This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

Digital Psychiatry: Risks and Opportunities for
Public Health and Well-Being
Christopher Burr, Jessica Morley, Mariarosaria Taddeo and Luciano Floridi

Abstract—Common mental health disorders are rising globally,
creating a strain on public healthcare systems. This has led to arenewed interest in the role that digital technologies may have for
improving mental health outcomes. One result of this interest is
the development and use of artificial intelligence for assessing,diagnosing, and treating mental health issues, which we refer to as‘digital psychiatry’. This article focuses on the increasing use ofdigital psychiatry outside of clinical settings, in the following
sectors: education, employment, financial services, social media,
and the digital well-being industry. We analyse the ethicalchallenges of deploying digital psychiatry in these sectors,
emphasising key problems and opportunities for public health,and offer recommendations for protecting and promoting publichealth and well-being in information societies.
Index Terms— artificial intelligence, digital ethics, digital
psychiatry, digital well-being, mental health.

I. INTRODUCTION

T

HE incidence of common mental health disorders is rising
globally. The World Health Organisation states thatbetween 2005 and 2015 the total estimated number ofpeople living with depression increased by 18.4% and 14.9%
for anxiety disorders [1]. At the same time, the Organisation for
Economic Co-operation and Development has stressed that thehealthcare systems of its member states are under increasingstrain from rising demand that has not been met by an increase
in resource [2]. This pressure, combined with additionalsocioeconomic issues (e.g. unequal access to treatment), hasresulted in an increased interest in the role that digitaltechnologies may have in improving mental health outcomes[3], [4].
Digital technologies that use artificial intelligence (AI) to
infer whether users are at risk of, or currently suffering from,mental health disorders are already available on the market,
including public app stores and commercially-available
services [5], [6]. We refer collectively to these technologies
using the label ‘digital psychiatry’. Digital psychiatry is now
This research was funded by a grant from Microsoft Research awarded toProfessor Luciano Floridi.
C. Burr is with The Alan Turing Institute. He was with the Digital Ethics
Lab at the Oxford Internet Institute, University of Oxford at the time thisresearch was conducted e-mail: cburr@turing.ac.uk).
J. Morley is with the Digital Ethics Lab at the Oxford Internet Institute,
University of Oxford.

used to infer, with varying degrees of reliability and validity [7],whether an individual is suffering from depression [8]–[10],anxiety [11], autism spectrum disorder [12], post-traumaticstress disorder [13], and suicidal ideation [14].
In addition to risk assessment and diagnosis, digital
psychiatry has also been used to deliver personalized treatment
to individuals [15], [16]. Many of these developments haveresulted from the increased availability of behavioral andbiometric data, and new data streams have the potential for
providing greater ecological validity for epidemiologicalstudies [17], while also enabling new forms of predictive
analytics [18]. However, such data are now also collected byinstitutions and organizations that are not part of formal
healthcare system, for the purpose of extracting mental healthinsights about consumers, employees, users, students, andothers. The increasing use of mental health data outside thehealthcare system, alongside technological developments indigital psychiatry, raises a number of pressing ethicalchallenges related to individual health and well-being, as wellas wider social concerns like public health (e.g. epidemiologicalinflation; diminishment of patient autonomy).
The goal of this article is to analyze the ethical challenges
related to digital psychiatry, identifying risks and opportunities,
known problems, proposed solutions, and outstanding gaps. We
focus primarily on the use of digital psychiatry outside ofclinical settings. This focus allows us to explore how the use ofdigital psychiatry in non-clinical settings could lead to a change
in the distribution of responsibility for the maintenance ofindividual and public mental health. In section II, we frame theethical challenges associated with digital psychiatry by
exploring its use within the context of formal healthcare
systems. The purpose of this framing is to emphasize the risks
associated with deploying digital psychiatry outside of anenvironment that is governed by well-established frameworksof accountability and ethical principles and practices (i.e. aformal healthcare system). In section III, we explore sector
specific uses of digital psychiatry in non-clinical settings,
including education, employment, financial services, social
media, and the digital well-being industry. 1 In section IV, we
M. Taddeo is with the Digital Ethics Lab at the Oxford Internet Institute,
University of Oxford, and also affiliated with the Alan Turing Institute.
L. Floridi is with the Digital Ethics Lab at the Oxford Internet Institute,
University of Oxford, and also affiliated with the Alan Turing Institute.
1
The term ‘digital well-being industry’ is used to refer to the growing,commercial availability of software (e.g. mobile apps), hardware (e.g. smart
watches), and services (e.g. online chatbots) that seek to promote the selfgovernance of mental health and well-being (see section III/E).

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

analyze further some of the key ethical opportunities and risks,and offer a series of related recommendations for protecting andpromoting individual and social well-being in informationsocieties. Finally, section V concludes the article.
II. FRAMING THE ETHICS OF DIGITAL PSYCHIATRY
Machine learning algorithms are used to learn “statistical
functions from multidimensional data sets to makegeneralizable predictions about individuals” [19, p. 91]. Thesemodels can then be used to build automated decision-making
systems that can serve as decision-support tools, when there isa human-in-the-loop, or fully automated systems, when
designed to bypass or replace human decision-making. Bothtypes of systems can be used in digital psychiatry, leading tosignificant opportunities for improving mental health outcomes[3]. However, there are also risks to such approaches that need
to be addressed, namely: diminished patient autonomy, limited
model generalizability, risk of epidemiological inflation,unequal access to (or engagement with) potential support, andloss of privacy and trust (see section IV for discussion). To
understand these risks better, it is helpful to note how digital
psychiatry is being deployed within formal healthcare systems.
The delivery of formal healthcare is typically governed bydeeply entrenched bioethical principles, norms of conduct, andregulatory
frameworks
that
maintainprofessional
accountability. These practices and frameworks are designed tominimize risks to patient health and well-being, among otherthings, and are starting to address the use of digital technologies
in healthcare. For instance, the UK’s Department of Health andSocial Care has recently established a code of conduct for thesafe and ethical deployment of AI and other data-driven
technologies within the NHS [20]. Like most areas ofhealthcare, clinical psychiatry can be split into the following
series of stages: assessment, diagnosis, and treatment. Digital
psychiatry can be used in each of these stages, either to support
clinicians or automate key aspects of healthcare delivery. For
instance, Lucas et al. found that the use of virtual humaninterviewers increased the disclosure of mental healthsymptoms among active-duty service members, who may
otherwise be unwilling to seek support due to perceived
stigmatization [16]. The use of this virtual interviewer was able
to improve user engagement with mental health services while
also creating a novel environment to collect behavioral data tofacilitate
assessment
anddiagnosis.
Furthermore,environmental and behavioral data collected by mobile devices(e.g. smartphones, wearables) can provide ecologically-valid
sources of information about patients [21], [22], which could be
used to extract insights about the social-determinants of theirhealth to improve decisions about treatment options (e.g. socialprescribing or pharmacological). The complexity and size ofthese datasets means that AI is often necessary to help clinicians
extract meaningful insights [23].
As the above examples suggest, digital psychiatry can beused to augment and extend the abilities of healthcare
professionals, automating key tasks while ensuring that human2
Similar concerns have been raised in relation to embodied robotics used inassisted living and geriatrics [27], [28].

judgement remains an integral part of the process. Others havealready commented on the importance of keeping humans (andsociety) in-the-loop when deploying AI systems in healthcare[24], but this concern takes on a particular importance in thecontext of psychiatry. As Burns notes,“[t]he diagnosis of a mental illness […] reflects ajudgement, albeit often imperfect, that individuals havebecome different from their ‘normal’ selves in some fairly
recognizable way.” [25, p. 178]
It is crucial, therefore, that the judgment as to whether
individuals have become “different from their “normal’ selves”
remains a responsibility of a human agent.
Furthermore, as Johnstone & Boyle note, the purpose of adiagnosis is“fundamentally an attempt to make sense of a person’spresenting problems, to understand how they have comeabout and what might help”. [26]
Automated decision-making systems may be well-suited totasks like classification, but they are poorly suited to performtasks involving, for example, therapeutic, interpersonal
conversation.2 Hence, this participatory process of sensemaking between a patient and psychiatrist, alluded to by
Johnston & Boyle, is also currently poorly reflected in digital
psychiatry.
In the same vein, decisions about when, how, and whether tointervene in digital psychiatry are complicated by the
introduction of new forms of data. For instance, Lehavot et al.
raise a series of ethical concerns in relation to the use of suicidal
postings on social media by healthcare professionals [29]. As
they explain, self-disclosure of suicidal thoughts can serve atherapeutic purpose for some patients. Therefore, ill-timed or
misjudged interventions, could lead to unintended harm byfailing to respect a patient’s perceived boundaries of privacy.
These ethical challenges are far from resolved even in cases
where only healthcare professionals are involved. Therefore,
the use of digital psychiatry to automate similar interventions
outside clinical settings are only likely to complicate mattersfurther.
These are just a couple of instances of where the use of digital
psychiatry to automate key aspects of the clinical pathway
raises potential risks—many of which emerge because of key
differences between human psychiatrists and digital psychiatry(see Table I for a list of important differences). These risks exist
despite the aforementioned principles and practices that govern
how formal healthcare is delivered, and sometimes despite
retaining a human-in-the-loop. Therefore, as the use of digital
psychiatry moves outside the remit of the institutional practices
and frameworks of healthcare systems, we can expect
additional ethical challenges to emerge.
In the next section, we introduce some of these risks inseveral sectors that have begun to deploy digital psychiatry:education, employment, financial services, social media, andthe digital well-being industry. While some risks are sectordependent, others are shared across sectors. For instance, thepotential to automate aspects of psychiatry outside of formal
healthcare systems suggests a shift in the distribution ofThis version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

