=Paper=
{{Paper
|id=Vol-2996/short2
|storemode=property
|title=Data Design Futures: Who is Responsible?
|pdfUrl=https://ceur-ws.org/Vol-2996/short2.pdf
|volume=Vol-2996
|authors=Renee Noortman
|dblpUrl=https://dblp.org/rec/conf/ewsn/Noortman21
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==Data Design Futures: Who is Responsible?==
https://ceur-ws.org/Vol-2996/short2.pdf
Data Design Futures: Who is Responsible?
Renee Noortman
Eindhoven University of Technology, Department of Industrial Design
Eindhoven, The Netherlands
r.r.noortman@tue.nl
ABSTRACT would most likely not comprehend the potential consequences of
With the imminent ubiquity of data, the healthcare domain is turn- sharing their data.
ing to data to increase efficiency and effectiveness. Health data In order to unwrap the issue of (over)extensive data tracking, we
tracking in everyday life introduces challenges around user pro- look specifically at health tracking in the medical domain. Especially
tection, data quality, and transparency about data goals. While in the healthcare domain, the promise of data-driven healthcare
adhering to GDPR legislation, data design practice has done little has led to an enormous data surge over the last decade. We see two
to protect users of health tracking tools from their data and as- major movements: user-generated health metrics (bottom-up) and
sociated mental health problems. Additionally, the quantification medical-industrial “data-fication” of healthcare (top-down). With
that many data trackers facilitate can lead to comparison, competi- the onset of the Quantified Self movement and the introduction of
tion, and addiction. Finally, it can be difficult for users to oversee consumer health trackers such as Fitbit, Jawbone and the Apple
the consequences of sharing their data in consumer and research Watch, an era of consumer health tracking has begun. Simultane-
contexts. ously, visions are being published on the healthcare of the future,
We argue that designers should take responsibility for data cura- promising P4 healthcare focused on medicine that is predictive,
tion and protection when designing with data, especially in health- preventative, personalised and participatory [6]. To achieve these
care. We introduce a new breed of designer: the data futures de- visions, many stakeholders turn to AI and big data as the technical
signer, who actively seeks out the edges of data tracking and dis- means. Inevitably, health data will become more ubiquitous, de-
cusses these with future users to inform them, probe their responses tailed, personalised and quantified [1]. However, research has also
and consequently define the future of data design, together. shown that self-tracking can lead to an unhealthy self-image and
mental health problems for some users, not all users have sufficient
KEYWORDS understanding of the technology to make sense of the data they
internet of health, responsible IoT, health tracking, quantified self, collect, and use of data trackers also influences relationships be-
ethical design, design reflection tween patients and their doctors. These arising issues indicate that
user protection requires more than legal and ethical procedures,
1 INTRODUCTION and that some of the problems that arise due to data collection and
processing cannot be solved by laws alone. If we do not somehow
In modern society, there is very little you can do without it being
acknowledge that data design practice has to play its part in user
documented somewhere, from all our behaviour online to move-
protection, consequences could be severe, not only for these users
ment through the city [24]. Data has become so embedded in our
themselves, but also for society at a larger scale as it normalizes
lives that the extent of data collection is no longer visible and com-
exploitative behavior with little oversight and regulation.
prehensible for the majority of society. With data legislation such
We argue for active data curation on the design and development
as GDPR [5] and its world-wide counterparts, governments are
side and for human-centred designers to think about which data
increasingly moving to better protect the privacy of citizens. Never-
tracking is actually necessary, and which data tracking is desirable
theless, the way in which this or similar legislation is implemented
for users. We lay out some of the main issues that exist in data
in user data collection processes is often sneaky and obscure.
collection processes today and in the recent past. In order to address
With recent advancements in health tracking technology and
these issues in the design process, we propose a new breed of
countless healthcare apps being released every week, the trend of
designer: the data futures designer, who considers the use of data
data collection has also expanded itself into the medical domain,
beyond the initial interaction between users and their data. We
coined as the Internet of Health Things [3, 19]. Users are rewarded
posit that this specific role should be laid out in order to protect
for sharing personal data such as their mood and consumed meals
consumers of health data tracking tools from themselves, the data-
by lifestyle apps or are pressured to consent to data sharing to
driven world around them and from unconscious harm to others.
receive access to specific content and functions such as activity
reports from their fitness tracker. Not only do users skip reading 2 ISSUES WITH DATA CURATION FOR
the policies that they consent to, but if they would read them they
DESIGN
Copyright 2021 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
While the recent developments in the field of health data tracking
sound positive and promising, a societal counter-movement inves-
tigates the downsides of continuous tracking for one’s well-being.
