of new emerging innovations to born in the sector
of wellness records. As illustrated by the few given examples [5, 6], the secondary usage of
personal data has been able to achieve ethically justifiable, positive results.
While the use of patient health records is highly regulated and monitored, the case of more
general wellness records is not that clear. The sector is not that heavily regulated by EU
regulations or national laws, yet the information handled is personal data and under, e.g., GDPR
regulations. Therefore, in the domain of wellness data applications, there could be space for new
innovations, which would benefit individual as well as society at large. However, the overall
actions might create atmosphere against innovations in this domain.
As discussed in the review of innovation drives in an organization, innovativeness is not built
just on declaring it as a company’s value or founding a reward programs for new ideas. Instead,
it is an end result of holistic approach towards new ideas and innovations.
In the following, we will discuss about the main issues identified, which are hampering the
innovativeness in this domain:
Fear of GDPR. Even large corporations, in the Nordic countries, seems to be afraid of GDPR. In
a study by Hyrynsalmi et al. [39], the authors interviewed large embedded-systems companies
on their developing data business activities. The study discussed that even the large companies,
studied for the paper, are cautious in their new innovations due to the fear of GDPR. While
also the GDPR fines for large companies are remarkably larger than for a startup or a medium-
sized company, it can be argued that the similar cautious atmosphere regarding the usage and
storing of data which would contain personal information is also prevalent in all companies. As
discussed previously, these kinds of deeply holds beliefs act against innovativeness.
Unclear future prospects. The legislation in the EU regarding the use of data is currently
98
evolving heavily1 . For instance, Data Act (DA) [40], Data Governance Act (DGA) [41], and
Digital Services Acts (DSA) [42] are new regulations put forth by the Council of European Union.
These are all parts of new actions prepared by the EU to curb the digital markets. Whereas some
of the regulations have already been set, some of them are still under construction. However,
while these new directives are likely to serve good purposes and suit the improper usage of
data in the markets, they will likely have impact by hampering the innovativeness.
Lack of incentives for a citizen to share the data. In the current setting used in Finland for
sharing wellness data, there is little if any incentives for a citizen, a user of the potential new
systems to share his or her data with the third party developers. In current system one gets
product benefits (lower price, additional features) by giving consent to use the data which in
longer term might lead to lock-in. Whether this is true problem is unknown in the advent of
innovation practises in this space. Fair data economy has been suggested as the solution [43],
as it would allow the users also to benefit from the data their are giving. However, there is
still a lack of, direct as well as indirect, benefits that the user would receive of sharing his data
willingly with third-party developers. This is even more evident in the case of startups and new
players in the markets as they cannot benefit of luring the users to share their data as some
larger actors could do.
Unclear and uncertain responsibilities in the era of AI. As often is with data, different
machine learning (ML) and artificial intelligence (AI) solutions can be expected to be in a central
role in the near future in the creation of new innovative product and services. However, it is
considered that AI itself cannot be held responsible for its actions [44]; yet, some form of a
group liability has been proposed as a solution2 . Nevertheless, the open question of liability
and responsibility in the results of AI’s or ML’s decisions might hinder the application of these
techniques in the domain wellness records. While the forthcoming Artificial Intelligence Act (AI
Act) [45] and AI Liability Directive [46] by the EU is likely to suit the use of the AI technology;
however, before they have became effective, the uncertainty towards the future might act against
the innovativeness in this domain.
4. Discussion & summary
While there is no doubt that GDPR legislation has worked well in securing personal data
and preventing misuse in several occasions, it is also acts against the innovativeness of the
data economy field in Europe. The benefits from the directive likely surpass the negative
consequences; however, innovations can bring good for individual and society alike. Therefore,
for cultivating a fruitful environment for innovations, preventing actions are needed.
Some technical solutions to support the fair data economy, as discussed earlier, exists. For
instance, a blockchain -based authorization solution for sharing as well as controlling who uses
the data — thus also allowing monetization of the data for its initial collector — could potentially
1
The Digital Decade website by Hannes Snellman Attorneys Ltd has collected the forthcoming changes and illus-
trates well the various different directives as well as regulations pushed forward by the EU. https://digitaldecade.
hannessnellman.com/. Last accessed August 10, 2023.
2
T. Kalliokoski (2023). Tekoälyn vastuuta voi verrata ryhmävastuuseen. https://etairos.fi/2023/02/21/tekoalyn-
vastuuta-voi-verrata-ryhmavastuuseen/ Last accessed August 10, 2023
99
tackle some of the presented hindrances. However, a platform like this would also create new
problems, especially regarding the responsibility and question regarding the maintenance and
use of the system. This question largely depends on the type of data used. In case of personal
data one can claim the responsibility never ends. In the case of anonymous data one can assume
the quality of the data determines the actual lifecycle of it.
In addition, the class action lawsuits pending in the United States [47]. regarding the relation-
ship between artificial intelligence and copyright, may affect the entire artificial intelligence
industry. While the result would not directly affect into the companies working in the EU, some
of the existing solutions and platforms are offered by the USA -based companies.
However, it is worth to note that the premises of this study should be critically analyzed,
too. For instance, Brey [48] argues that privacy has only instrumental value in a good society
augmented with technology. According to his reasoning, only well-being and justice are intrinsic
values, and therefore privacy should be evaluated against these. Thus, the problematization
presented in this study, as emphasized also in the title, calls further ethical discussion.
Finally, it is worth to note that GDPR-compliant data economy products and services could be
of competitive advantage in the global market, where the privacy and proper usage of personal
data have been constantly gained more attention. Therefore, cultivating the innovative culture
in the European data economy could create globally remarkable new solutions.
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