responsibility for the maintenance of public mental health. Thiscan be seen when considering the case of employers, whopreviously did not have a reliable mechanism for assessing themental health of employees, but with the development of digital
psychiatry are now able to take advantage of new opportunities
to support employee well-being. However, it is possible thatthese new opportunities are also establishing new duties of careand responsibilities to intervene (e.g. detecting an employeethat is suffering from high levels of occupational stress andanxiety), which will be hard to discharge without the ethical andlegal frameworks that provide necessary guidance and support.3
These frameworks are already well-established in the case offormal healthcare systems and supported by mechanisms oflegal and professional accountability that are designed tosafeguard public health, but they may fail to translate outside of
their original domain [30]. This is why the use of digital
psychiatry outside of formal healthcare systems raises pressing
ethical challenges related to health and well-being.

III. THE WIDER USE OF DIGITAL PSYCHIATRY
In this section we explore several sectors where digital
psychiatry is used outside of formal healthcare systems,
introducing some key ethical challenges that are exploredfurther in section IV.4
A. Education
AI has already had a significant impact on the organization anddelivery of formal education [31]. In relation to digital
psychiatry, two developments stand out: the use of AI todevelop ‘intelligent tutoring systems’ that facilitate selfdirected learning (e.g. delivering immediate and personalized
feedback), enabling teachers to focus on tasks that machines are
ill-equipped to deliver (e.g. nurturing more creative modes oflearning); and the development of ‘intelligent support systems’for school and university administrators who wish to identify
students who may be at risk of mental health issues and theirrelated challenges (e.g. absenteeism from bullying).
Many examples of intelligent tutoring systems exist,
including online learning platforms that use AI to adapt tostudent performance [32], and wearables that use sensors tomeasure levels of attention and engagement in students andprovide real-time feedback to teachers.5 Therefore, thepossibility of delivering personalized, real-time feedback andhelp to students who require additional support due to mental
health issues is an interesting opportunity for using digital
psychiatry in education. A research team at IBM characterize
this opportunity in terms of a ‘digital assistant’. They note howthe“increased access students have to smart devices that havea multitude of sensors […] can provide data for better
3This concern applies to several areas and is discussed in more detail in
section IV.
4
These sectors are not intended to be exhaustive and our review is not basedon a systematic search of the literature. However, the scope of the analysis issufficient for our purpose to provide motivating reasons for the claim that
digital psychiatry has wider public health issues and ethical implications that
extend beyond formal healthcare systems.

understanding of the student’s context and usage patterns”[35, p. 6976].
A primary concern here is the fact that such contextual
information or usage patterns has also been shown to include
latent information pertaining to an individuals’ mental health,such as levels of anxiety inferred from computer usage logs[11]. As such, the ability to use AI in the classroom to monitor
student performance and engagement may lead to theunintended consequence of unfair treatment of those with
undiagnosed mental health issues (e.g. attention deficit
disorder). While this may also be framed as an opportunity forearlier assessment, this would only be true if the respective
educational institution has the necessary facilities (e.g.
counselling services) to ensure that the student receives
appropriate support. Otherwise, additional monitoring ofstudent performance and engagement may simply create a
feedback loop that further exacerbates mental health issues,
prompting increased depression, anxiety, and decreased selfesteem, which are associated with academic pressures [36].
Similar concerns also extend to intelligent support systems,
which could (it has been suggested) be used by school anduniversity administrators to identify students that may be inneed of more targeted support [37], and used to predict selfharm, drug abuse and eating disorders [38]. Further examples
include the use of mobile phone location data for detection ofdepression and anxiety in undergraduate students [39], and theanalysis of social media data to detect vulnerable students [40].
When we consider that educational institutions typicallyhave a duty of care towards young people, it is understandable
that they would be interested in employing digital psychiatry tosupport this duty. However, the scope of this duty, and the(perceived) protection it affords to students can vary. A recent
report commissioned by the UK All-Party Parliamentary Groupon Data Analytics notes,“whilst data can help with this role, it can also hurt theindividual when mistakes are made, potentiallyirreparably. The Inquiry was given an example of a
student who had been mortified when their disability was
revealed to their lecturer, and the comment was made that
although the scale (and therefore severity) of the error
might be very low for the organisation committing it, forthat individual it could be life-changing” [41, p. 32].
Examples like this one highlight an ethical and practical needto consider the competing interests, values, and priorities of
those who are affected by digital psychiatry. For instance, a
student who suffers with mental health issues, and wishes tokeep this fact private, may find the use of digital psychiatry byadministrative staff to be a violation of their privacy, and judge
it as a disproportionate action taken under the auspices of a dutyof care.

5
A Harvard-based technology firm have developed a product known as
FocusEDU, which they describe as providing “the world’s first technology thatcan quantify real-time student engagement in the classroom”, in order to enable
teachers to “track student engagement and class attention levels as they’re
happening” [33]. In addition, a research group at MIT’s Media Lab, have also
developed a similar product (AttentivU) that seeks to improve attention throughreal-time monitoring of a user’s engagement [34]. Both devices employ
physiological sensors (i.e. electroencephalography) to measure engagement.

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

B. Employment
Like education, there are two strands to highlight in connection
with the use of digital psychiatry in employment: its use tosupport management practices and hiring practices (i.e. HR
analytics).
Koldijk, Neerincx, & Kraaij demonstrate how unobtrusive
sensors (i.e. computer logging, facial expressions, posture andphysiology) can be used to collect employees’ behavioral datato train automatic classifiers to infer working conditions andstress-related mental states [42]. While such techniques could
support employees’ well-being, we cannot ignore the potential
risk of privacy violations, especially in light of a recent reportabout the increasing use of monitoring techniques by large
firms, to track employee behaviors while at work or keystroke
loggers to monitor employee communication [43]. A recent
qualitative study explores how the use of digital technologies tomonitor employees is related to self-reported impacts onanxiety, stress, and depression, and argues that the use of such
systems impacts negatively an employee’s mental health, andmay lead to a loss of commitment, professional identity, andself-confidence [44]. The risks raised in relation to the use ofdigital psychiatry in education and the potential to create
feedback loops that exacerbate mental health issues also apply
here. However, employers are less likely than educators to havethe necessary human resources to support employees (e.g.
access to counsellors), and the risks for individual privacy
remain high. For instance, continuous monitoring, could beconstrued as surveillance and ‘policing’ for compliance, andusers may perceive the risk that the collected data is not justused for the purpose of promoting their well-being but also toensure they are abiding by the ‘rules’ [45].
Turning to the use of digital psychiatry to support hiring
practices, it has been noted that some firms are exploring how
AI can improve HR-analytics by mining sources such as social
media (e.g. LinkedIn profiles) [46]. This, along with theacknowledgment that “Facebook data has also been used toinfer dark side [sic] personality traits, such as psychopathy […],and narcissism […]” [46, p. 14], raises substantial ethicalconcerns related to privacy and discriminatory profiling.
AI may also be used to infer relevant information frominterview data, possibly using design strategies fromgamification to structure interviews (e.g. to infer cognitive
ability or problem-solving ability). Chamorro-Premuzic et al.
argue that these methods could help standardise the interview
process “making it more objective and cost efficient while
reducing the impact of interviewer biases” [46, p. 14], while
also proving beneficial to firms looking to identify the besttalent. For instance, Chen et al. developed an automated system,
which is able to extract relevant behavioral and psychologicalfeatures from video interviews [47]. Part of this process relied
on well-known linguistic assessment tools (e.g. Linguistic
Inquiry and Word Count, LIWC6), which detects
psycholinguistic properties associated with positive andnegative emotions. However, this system also relies on theautomated scoring of an individual’s speaking skills thatcomprised multiple dimensions, including fluency, prosody,and pronunciation.
Although the use of intelligent systems for hiring practices ishighly varied in nature, there are two specific concerns to which
we can point. First, individuals with Social Anxiety Disorder(SAD) sometimes speak slower and more quietly than others[49]. It is realistic to imagine that this behavioral feature may
reduce employment prospects for individuals suffering from
SAD. For example, forms of algorithmic discrimination may
place too high a weight on prosodic features (e.g. due to biases
inherent in their training datasets or feedback loops that
propagate existing biases further and increase the risk of
discrimination). Second, employment can have myriad benefits
for mental health and well-being, such as potentially supportive
social relationships [50]. The misuse of digital psychiatry inhiring, therefore, may act as a barrier for those suffering withmental health issues, preventing them from accessing social
environments that could be beneficial to their mental health andwell-being.7

6
LIWC is a popular method in computational linguistics for inferring
psychological information based on an individual’s language use [48].
7
It should be noted that a recent review showed how digital psychiatry
techniques are still in their infancy in employment and not widely used [51].