At present, design is often too comfortably associated with the for-
mer, not embracing the risk perspective enough. In this section, we
approach the core issues of data curation for design from a societal
CHIIoT 1, February 17, 2021, Delft, The Netherlands Noortman
perspective, then focus on healthcare and data-enabled design as a 2.2 Self-tracked data in a clinical context
form of a design research that targets data literacy and agency for In medical and even clinical settings, there is an increasing interest
design. Data-enabled design is an ideal setting to showcase how in collecting contextual and self-tracked data from patients [2, 9, 27].
conventional design values and attitudes can lead to problematic The contextual and real-time information about patient well-being
data use, unintentionally harming end-users in the long run. can for example be used to prevent chronic and complex diseases
such as diabetes [6], predict and prevent hospital re-admissions,
2.1 Self-tracking your freedom away and reduce costs [1]. Self-monitoring is also being used to keep an
eye out during rehabilitation processes, which has allowed patients
There are numerous studies that have indicated issues with data
to take more responsibility over their own health, even increasing
logging and extensive tracking of habits and bodily measures. These
medication adherence [8]. Furthermore, contextual data has made
studies have shown that aiming at an always better, fitter, slimmer
it possible to include the partners of patients in lifestyle change
and healthier body can have severe consequences for the mental
programs [9], as well as foster active collaboration between parents
health of excessive users of health tracking technology [16, 23].
and care providers while diagnosing conditions in newborns [27].
2.1.1 Upward or downward spiral. As there are always aspects of 2.2.1 Care relationships. However, when patients are asked to
health, vitality and well-being that can be improved, your fitness keep track of their health by either their doctor or through an
tracker will never tell you that you are ‘done’, or that you have application, this can change the relationship between patient and
reached your ultimate healthy body and living pattern–conceptually care provider. It can put a larger responsibility on the patient and
an unbounded positive feedback loop. For some, constant competi- the care provider needs to be more reactive when responding to
tion can be motivating and lead to behaviour change, but these are versatile data that patients bring into the doctor’s office. When
also likely to already be interested in exercising and performing patients collect data, it is likely that they will assume that the data
healthy behaviour. For many others, competitive motivation is not they track is also being looked at, which can put extra pressure
effective and quantification of exercise does not work as motivation on their doctors as they feel an obligation to use the data in their
for everyone [13]. Fitness tracking has lead to addiction in the past, treatment [29]. At the same time, patients might feel less worried
in extreme cases even resulting in death, e.g., when a cyclist went about potentially alarming health data when they are not getting
downhill so fast to beat a record in a cycling app that he propelled any return on their data under the assumption that ’the doctor has
himself to his own death [16]. These obsessive behaviours are not looked at it, so it should be fine’.
strange when we look at the way in which health tracking apps
2.2.2 Quantity over quality. Additionally, the quantified data that
and devices have been designed. They offer extrinsic rewards for
is easiest to measure might move more to the foreground, and the
healthy behaviours and push you to push yourself constantly and
qualitative data that is harder to capture at a larger scale might be
to always be self-critical.
neglected. Similarly, quantitative data is much easier to interpret
and agree upon, as we generally can agree that a 10 is better than a
2.1.2 Competition and comparison. Furthermore, health tracking
9, while it is much harder to decide whether it is better to be sad
apps often have elements of gamification, competition and per-
or to be angry. The former is generally accepted, while the latter
formance sharing to keep users and their network engaged [28].