However, the fact that these tools and techniques could enable employers to cut
costs both at the level of management and hiring suggests that their use is likely
to grow.

C. Financial Services
Mental health disorders can lead to serious financial difficulties
and further exacerbate an individual’s level of suffering. For
instance, disorders due to addictive behaviors (e.g. gambling or
mood disorders) are associated with compulsive buying andspending that place heavy burdens on individuals [52].
Technologies already exist to mitigate the negative impacts on
one’s finances, like spending blocks for customers who wish tolimit purchases during specific times (e.g. when at risk frommanic episodes). These technologies may provide more
immediate support to individuals, offering the necessary means
to manage their finances in ways that are responsive to theirindividual needs. However, financial services companies are
increasingly looking at whether AI can be used to detectproblematic behavioral patterns in transaction data before theyarise. For example, the UK’s Financial Conduct Authority(FCA) notes that one firm uses speech analytics software toparse calls for “triggers or clues to vulnerability, such as
mention of illness, treatment, diagnosis, depression” [53, p. 81].
And, Evans notes that financial services companies areinterested in the potential of combining transaction datasetswith additional sources of data, such as social media usage orbiometric signals (e.g. heart-rate variability) [54]. However, sofar, ethical, legal, and regulatory concerns have impeded thesedevelopments [54].
In recognition of these concerns, the FCA has established acode of good practice that seeks to impose a duty of care offinancial service providers towards vulnerable consumers,
namely, “someone who, due to their personal circumstances, isespecially susceptible to detriment [e.g. an individual with amental health issue], particularly when a firm is not acting withappropriate levels of care” [53, p. 20].

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

As part of their code of good practice, the FCA encourages
proactive intervention, rather than just reactive support, forcustomers who choose to disclose information about theirmental health. There are many ways in which the term‘proactive intervention’ could be construed. One suggestionthat the FCA offers is to “spot abnormal patterns or danger
signals and act before people are actually in difficulties” [53, p.
88]. While the intention behind this suggestion may bebeneficent, its lack of specificity means that it is up to firms toexercise caution when deciding how to act upon the guidance,
raising two main concerns.
First, there may be no clear justification for using digital
psychiatry instead of alternative strategies that upholdprinciples of data minimization [55] and offer support without
explicit diagnosis or identification. Moreover, once identified,there is a risk that such consumers may become targets of
unscrupulous actors [56]. A narrow focus on the opportunities
that digital psychiatry offers carries a risk of creating an
imbalanced incentive structure, whereby alternative strategies
may be overlooked (e.g. universal design methods that aim tosupport and empower all users [57]).
Second, as Evans asks in response to the FCA’s
recommendation of proactive intervention: “[d]oes identifyingthat a customer may be at risk place an ethical obligation on a
firm to take action, even if the customer has not sought supportand may not know they’re unwell?” [54, p. 3]
In this case too, we see a concern about how far the duty ofcare of financial services organizations extends, and whether
they have a right to intervene.
D. Social MediaSocial media receives a lot of attention in relation to the use ofdigital psychiatry techniques for suicide prevention [58], [59].
Part of the attention is due to empirical uncertainty about social
media’s own role in increased suicide rates among adolescents,
and the differential impact on males and females [60], [61].8
Because many people who are suicidal are unknown tohealthcare professionals, some have begun to ask what rolesocial media can play in detecting suicidal ideation anddelivering targeted prevention [63]. Here, a key aim is to use
digital psychiatry to develop risk assessment tools. These tools
can be viewed as attempting to augment and automate thetraditional psychiatric pathway, perhaps by adding an earlier‘screening’ stage. Risk assessment tools, as Velupillai et al.
note,“may be powerful even if the [positive predictive value]of the predictions are low, because they can be deployedon a large scale.” [64, p. 3]
The ability to operate at scale is what makes digital
psychiatry so promising for deployment on social media
platforms [65]. However, this is a double-edged sword, as thelarger scale also compounds the potential risk of harm (i.e.
greater number of inaccurate assessments). Part of this riskstems from the widespread use of natural language processing
8
For instance, the World Health Organisation are working towards theglobal target of reducing the suicide rate in countries by 10% by 2020, and as
part of their efforts are investigating the “supplementary role that the Internet(NLP) techniques to extract relevant psychiatric features in text
(e.g. risk factors such as psychiatric stressors [66]). Gkotsis etal. present findings from a NLP study, which classifies social
media posts as indicative (or not indicative) of a mental healthdisorder, and links particular posts to specific disorders [67].
However, as they note, such a strategy may be insufficient todiagnose users with a specific mental health disorder—for
instance, they could be posting about a friend or family
member.
More recently, Merchant et al. showed how machine learningalgorithms trained on social media posts were more accurate
than those trained on demographic information, where theground truth was extracted from consenting patient’s electronic
health records [68]. This can help overcome the previous
problem of determining the ground truth, but it is unlikely to bea strategy to be deployed outside of clinical settings due to thelimited access to patient records.
While there are undoubtedly many positives to social media
companies using their size and influence for good in this way,
there are also serious ethical concerns related to the use ofdigital psychiatry by social media platforms that need urgent
consideration [59]. For example, there is a lack of transparency
regarding how risk assessment tools are developed and operate,
leading to diminished trust in the ability of social media
platforms to use such tools in an ethical and safe manner [58].
Goggin reports that once Facebook’s risk assessment tools
identify a vulnerable user on the basis of their profile, twooutreach actions can take place, “[r]eviewers can either send theuser suicide resource information or contact emergency
responders” [65]. However, it is unclear how often each of theseactions takes place, or what determines whether a user is highor low risk. This lack of transparency is problematic because
social media platforms can provide users with valuable spaces
to enquire about issues like side effects from treatments, sharecoping skills, and even engage in therapeutic forms of selfdisclosure, thereby feeling less isolation or stigma [29], [67].
The recurring issues surrounding the transfer of responsibility
from formal healthcare systems to the private sector and theethical challenges of determining when there is a right or duty
to intervene, previously highlighted in the case of education andhuman resources, also apply here.
E. The Digital Well-Being Industry
In 2013, the World Health Organisation recommended thedevelopment and implementation of “tools or strategies for selfhelp and care for persons with mental disorders, including theuse of electronic and mobile technologies” as a way of“improving access to care and service quality” [69].
Reports focusing on the on-going trends in the digital wellbeing industry note that the market is worth nearly $8 billion in
the United States [70], and that there are now more than
300,000 health-related mobile apps available [71]. However, it
is not clear whether the digital well-being industry delivers on
promises—like improved access, real-time support, and more
and social media are playing in suicide communications” (World HealthOrganisation, 2014, p. 32).