might be different from person to person and from case to case. In
The constant quantification and comparison of one’s life to that of
many cases, however, the qualitative data such as how the patient
others can have detrimental effect on mental health and lead to com-
experiences symptoms or how they are feeling at specific moments
pulsive or even addictive behaviours on the one hand, and lack of
during a day might give much more information on how the patient
self-worth and depression on the other hand. Though users do quit
would be best helped. As people use trackers more and more often
these applications for the sake of their mental health through what
to understand their own bodies and behaviours, they might also
is often referred to as a ’detox’, feelings of guilt over not sharing
start relying on the numbers more. When your phone knows exactly
progress or not “being their best self”, i.e., integrating and lever-
what you need to do to be healthy, why would you still listen to
aging metrics in their everyday life successfully, remain [10]. This
your body? The same could count for healthcare providers: imagine
makes the health tracking addiction extremely complex as the habit
that you are on the list for a stomach reduction surgery but your
of health tracking is still perceived as something that will improve
data indicates that you lack motivation to work on improving your
one’s health, while it simultaneously causes averse side effects to
lifestyle, which renders the surgery useless from your doctor’s
users’ mental health. To add onto that, the negative side-effects
point of view. While the numbers might not be in your favour, the
of self-quantification are not something that users are generally
surgery might still give you confidence, or the feeling that you are
warned about, as the overall, net-positive effects of the habit are
seen. Although these factors are not as measurable as a reduction in
still seen as something positive. This form of utilitarian ethics [15]
BMI, they could have an equal or even bigger impact on a patient’s
can fail for the individual: while it is by now generally accepted
quality of life–and set them on a different path in life. It is essential
that packets of cigarettes state that smoking is addictive and deadly,
that these emotions and qualitative arguments are not lost when
that bottles of alcohol indicate a recommended daily maximum,
striving for optimisation and quantification.
and that borrowing money costs money, there is no warning or
indication of the possible side effects that health tracking might 2.2.3 Health literacy. Furthermore, few users of healthcare track-
have when you install an app or buy a tracking device. An app store ing devices actually have sufficient health literacy to know exactly
is not a pharmacy, nor should it be. how to interpret and use their own health data [25]. Health literacy
Data Design Futures: Who is Responsible? CHIIoT 1, February 17, 2021, Delft, The Netherlands
in self-tracking can be compromised in many different ways, from in the workplace and beyond. Furthermore, an ongoing critical
understanding which tools are appropriate for achieving certain movement in the IoT domain has investigated dark patterns in
health goals, to knowing how to interpret the collected data, to connected devices, drawing attention to the security of IoT devices,
knowing how to change behaviour to achieve better results [25]. and highlighting the possibility for providers to manipulate IoT for
The same counts for the side of the health providers. While they may their own benefit [4].
have an extensive understanding of the condition of their patients, The above points indicate how acceptance of health tracking
they might not know how to interpret contextual data, or they technology can have impact beyond the individual, through nor-
might not even trust data that has not been gathered with clinically malisation. Both through social pressure and through changing
approved tools [29]. Currently, interpreting the data still requires infrastructure, society changes along with new technology. This
collaboration between care providers and their patients [14]. Fur- does not have to be a bad thing, but the problem is that if it turns
thermore, it could be hard for users of smart products to understand out to be bad after all, it is incredibly hard to turn it back when it
underlying technical aspects of their devices [21], and thus to un- has been accepted and adopted by most. Everyone that shares their
derstand whether the device and the advice it gives are trustworthy. personal health care thus contributes to normalisation of health
Overall, these arguments show that merely sharing quantitative data tracking.
data is often not enough and requires some additional explanation
in the shape of qualitative data. This explanation is required on both 2.4 The designer is complicit
sides: on the side of the user it is important that they are guided in
As HCI experts, designers and design researchers, we are increas-
interpreting and understanding their data, but also on the side of
ingly dealing with data in our design processes through method-
the care provider it is important that qualitative and quantitative
ologies such as data-driven, data-informed, data-aware and data-
data are balanced to give an accurate representation of the patient’s
enabled design [7, 11, 26]. Acknowledging data streams within our
condition, while keeping the amount of data manageable in its
design work is important, but it has become clear that being aware
everyday use.
of data within our design work alone is not enough and that a new
perspective on data design practice is necessary [12].
2.3 Consequences of self-tracking for others
The undesirable consequences of sharing one’s own personal (health) 2.4.1 Different types of data. Designers often end up in a conflicted
data are relatively straightforward. What might be less straightfor- position, trying to understand and satisfy user needs on the one
ward are the secondary and tertiary consequences of sharing your hand, and trying to satisfy the needs and business goals of their
own personal data. employer on the other. Adding data into this mix makes it even
more conflicting as data can be used in different ways. Building
2.3.1 Unintentional exclusion. The Dutch government recently an-
on data-enabled design, design research works with two types of
nounced that it will soon be possible to download a report indicating
data: research data and solution data. Research data is data that is
which vaccinations you have received, to be used as proof for receiv-
collected to perform research through design: to monitor how a
ing a COVID-19 vaccination. Three out of four Dutch citizens have
design is being used in practice in order to improve the design or
already indicated to be in favour of showing proof of vaccination to
to learn something new about user behaviour. Solution data is part
enter a pub1 . However, for someone who did not get the vaccination
of the design: it is required in order for the design to function, e.g.,
for whatever reason (e.g., religion, political standpoint, health risk
by training an algorithm such that it gives personalised recommen-
or lack of access), this development can quickly become a source of
dations [26]. As we design novel healthcare applications that work
discrimination and exclusion. Thankfully, Dutch government has
with data, we are complicit in the collection and curation of both
since deemed rewarding people for vaccination undesirable2 .
types of data, and there might sometimes be overlap or confusion
2.3.2 Opting out. As health tracker use is being promoted in the about the purpose of collecting specific data. We want to collect
medical domain, and as more and more people start participating both types of data as we want to learn how to make our design
in the movement, it might become much harder for patients to opt- better and understand our user, while at the same time we want to
out of sharing this data in the future. Health insurers have already deliver a satisfying user experience. For a user, that confusion is
actively started promoting the use of health trackers [18, 23], and bound to be even bigger, especially when we also start using the
the implementation of trackers in regular care programs might data to change the design that they are using along the way. When
make patients feel that medical care is only optimal with tracker users are asked to provide a lot of research data at the beginning,
use, much like websites are increasingly becoming unusable when they might expect to get a big return in the shape of solution data
you refuse cookies. Similarly, employers around the world have as well, or even analysis or interpretation of the data. These ex-
also started handing out activity trackers to employees [17]. As an pectations create a continuous back-and-forth between user and
employee, you are still free to wear the tracker or not, but not using designer which could almost be seen as a negotiation between both
it might be frowned upon, or rewards could be offered to those who parties: who will get the most (data) out of it?
do wear it. Without strict regulation, it is only a small step from
this development to economic discrimination and marginalization 2.4.2 Reward and risk. The proposed benefits of using data in this
real-time manner–that both sides can use the data–often blur the
1 https://nos.nl/nieuwsuur/artikel/2361109-met-vaccinatiebewijs-toegang-tot-de-
line between research and solution data: the user can potentially
kroeg-driekwart-nederlanders-is-voor.html
2 https://nos.nl/artikel/2361134-kabinet-ziet-weinig-in-voordelen-voor- get feedback on the personal data that they share, while the design
gevaccineerden.html researchers learn about the context and motivations of the user. The
CHIIoT 1, February 17, 2021, Delft, The Netherlands Noortman
risk is that the boundary for users to share their data is explicitly data and intelligence technology to go. If not, we should seriously
lowered by the design researchers by offering the users something question what we are trying to achieve. Instead of asking users for
in return (i.e., insight into their data and themselves). As users consent for cookies once our plan has been rolled out, we should ask
are offered something in return, they might not realise that they them before whether our terms are something they would consent
are actually allowing the design researchers into some of the most to or if they object to (parts of) it, and why.
intimate corners of their lives, and that they are thus giving the Besides an ethical perspective, there are also political and eco-
researchers the possibility to understand motives and behaviours nomic perspectives to take into account. From a political standpoint,
at a far deeper level than they would be comfortable with. When new technology asks for new legislation. Legal and regulatory pro-
data is collected and processed across individuals, inter-personal cesses that are already in place are important safeguards that need
patterns might emerge that the individual would never want to time to be implemented fully and sometimes need to be tested
share. At the same time, with highly automated data collection in courts. We also see that their focus might need to be adjusted.