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

empowered users—or whether the proliferation of digitaltechnologies for personal well-being create new risks for publichealth, leading to additional challenges for formal healthcare
systems.
Unlike the previously discussed sectors, the use of digital
psychiatry within the digital well-being industry is too diffuse
to categorize neatly. However, two key developments arecommercially-available mobile health (mHealth) devices (e.g.
mobile apps and fitness trackers) and online services (e.g.
chatbots). These technologies offer users a range of services
that aim to enhance a user’s ability to monitor or track healthrelated behaviors in a more self-directed manner, by using
digital psychiatry to personalize feedback and offer real-time
support [5]. However, Barras also draws attention to a growing
reliance on unlicensed therapists, noting concerns fromhealthcare professionals about the potential of a reduced quality
in mental healthcare, and questioning the efficacy of the formsof motivational therapy offered by these services [15].
Similar concerns about efficacy have also been raised withrespect to services that claim to offer support for mental healthissues such as anxiety, schizophrenia, post-traumatic stressdisorder, eating disorders, and addiction [5]. Larsen et al.
evaluate the quality of health-related claims made bycommercially-available mHealth apps, noting that 36% of theapps evaluated made claims about improvements in knowledge
or skills to support self-management, 30% made claims about
improvements in symptoms or mood, and 10% claimed theability to diagnose or detect a mental health condition [72].
However, only two apps offered details of the direct evidence
supporting these claims, and only one provided citation details
to the scientific literature. Similar findings regarding the lack of
empirical support exist for other commercially-available
services [73], [74], raising questions about the informational
value of digital psychiatry in the digital well-being industry.
The concern about efficacy becomes more pressing when we
acknowledge that these products or services may also distanceusers from healthcare professionals, who may be able tomitigate some of the worst effects. In the UK, for example,
products and technologies that are classified as medical devices
are heavily regulated by the Medicines and Healthcare products
Regulatory Agency. Products and services within the digitalwell-being industry often currently fall outside of thisregulatory scope. As Anthes notes,“an app that claims to prevent, diagnose or treat a specificdisease is likely to be considered a medical device and to
attract regulatory scrutiny, whereas one that promises to'boost mood' or provide 'coaching' might not”. [5]This is why so many services rely on unlicensed therapists (i.e.
‘motivational therapist’ or ‘wellness coach’) or hide behind
disclaimers to avoid scrutiny [72].
Healthcare services and governments across the globe have
responded to these developments [75], [76]. For instance, theAmerican Psychiatric Association has designed an evaluation
framework to guide informed decision-making around the useof smartphone apps in clinical care [6]. These are positive

developments, but nevertheless have limited reach due to theirfocus on clinical contexts, rather than the wider sectors
discussed in this paper.
Many of the risks analyzed throughout this section are a
result of the transfer of responsibility from traditional
healthcare providers to institutions, organizations, andindividuals. This raises a series of questions, such as whether
an organization has a duty or right to intervene on the basis ofinferred information; whether an institution has the necessary
resources to support individuals (e.g. students or employees);
and whether an individual has the decisional capacity and healthliteracy to take responsibility for their mental health using
commercially-available services. Table II summarizes theseissues, for each the sectors identified in this section, and we
explore them further in the next section.

9This acknowledgement is part of the recovery approach [81, p. 527], which
views recovery as “a deeply personal, unique process of changing one’s
attitudes, values, feelings, goals, skills, and/or roles. It is a way of living asatisfying, hopeful, and contributing life even with limitations caused by illness.
Recovery involves the development of new meaning and purpose in one’s life
as one grows beyond the catastrophic effects of mental illness.”

IV. THE ETHICS DIGITAL PSYCHIATRY: OPPORTUNITIES AND
RISKS

In this section we expand on the ethical issues identified in theprevious section. As these issues are not all common instancesof ethical principles, values, or standards, etc., we do not
attempt to organize them within an over-arching framework(e.g. mapping them to principles of bioethics or principles thatgovern the design and development of AI [77], [78]). We also
note open questions and positive recommendations that we
expect will be central to ongoing discussions of digital
psychiatry.
A. Autonomy
The concept of ‘autonomy’ is an important concept in bothbioethics [30] and moral and political philosophy more broadly[79]. While its usage varies, for present purposes it is sufficient
to note that its importance and value reflects a respect for bothan individual’s capacity to decide and freedom of whether todecide [80]. This is because medical decisions are understoodto involve value judgements (e.g. comparative quality of life
assessments for two treatment options). For instance, a patient
experiencing depression may be fully informed by their
psychiatrist about their mental health and the options available
to them in terms of recovery, but nevertheless autonomously
decide to forego any treatment because their condition may be
an important part of their self-identity.9
While these features of autonomy are important in bioethics,
we also need to go beyond the perspective of patient autonomy
to identify specific ethical issues that arise with the use ofdigital psychiatry outside of clinical settings. For instance, we
can ask:(1) Does the passive collection of data impact a user’s
ability to participate in the decision-making process?
(2) How does the use of digital psychiatry outside of aformal healthcare system change the way that autonomy
is construed?
Starting with (1), we can note how passive forms of data
surveillance and monitoring may create a non-transparent formof informational asymmetry, whereby the user may be unaware

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

which behavioral signals are used as input features forassessment or diagnosis. This informational asymmetry can
impact autonomy in several ways. First, it limits the availabilityof information that an individual may use to decide how topresent themselves, affecting their ability to influence how they
are perceived by others [82]. Second, it may undermine users’
trust in the respective system due to perceived privacy risks,
which could alter the evaluation of possible, desirable actions(e.g. whether to use a support forum). Third, reliance on a predetermined set of features that are used to assess or diagnose anindividual may prevent an individual from being able tochallenge decisions by appealing to other relevant features thatfall outside of the system’s ontology (e.g. an over-reliance on
the use of linguistic features like prosody impacting hiring
outcomes to the detriment of specific applicants).
Moving on to (2), non-clinical uses of digital psychiatry
could lead to concerns regarding autonomy that may not arisein the context of formal healthcare—most notably questions
about whether an organization has the right to intervene on thebasis of inferred information 10. For instance, the use of digital
psychiatry to monitor the psychological well-being of
employees to improve management practices may be perceived
as unjustified forms of surveillance that infringe on an
individual’s right to mental integrity and self-governance.
However, employees may choose not to challenge this use of
personal data for fear of repercussions, instead suffering fromthe increased anxiety that the use of such systems may create.
To address these questions, it is necessary to consider how
different theories of autonomy—including those not developed
for the explicit purpose of medical practices or relationships—
may conceptualize the risks and opportunities of digital
psychiatry across different sectors. There is no easy way aroundthis, other than pursuing further research that explores how
ethical principles, such as ‘autonomy’, require contextual
specification in order to provide useful practical guidance fordevelopers and policymakers in different sectors.11
B. Model Generalizability
Where predictive models have been trained on nonrepresentative samples (e.g. limited demographic variation orsets of features in the dataset), there is a risk of embedding bias
into the performance of the respective classifiers (e.g. digital
psychiatry in hiring). This connects to the issue of model
generalizability, which refers to the performance of a decisionfunction (e.g. a machine learning algorithm trained on one
dataset) to new cases or contexts (e.g. temporal, geographic,
genetic, cultural, or disease related) [19]. Model
generalizability is especially problematic in psychiatry, where
there is widespread acceptance that different demographics
experience mental health in varied ways (e.g. different social

10
Our use of the term ‘right’ is used in a loose sense and does not reflect theendorsement of a particular normative theory.
11
Unfortunately, this article is not the appropriate place to expand on thesecomplex normative questions, which will be expanded on in future work.
12
Although individual models could be developed for specific use cases, thiswould not necessarily fully overcome all of the issues discussed in this subsection.

attitudes towards a particular disorder impacting severity ofsymptoms).
If the aim of digital psychiatry is to construct generalizable,
predictive models to be deployed in an online environment, thesample data needs to be sufficiently representative of theheterogeneous population to which it will be applied.12
Consider again the case of early screening for suicide
prevention. It is important to note that differences indemographic and personal characteristics, like gender [83], leadto differences in how individuals seek help. Freeman et al. notethat“[c]onsidering the differences in suicidal intent between
males and females […], gender targeted prevention andintervention strategies would be recommended.” [83, p. 1]
This is why a single model for detecting and acting upon
indicators of suicidal ideation is unlikely to be suitable.
Given the impact on individual health and well-being, aside
from being an empirical matter, ensuring adequate model
generalizability is an inherently ethical responsibility fordevelopers to ensure the technology they are deploying is fitfor-purpose. This is why the assessment of any new service
should not be carried out in isolation from the rest of the system
in which it will be embedded. For example, an app that canidentify early signs of depression could be dangerous if there is
not a well-functioning healthcare system to support theindividual. However, as many of these digital solutions aredeveloped by commercial companies, it is not immediately
apparent who is responsible for conducting this assessment. In
state-provided, single-payer systems like the UK’s National
Health Service (NHS), it would be the responsibility of thosecommissioning the service. However, if the service is provided
as ‘direct-to-consumer’, this option may not be available. This
creates a scenario in which the onus may sit with the technology
providers, requiring them to accept a public healthresponsibility that may admittedly be viewed as asupererogatory action13.
One viable option is for public health organizations togenerate the evidence of the public health risks of commercial
uses of digital psychiatry and use this evidence base as leverage
to instigate partnerships with developers. For example, this
could be facilitated by targeting the process of uploading apps
to commercial app stores, so that developers receive a message
notifying them that before they are able to upload a healthrelated app it must be submitted to review by the relevant healthauthority for the purpose of ensuring public safety. In this
scenario, app stores would incur a cost in slowing the rate of
uploaded apps, potentially resulting in a slight reduction inincome from this stream, but would also reduce the risk ofliability for supporting potentially dangerous products without
taking on anything other than a signposting responsibility.

13
In ethics, the term ‘supererogatory’ is used to refer to an action that is
morally praiseworthy but not obligatory. For instance, an action that goes“above and beyond” what is morally acceptable.

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

C. Epidemiological Inflation
The use of digital psychiatry outside of formal healthcare raises
a possible risk regarding epidemiological inflation. The generalidea is that potentially well-intentioned tools that seek to
identify mental health issues could contribute to a rise in theirprevalence within the population. For instance, some havestressed the possibility that the use of symptom tracker apps
may worsen the very symptoms they’re designed to help with(e.g. anxiety associated with sleep tracking) [84]. There isinsufficient evidence, at present, to determine how widespreadsuch effects may be for any given condition. This is because
separating the truth from mere panic is complicated by myriad
factors. For instance, as Makin notes,“[c]comparing the rate of mental-health diagnoses intoday’s adolescents with those in previous generations iscomplicated by changes in definitions and a greater
willingness to report mental-health problems.” [85]The lack-of empirical certainty does not mitigate concerns
entirely. Research into placebo and nocebo effects suggests thatphysiological changes can occur as a result of learning of one’s
genetic risk for disease like Alzheimer’s, independent of actual
genetic risk [86]. If this phenomenon extends to scenarios
where a user learns about wider risk factors associated withother mental health issues, then there would be further reason
to adopt a more cautious attitude to the manner in which digital
psychiatry may present information to users.
There is a trade-off here between competing interests ofseeking to do good (beneficence) versus seeking to avoid harm
(non-maleficence). In lieu of further empirical evidence about
the causal contribution of digital psychiatry to mental healthissues, it is likely that policymakers and healthcare
professionals will fall back on the more cautious (andpragmatic) principle of non-maleficence. This has implications
for the way in which ‘diagnostic’ uses of digital psychiarty are
calibrated. Developers are likely to follow the ‘smoke detector
principle’ (i.e. give more ‘false positives’ than ‘false
negatives’), especially in the absence of clear guidance on
liability, as it is believed that less harm can come to theindividual from a false ‘referral’ for further treatment than frommissing a potentially life-limiting symptom of mental healthcrisis. Without evidence as to whether these ‘diagnoses’ can belinked to greater levels of health anxiety, however, it is notpossible to say whether such a policy is justifiable.
Healthcare policymakers should commit to funding researchin this area, so that the necessary evidence can be generated to
inform appropriate and proportionate governing policies.
Research should include elements related to cognitive andbehavioral sciences, including design constraints from humancomputer interaction, to specify the way in which information
is displayed impacts the relevant health outcome. This
information will enable policymakers to develop detailed
guidance and standards to govern the design and use of digital
psychiatry. Such standards must include guidance on how
liability will be dealt with if information is interpreted in a waythat is harmful, or if a diagnosis is missed or over-inflated, to
14
When compared with the average 2-week waiting time for booked inperson consultations [88].

minimize the extent to which false positives are seen as the‘safer’ option.
D. Earlier Screening and Improved Access
It is well-understood how early identification and treatment of
mental health issues such as depression can reduce the risk ofsuicide [25]. If digital psychiatry can deliver earlier screening
and improved access, it is certainly worth further attention, as
it may improve the sort of ‘just-in-time’ interventions that could
save lives through more efficient prioritizing of resources [87],or help reduce unnecessary distress in sectors like financial
services or education. While this may justify the use of risk
assessment tools, it is important to stress that their use may also
lead to some loss of privacy for individual users, due tonecessary data collection. Aside from risks to privacy, aquestion remains as to whether the operators of relevant
platforms or technology have a right to intervene if theirautomated tools identify a user at risk. The lack of agreement
upon standards or codes of conduct for practices outside formal
healthcare—aside from implicit organizational norms orcommitments to principles such as data minimization—will
likely serve as an obstacle to reaching any agreement on thisquestion.
Enabling risk assessment tools to provide genuine benefits tothe health outcomes of individuals is difficult because when
diagnoses are made outside of the formal healthcare systemthey act more like inefficient and ineffective referrals. Whilerisk assessment tools might suggest to an individual that theyshould seek medical advice, they do not necessarily provide
them with access to the appropriate support or treatment.
Instead, it is entirely possible that an individual who has beenassessed outside of the formal healthcare system must be
assessed again in a clinical setting before they are able to accesstreatment. In these cases, risk assessment tools do not “improveaccess” for individuals in a meaningful way, while they may
create a psychologically distressing situation where individuals
must live with the knowledge of a potential diagnosis for an
extended period of time without any support.
Even when digital psychiatry does improve access in ameaningful sense, it may not do so fairly. Consider Babylon’s
NHS England service GP at Hand, which provides patientswith video-based GP consultation with minimal waitingtimes14. This service has been criticized for ‘cherry picking’patients—improving access for those who are digitally-savvy
and relatively healthy whilst worsening access for those who
still want to (or can only) meet GPs in-person [89]. On the one
hand, there is nothing inherently ‘wrong’ with providing
targeted services, or with giving individuals the ability to assessthemselves; on the other it is important that the service’s impact
on the system as a whole is considered, so that ‘improved
access’ does not lead to new forms of health inequality. To
avoid this, healthcare system governing bodies should invest in
the ability to model routinely the impact of digital psychiatry
services in much the same way that care pathways in hospitals
can be A/B tested in a modelling environment to maximize

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

efficiency of patient flow. This would give healthcare systems
the ability to take necessary actions to ensure improved accessgenuinely means improved access.
E. Greater Engagement and Empowerment
Digital psychiatry may lead to greater engagement andempowerment for patients. However, as Morley and Floridi
note, what constitutes empowerment is far from clear [90]. Thisis because, in many instances, the concept is predicated on anoften-unfounded assumption that reflection on information (forexample one’s medical record) will automatically result inrational action. Yet, there is a risk of harm to individuals who
do not have the ‘right capacities’ to engage critically with healthinformation in the moment that they are presented with it, andwho may be unable to identify accurately ‘wrong’ or nonvalidated sources of information. This risk has increased asdigital psychiatry techniques have begun to operate outside offormal healthcare systems which can act as information
gatekeepers protecting those who lack the decisional capacity
to critically assess informational value-—an acute problem inthe context of mental health disorders [91]—from potentiallyharmful information. For instance, a study found that somecommercially-available apps—outside of the gatekeeping
capacity of the formal healthcare system—make false claims,
such as bipolar disorder is contagious or that drinking hardliquor before bed is a good way to deal with a manic episode[92]. Thus, Burr and Morley argue that the goal of genuine
empowerment requires greater attention to possible barriers toengagement, which prevent individuals from seeking treatment
for mental health issues in the first place [93].
Given the complex nature of mental health (e.g. personspecific contextual factors such as environmental influence),
however,theclinically-demonstrated
efficacyoftechnologically-mediated interventions may be restricted to theoriginal context in which the study was conducted [94].
Therefore, while genuine empowerment demands prior focus
on the barriers to engagement, the complex nature of thesebarriers in relation to mental health limits the scope of thisopportunity. For example, lack of social support may prove to
be a significant psychological burden for some, whereas
economic factors may represent more serious barriers toengagement and empowerment for others. It is for this reason
that digital psychiatry interventions should not be viewed as‘silver bullets’, capable of cutting through existingsocioeconomic complexities and creating entirely new methodsof care. This is particularly important while the evidence of
their social impact at scale is lacking.
There is also an ethical concern related to the intended goalof these technologies, especially those within the digital wellbeing industry. It is possible that some uses of digital psychiatry(e.g. chatbots) do little more than provide paid-for access todigital forms of talk therapy. As such, it could be argued thatthese services commercialize an important social function thathas typically been provided by friends and families. Instead of
15
Where the apps in question are part of the wider ecosystem of formal
healthcare systems, for example in the instance of the NHS Apps Library, theproviders of these service can be encouraged, or contractually obliged, toempowering users, these services could contribute to a potential
diminishment of the inherent value of social relationships byremoving the opportunity for an actual friend to engage invirtuous forms of compassionate listening, and possibly offer
more insightful support due to a wider understanding of thecontextual factors (e.g. lifestyle, previous experiences).
F. Real-Time Support
A well-known challenge for public health is adherence totreatment. This is particularly problematic in cases of on-going
therapy for chronic conditions, where patients are often
expected to engage in long periods of self-directed treatment
without access to a medical professional. Digital psychiatry
may help with this challenge if implemented into alreadyexisting, ubiquitous mobile technologies. This is why digital
psychiatry techniques that can extract relevant informationfrom ecologically-valid biometric signals are attracting
increasing attention (e.g. monitoring of student engagement inclassrooms).
The value-sensitive design of these technologies is key tograsp these opportunities. For example, Mohr et al. state thatadherence itself is not sufficient for improved healthoutcomes—improvement often requires “sustained behavior
change over many weeks or months” [94, p. 427]. However,they continue, current“mental health technologies are mainly didactic or
informational, which might not be ideal for promoting
sustained engagement and behavior change for many
people”. [94, p. 427]
In this respect, Peters, Calvo, & Ryan argue that contemporary
research into the satisfaction of psychological needs can be used
to provide valuable design constraints for engineers anddevelopers [95]. This approach can help develop more effective
behavior change strategies, which foster sustained engagementby targeting an individual’s values that are intrinsicallymotivating. While a promising avenue to explore, Mohr et al.
also give reasons to be cautious:“[a]lthough improved design and technology may makemental health technologies easier and more engaging touse in the future, many of today’s mental healthtechnologies require some human support from a coach ortherapist to sustain engagement and obtain substantive,
reliable outcomes.” [94, p. 427]
This suggests that the use of digital psychiatry to automate
aspects of the user experience may need to be limited, even if it
results in some additional friction. Instead of ambientmonitoring, services could have emergency features thatconnect users to immediate in-person support services, which
are accessible through voice, touch etc., in order to uphold
accessibility and universal design standards [57]. Push
notifications, which can be disabled on smartphones to give
users greater control, could also be sent to mHealth devices sothat active users can provide feedback on if, how, or why thedevice is enabling them to manage their health better.15 Overprovide feedback data back to the central system so that it can ‘learn’ what apps
or services are most effective at supporting its patients.