comes the risk of misinterpretation and problematic data analysis While current legal processes are aimed at risk assessment, research
chains. This is compounded by sharing data beyond the design quality control and participant safety, their focus might shift more
researcher, perhaps in anonymised form or as a trained model. For towards the ‘why?’ and the ‘what for?’ of design research–not so
example, users might be more willing to share details about their much in terms of assessing a data need for a study, but to assess
exercise patterns with a designer who is committed to designing the impact of collected data more broadly. A political question
something to improve their personal lifestyle than they are willing also arises about whether governments should impose restraints
to share the same information with their doctor to determine a on health tracking technology that is being used in public health
treatment plan. However, what they might not realise is that this services.
doctor will eventually use the data gathering tool that the designer From an economic perspective, collecting and selling data has
developed. The designer is thus not a separate, neutral entity, but slowly become one of the most effective business models in the tech
complicit in the data gathering goals of their employer or business industry. As a result, IoT researchers are increasingly looking into
client, which might not always be in the user’s best interest. Far how to design for consumer privacy [20, 22]. The question here
more than for traditional products and research objectives, we need is how designers can increase consumer awareness about privacy,
to be clear and communicate truthfully to our users not only about and whether consumers will care enough about their data and
the purpose of the research we conduct, but also where the results the consequences of sharing it to pay (more) for the products and
of this research could lead us in the future. services they use. This will be a complex endeavour as by now, so
many free products and services have been made available on the
consumer market.
3 WHAT DESIGN CAN DO In light of these different perspectives, perhaps it is time for
Design and related disciplines such as data science, engineering, a new profession, one which exists at the junction of ethics, de-
and politics play a key role in defining how data is being used sign and formal data regulation. Such an expert could go by the
in everyday life. The entanglement of many different disciplines name of ‘data futures designer’. Their tasks would include inform-
in the design of data technology makes the situation inherently ing the public about new innovations and presenting them with
complex. Even more complex is to point out the discipline that is scenarios for future use of data and intelligence. Besides collect-
responsible for the consequences of data technology. As practices ing public opinion, they would also be tasked with investigating
are intertwined, it is inherently important that these different disci- and documenting possible consequences of data collection, data
plines each take on part of the responsibility for the well-being of processing and intelligent technology. On top of consequences for
society as well. For design, this includes not only the responsibility users, these would also include the consequences for others who are
to do well by our users, but also to consider the bigger picture and indirectly affected and society from a broader, futures perspective.
the (peripheral) consequences of wide-spread data collection on Data transparency is key for the data futures designer, covering
different aspects of life and society. both transparency about data being collected and the ultimate goal
In order to do well by our users and design with their interests that it is being collected for.
and societal interests in mind, we should actively involve users We should think about which data best captures the situation and
not only in our design process but also in our thought process, challenge ourselves to find richness in health data by collecting as
considering them as individuals, as groups and families, and as a little of it as possible. How do we decide which data really matters
heterogeneous collective. This includes clear communication about and reduce this to the bare minimum to decrease the impact on
our objectives to study participants but also actively seeking out user privacy, security and potentially well-being? We should not
opinions of potential users, in particular from those opposed to shy away from the qualitative data simply because it is difficult to
our ideas. Design education should thus address these processes gather or difficult to process. We should, however, shy away from it
in detail and make designers aware of their ethical responsibility, once it becomes invasive or ethically questionable to collect it. “It’s
especially around data, privacy and ways in which data technology just for this one time,” or “It’s only for research” can no longer be
could inflict harm. But perhaps we should take it much further arguments when we are collecting real-time data in real contexts
than that. Beyond only asking users for their opinion, we could with real people. When conducting data-enabled design, it is never
provide them with scenarios to have an opinion about. Being open just for research, and collecting the data will always have an impact
about data means proactive confrontation with what could be. After on the research subjects.
all, we are the designers and we have a vision for where we want
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