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.

time, this may be perceived as best practice, and providers oftechnologies who do not do this may be seen as less focused onachieving positive health outcomes for their users and,
therefore, less trustworthy.
G. Privacy and Trust
As public perception of digital psychiatry techniques increases,
alongside media coverage of privacy violations [96], we will
likely see a greater public concern for online privacy and adiminished sense of trust in organizations that deploy digital
psychiatry [97], [98].
Huckvale et al. found systematic gaps in accredited mental
health app’s compliance with data protection principles (i.e.
unsecure transmission of sensitive or personally identifying
information, lack of local encryption, lack of privacy policies)[99]. Systematic reviews like this are vital to foster compliancebut may prove to be inadequate to prevent individuals fromaltering their online behavior on the basis of perceived privacy
violations. This is sometimes referred to as “self-surveillance”
or “chilling effects” [100], and refers to the psychologicalphenomena whereby a belief that one is being watched leads toaltered behavior.16 Self-surveillance can arise from a perceived
privacy risk (legitimate or not, i.e., a violation of privacy as
determined by a legal framework, or a belief that a social media
platform is engaging in intrusive data collection when it is not).
This highlights the importance of trust and transparency
between service users and platforms [101]. These are crucial,
for instance, to enable the use of media platforms and online
communities for the therapeutic disclosure of personal orsensitive information.
One potential way to tackle privacy risks and lack of trust is
to tighten policies related to tracking and targeted advertising[102]. Current mechanisms for creating ‘safe(r) spaces’ online,
require greater digital literacy than the majority of people
have—for example, using a VPN or the browser extensions(e.g. ‘trackmenot’ extension17)—and, people struggling withsignificant mental health issues may find that their capacity totake these actions is reduced. Thus, the responsibility forcreating these spaces should not be incumbent on individuals.
Instead, it should be discharged by public institutions and social
media platforms. The creation of these spaces could fall, at leastin the UK, under the ‘duty of care’ that the British government
wants to see imposed on online companies [103].

with more attentive design and regulation. Our analysis
uncovers open questions and a series of recommendations that
require further work to be addressed—most notably thequestion of when institutions and organizations have a right or
duty to intervene. The analysis also indicates that the
deployment of digital psychiatry in non-clinical settings could
lead to significant public health risks. Therefore, it is vital that
researchers, health professionals, technology designers, andpolicymakers continue to assess and evaluate how and where
digital psychiatry should be deployed to maximize the benefits
for individual and social well-being, while minimizing the
public health risks.

REFERENCES
[1]

[2]

[3]
[4]
[5]
[6]

[7]
[8]

[9]
[10]

[11]

[12]

V. CONCLUSION
Digital psychiatry is a relatively new phenomenon, both within
and outside of formal healthcare systems, which raises pressing
questions concerning its impact on mental health at both anindividual and social perspective (e.g. social media’s impact on
public health). Answers to these questions will need to beaddressed before we can harness the full potential of digital
psychiatry.
The analysis presented in this article identifies key ethicalchallenges posed by digital psychiatry, which could be avoided
16
For instance, chilling effects were noted in response to Edward Snowden’srevelation of widespread surveillance by the NSA [100].

[13]

[14]

[15]

World Health Organisation, ‘Depression and Other Common Mental
Disorders: Global Health Estimates’, World Health Organisation,
Geneva, 2017.
G. P. Martin, E. Sutton, J. Willars, and M. Dixon-Woods, ‘Frameworks
for change in healthcare organisations: A formative evaluation of the
NHS Change Model’, Health Serv Manage Res, vol. 26, no. 2–3, pp.
65–75, Aug. 2013, doi: 10.1177/0951484813511233.
T. Foley and J. Woollard, ‘The digital future of mental healthcare andits workforce’. Health Education England, 2019.
P. Garg and S. Glick, ‘AI’s Potential to Diagnose and Treat Mental
Illness’, Harvard Business Review, 22-Oct-2018.
E. Anthes, ‘Mental health: There’s an app for that’, Nature News, vol.
532, no. 7597, p. 20, Apr. 2016, doi: 10.1038/532020a.
J. Torous et al., ‘Towards a consensus around standards for smartphone
apps and digital mental health: Towards a consensus around standards
for smartphone apps and digital mental health’, World Psychiatry,
vol. 18, no. 1, pp. 97–98, Feb. 2019, doi: 10.1002/wps.20592.
C. Burr and N. Cristianini, ‘Can Machines Read our Minds?’, Minds
and Machines, Mar. 2019, doi: 10.1007/s11023-019-09497-4.
J. C. Eichstaedt et al., ‘Facebook language predicts depression in
medical records’, Proceedings of the National Academy of Sciences,vol. 115, no. 44, pp. 11203–11208, Oct. 2018, doi:
10.1073/pnas.1802331115.
A. G. Reece, ‘Instagram photos reveal predictive markers ofdepression’, p. 12, 2017.
S. Saeb, E. G. Lattie, S. M. Schueller, K. P. Kording, and D. C. Mohr,‘The relationship between mobile phone location sensor data anddepressive symptom severity’, PeerJ, vol. 4, p. e2537, Sep. 2016, doi:
10.7717/peerj.2537.
Y. Fukazawa, T. Ito, T. Okimura, Y. Yamashita, T. Maeda, and J. Ota,
‘Predicting anxiety state using smartphone-based passive sensing’,
Journal of Biomedical Informatics, vol. 93, p. 103151, May 2019, doi:
10.1016/j.jbi.2019.103151.
Y. Nakai, T. Takiguchi, G. Matsui, N. Yamaoka, and S. Takada,‘Detecting Abnormal Word Utterances in Children With Autism
Spectrum Disorders: Machine-Learning-Based Voice Analysis
Versus Speech Therapists’, Perceptual and Motor Skills, vol. 124, no.
5, pp. 961–973, Oct. 2017, doi: 10.1177/0031512517716855.
D. Leightley, V. Williamson, J. Darby, and N. T. Fear, ‘Identifying
probable post-traumatic stress disorder: applying supervised machinelearning to data from a UK military cohort’, Journal of Mental
Health, vol. 28, no. 1, pp. 34–41, 2019, doi:
10.1080/09638237.2018.1521946.
G. Coppersmith, R. Leary, P. Crutchley, and A. Fine, ‘Natural
Language Processing of Social Media as Screening for Suicide Risk’,
Biomedical Informatics Insights, vol. 10, p. 117822261879286, Jan.
2018, doi: 10.1177/1178222618792860.
C. Barras, ‘Mental health apps lean on bots and unlicensed therapists’,
Nature Medicine, Mar. 2019, doi: 10.1038/d41591-019-00009-6.

17
TrackMeNot is a browser extension that helps protect web searchers fromsurveillance and data-profiling: http://trackmenot.io

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.
[16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24]
[25]
[26]

[27]

[28]

[29]

[30]
[31]

[32]

[33]

[34]

[35]

G. M. Lucas et al., ‘Reporting Mental Health Symptoms: Breaking
Down Barriers to Care with Virtual Human Interviewers’, Frontiersin Robotics and AI, vol. 4, Oct. 2017, doi: 10.3389/frobt.2017.00051.
A. Madan, M. Cebrian, D. Lazer, and A. Pentland, ‘Social sensing for
epidemiological behavior change’, in Proceedings of the 12th ACM
international conference on Ubiquitous computing - Ubicomp ’10,
Copenhagen,Denmark,
2010,
p.
291,
doi:
10.1145/1864349.1864394.
B. Reeves et al., ‘Screenomics: A Framework to Capture and Analyze
Personal Life Experiences and the Ways that Technology Shapes
Them’, Human–Computer Interaction, pp. 1–52, Mar. 2019, doi:
10.1080/07370024.2019.1578652.
D. B. Dwyer, P. Falkai, and N. Koutsouleris, ‘Machine Learning
Approaches for Clinical Psychology and Psychiatry’, Annual Reviewof Clinical Psychology, vol. 14, no. 1, pp. 91–118, 2018, doi:
10.1146/annurev-clinpsy-032816-045037.
Department of Health and Social Care, ‘Code of conduct for data-driven
health and care technology’, GOV.UK, 19-Feb-2019. [Online].
Available:
https://www.gov.uk/government/publications/code-ofconduct-for-data-driven-health-and-care-technology/initial-code-ofconduct-for-data-driven-health-and-care-technology. [Accessed: 15Apr-2019].
D. C. Mohr, M. Zhang, and S. M. Schueller, ‘Personal Sensing:
Understanding Mental Health Using Ubiquitous Sensors and Machine
Learning’, Annu. Rev. Clin. Psychol., vol. 13, no. 1, pp. 23–47, May
2017, doi: 10.1146/annurev-clinpsy-032816-044949.
M. Khan, G. Fernandes, U. Sarawgi, P. Rampey, and P. Maes, ‘PAL: A
Wearable Platform for Real-time, Personalized and Context-Aware
Health and Cognition Support’, arXiv:1905.01352 [cs], May 2019.
D. S. Watson et al., ‘Clinical applications of machine learningalgorithms: beyond the black box’, BMJ, p. l886, Mar. 2019, doi:
10.1136/bmj.l886.
J. Morley and L. Floridi, ‘How to Design a Governable Digital Health
Ecosystem’, SSRN Journal, 2019, doi: 10.2139/ssrn.3424376.
T. Burns, Our Necessary Shadow: The Nature and Meaning of
Psychiatry. London: Penguin, 2014.
L. Johnstone and M. Boyle, ‘The Power Threat Meaning Framework:
An Alternative Nondiagnostic Conceptual System’, Journal of
Humanistic Psychology, p. 002216781879328, Aug. 2018, doi:
10.1177/0022167818793289.
M. Coeckelbergh, ‘Health Care, Capabilities, and AI Assistive
Technologies’, Ethical Theory and Moral Practice, vol. 13, no. 2, pp.
181–190, Apr. 2010, doi: 10.1007/s10677-009-9186-2.
A. Fiske, P. Henningsen, and A. Buyx, ‘Your Robot Therapist Will See
You Now: Ethical Implications of Embodied Artificial Intelligence in
Psychiatry, Psychology, and Psychotherapy’, J Med Internet Res, vol.
21, no. 5, p. e13216, May 2019, doi: 10.2196/13216.
K. Lehavot, D. Ben-Zeev, and R. E. Neville, ‘Ethical Considerations
and Social Media: A Case of Suicidal Postings on Facebook’, Journalof Dual Diagnosis, vol. 8, no. 4, pp. 341–346, Nov. 2012, doi:
10.1080/15504263.2012.718928.
T. L. Beauchamp and J. F. Childress, Principles of biomedical ethics,
7th ed. New York, N.Y.: Oxford University Press, 2013.
N. Sclater, A. Peasgood, and J. Mullan, ‘Learning Analytics in Higher
Education: A review of UK and international practice’, Jisc, Apr.
2016.
J. Kay, P. Reimann, E. Diebold, and B. Kummerfeld, ‘MOOCs: So
Many Learners, So Much Potential ...’, IEEE Intelligent Systems, vol.
28, no. 3, pp. 70–77, May 2013, doi: 10.1109/MIS.2013.66.
BrainCo, ‘FOCUSEDU’, brainco.tech, 2019. [Online]. Available:
https://www.brainco.tech/product/focusedu. [Accessed: 12-Jun2019].
N. Kosmyna, C. Morris, U. Sarawgi, T. Nguyen, and P. Maes,‘AttentivU: A Wearable Pair of EEG and EOG Glasses for Real-Time
Physiological Processing’, in 2019 IEEE 16th International
Conference on Wearable and Implantable Body Sensor Networks(BSN),
Chicago,
IL,
USA,
2019,
pp.
1–4,
doi:
10.1109/BSN.2019.8771080.
R. Kokku, S. Sundararajan, P. Dey, R. Sindhgatta, S. Nitta, and B.
Sengupta, ‘Augmenting Classrooms with AI for Personalized
Education’, in 2018 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 2018, pp. 6976–6980, doi:
10.1109/ICASSP.2018.8461812.

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43]
[44]

[45]
[46]

[47]

[48]

[49]

[50]

[51]

[52]

[53]
[54]

R. Beiter et al., ‘The prevalence and correlates of depression, anxiety,and stress in a sample of college students’, Journal of Affective
Disorders, vol. 173, pp. 90–96, Mar. 2015, doi:
10.1016/j.jad.2014.10.054.
R. J. Watson and J. L. Christensen, ‘Big data and student engagement
among vulnerable youth: A review’, Current Opinion in Behavioral
Sciences,
vol.
18,
pp.
23–27,
Dec.
2017,
doi:
10.1016/j.cobeha.2017.07.004.
R. Manthorpe, ‘Artificial intelligence being used in schools to detectself-harm and bullying’, Sky News, 21-Sep-2019. [Online]. Available:
https://news.sky.com/story/artificial-intelligence-being-used-inschools-to-detect-self-harm-and-bullying-11815865. [Accessed: 24Sep-2019].
P. I. Chow et al., ‘Using Mobile Sensing to Test Clinical Models ofDepression, Social Anxiety, State Affect, and Social Isolation Among
College Students’, J Med Internet Res, vol. 19, no. 3, p. e62, Mar.
2017, doi: 10.2196/jmir.6820.
L. R. Shade and R. Singh, ‘“Honestly, We’re Not Spying on Kids”:
School Surveillance of Young People’s Social Media’, Social Media+ Society, vol. 2, no. 4, p. 205630511668000, Nov. 2016, doi:
10.1177/2056305116680005.
J. Tindale and O. Muirhead, ‘Building Ethical Data Policies for the
Public Good: Trust, Transparency and Tech’, Policy Connect, UK,2019.
S. Koldijk, M. A. Neerincx, and W. Kraaij, ‘Detecting Work Stress in
Offices by Combining Unobtrusive Sensors’, IEEE Transactions on
Affective Computing, vol. 9, no. 2, pp. 227–239, Apr. 2018, doi:
10.1109/TAFFC.2016.2610975.
E. Saner, ‘Employers are monitoring computers, toilet breaks – evenemotions. Is your boss watching you?’, The Guardian, 14-May-2018.
B. Skinner, G. Leavey, and D. Rothi, ‘Managerialism and teacher
professional identity: impact on well-being among teachers in theUK’, Educational Review, pp. 1–16, Jan. 2019, doi:
10.1080/00131911.2018.1556205.
V. Eubanks, Automating inequality: How high-tech tools profile,police, and punish the poor. St. Martin’s Press, 2018.
T. Chamorro-Premuzic, R. Akhtar, D. Winsborough, and R. A.
Sherman, ‘The datafication of talent: how technology is advancing
the science of human potential at work’, Current Opinion in
Behavioral Sciences, vol. 18, pp. 13–16, Dec. 2017, doi:
10.1016/j.cobeha.2017.04.007.
L. Chen et al., ‘Automated scoring of interview videos using Doc2Vec
multimodal feature extraction paradigm’, in Proceedings of the 18th
ACM International Conference on Multimodal Interaction - ICMI
2016,
Tokyo,
Japan,
2016,pp.
161–168,doi:
10.1145/2993148.2993203.
J. W. Pennebaker, R. L. Boyd, K. Jordan, and Blackburn, K, ‘The
development and psychometric properties of LIWC2015.’, Universityof Texas at Austin, Austin, TX, 2015.
V. Silber-Varod, H. Kreiner, R. Lovett, Y. Levi-Belz, and N. Amir, ‘Do
social anxiety individuals hesitate more? The prosodic profile ofhesitation disfluencies in Social Anxiety Disorder individuals’,presented at the Speech Prosody 2016, 2016, pp. 1211–1215, doi:
10.21437/SpeechProsody.2016-249.
M. Modini et al., ‘The mental health benefits of employment: Resultsof a systematic meta-review’, Australas Psychiatry, vol. 24, no. 4, pp.
331–336, Aug. 2016, doi: 10.1177/1039856215618523.
J. H. Marler and J. W. Boudreau, ‘An evidence-based review of HR
Analytics’, The International Journal of Human Resource
Management, vol. 28, no. 1, pp. 3–26, Jan. 2017, doi:
10.1080/09585192.2016.1244699.
T. Richardson, M. Jansen, and C. Fitch, ‘Financial difficulties in bipolar
disorder part 1: longitudinal relationships with mental health’,
Journal of Mental Health, vol. 27, no. 6, pp. 595–601, Nov. 2018,
doi: 10.1080/09638237.2018.1521920.
Financial Conduct Authority, ‘Consumer Vulnerability’, Financial
Conduct Authority, Feb. 2015.
K. Evans, ‘Financial transactions data, AI and mental health: The
challenge’, Money and Mental Health Policy Institute, Jan-2019.
[Online]. Available: https://www.moneyandmentalhealth.org/wpcontent/uploads/2019/01/FCA-financial-transactions-datadiscussion-note-footnotes-at-end.pdf. [Accessed: 16-May-2019].

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.
[55]

[56]

[57]

[58]

[59]

[60]

[61]

[62]
[63]

[64]

[65]

[66]

[67]

[68]

[69]

[70]

[71]

[72]

[73]

ICO, ‘Principle (c): Data minimisation’, Information Comissioner’s
Office, 24-Apr-2019. [Online]. Available: https://ico.org.uk/fororganisations/guide-to-data-protection/guide-to-the-general-dataprotection-regulation-gdpr/principles/data-minimisation/. [Accessed:
24-Sep-2019].
K. Evans, ‘Money and Mental Health submission to the Department for
Digital, Culture,Media and Sport’s consultation on the Centre for
Data Ethics and Innovation’, Money and Mental Health Policy
Institute,
05-Sep-2018.
[Online].
Available:
https://www.moneyandmentalhealth.org/wpcontent/uploads/2018/09/20180905-Centre-for-Data-Ethics-andInnovation-Consultation-Money-Mental-Health-response-.pdf.
[Accessed: 24-Sep-2019].
M. Toboso, ‘Rethinking disability in Amartya Sen’s approach: ICT andequality of opportunity’, Ethics Inf Technol, vol. 13, no. 2, pp. 107–
118, Jun. 2011, doi: 10.1007/s10676-010-9254-2.
T. Basu, ‘Facebook wants to fight teen suicide. Experts aren’t surethey’re doing it right’, MIT Technology Review, 11-Sep-2019.
[Online].
Available:
https://www.technologyreview.com/s/614301/facebook-wants-tofight-teen-suicide-experts-arent-sure-theyre-doing-it-right/.
[Accessed: 24-Sep-2019].
N. N. Gomes de Andrade, D. Pawson, D. Muriello, L. Donahue, and J.
Guadagno, ‘Ethics and Artificial Intelligence: Suicide Prevention on
Facebook’, Philos. Technol., vol. 31, no. 4, pp. 669–684, Dec. 2018,
doi: 10.1007/s13347-018-0336-0.
House of Commons and Science and Technology Committee, ‘Impact
of social media and screen-use on young people’s health’, House ofCommons Science and Technology Committee, Fourteenth Report ofSession 2017–19, Jan. 2019.
D. A. Ruch, A. H. Sheftall, P. Schlagbaum, J. Rausch, J. V. Campo, andJ. A. Bridge, ‘Trends in Suicide Among Youth Aged 10 to 19 Yearsin the United States, 1975 to 2016’, JAMA Netw Open, vol. 2, no. 5,
p. e193886, May 2019, doi: 10.1001/jamanetworkopen.2019.3886.
World Health Organisation, ‘Preventing Suicide: A Global Imperative’,
World Health Organisation, Luxembourg, 2014.
R. Bruffaerts et al., ‘Treatment of suicidal people around the world’, Br
J Psychiatry, vol. 199, no. 1, pp. 64–70, Jul. 2011, doi:
10.1192/bjp.bp.110.084129.
S. Velupillai et al., ‘Risk Assessment Tools and Data-Driven
Approaches for Predicting and Preventing Suicidal Behavior’, Front.
Psychiatry, vol. 10, p. 36, Feb. 2019, doi: 10.3389/fpsyt.2019.00036.
B. Goggin, ‘Inside Facebook’s suicide algorithm: Here’s how thecompany uses artificial intelligence to predict your mental state fromyour posts’, Business Insider, 06-Jan-2019. [Online]. Available:
https://www.businessinsider.com/facebook-is-using-ai-to-try-topredict-if-youre-suicidal-2018-12. [Accessed: 11-Jun-2019].
J. Du et al., ‘Extracting psychiatric stressors for suicide from social
media using deep learning’, BMC Med Inform Decis Mak, vol. 18, no.
S2, p. 43, Jul. 2018, doi: 10.1186/s12911-018-0632-8.
G. Gkotsis et al., ‘Characterisation of mental health conditions in social
media using Informed Deep Learning’, Sci Rep, vol. 7, no. 1, p.
45141, Apr. 2017, doi: 10.1038/srep45141.
R. M. Merchant et al., ‘Evaluating the predictability of medical
conditions from social media posts’, PLoS ONE, vol. 14, no. 6, p.
e0215476, Jun. 2019, doi: 10.1371/journal.pone.0215476.
World Health Organisation, ‘SIXTY-SIXTH WORLD HEALTHASSEMBLY: Comprehensive mental health action plan 2013–2020(WHA66.8)’. 27-May-2013.
D. Agarwal, J. Bersin, and G. Lahiri, ‘Well-being: A strategy and a
responsibility 2018 Global Human Capital Trends’, Deloitte Insights,
28-Mar-2018.
[Online].
Available:
https://www2.deloitte.com/insights/us/en/focus/human-capitaltrends/2018/employee-well-being-programs.html. [Accessed: 11Jun-2019].
The Lancet Digital Health, ‘An app a day is only a framework away’,
The Lancet Digital Health, vol. 1, no. 2, p. e45, Jun. 2019, doi:
10.1016/S2589-7500(19)30031-7.
M. E. Larsen et al., ‘Using science to sell apps: Evaluation of mental
health app store quality claims’, npj Digit. Med., vol. 2, no. 1, p. 18,
Dec. 2019, doi: 10.1038/s41746-019-0093-1.
M. E. Larsen, J. Nicholas, and H. Christensen, ‘A Systematic
Assessment of Smartphone Tools for Suicide Prevention’, PLoS

[74]

[75]

[76]

[77]

[78]

[79]
[80]
[81]

[82]

[83]

[84]
[85]

[86]

[87]
[88]

[89]

[90]
[91]

[92]

[93]

[94]

ONE, vol. 11, no. 4, p. e0152285, Apr. 2016, doi:
10.1371/journal.pone.0152285.
D. R. Bateman et al., ‘Categorizing Health Outcomes and Efficacy ofmHealth Apps for Persons With Cognitive Impairment: A Systematic
Review’, J Med Internet Res, vol. 19, no. 8, p. e301, Aug. 2017, doi:
10.2196/jmir.7814.
A. Ferretti, E. Ronchi, and E. Vayena, ‘From principles to practice:benchmarking government guidance on health apps’, The Lancet
Digital Health, vol. 1, no. 2, pp. e55–e57, Jun. 2019, doi:
10.1016/S2589-7500(19)30027-5.
P. Henson, G. David, K. Albright, and J. Torous, ‘Deriving a practicalframework for the evaluation of health apps’, The Lancet Digital
Health, vol. 1, no. 2, pp. e52–e54, Jun. 2019, doi: 10.1016/S25897500(19)30013-5.
L. Floridi et al., ‘AI4People—An Ethical Framework for a Good AI
Society: Opportunities, Risks, Principles, and Recommendations’,
Minds and Machines, vol. 28, no. 4, pp. 689–707, Dec. 2018, doi:
10.1007/s11023-018-9482-5.
A. Jobin, M. Ienca, and E. Vayena, ‘The global landscape of AI ethics
guidelines’, Nature Machine Intelligence, vol. 1, no. 9, pp. 389–399,
Sep. 2019, doi: 10.1038/s42256-019-0088-2.
G. Dworkin, The Theory and Practice of Autonomy. Cambridge:
Cambridge University Press, 1988.
A. Sen, The Idea of Justice. London: Penguin, 2010.
W. A. Anthony, ‘Recovery from mental illness: The guiding vision of
the mental health service system in the 1990s.’, Psychosocial
Rehabilitation Journal, vol. 16, no. 4, pp. 11–23, 1993, doi:
10.1037/h0095655.
A. Sen, ‘Well-Being, Agency and Freedom: The Dewey Lectures
1984’, The Journal of Philosophy, vol. 82, no. 4, pp. 169–221, 1985,
doi: 10.2307/2026184.
A. Freeman et al., ‘A cross-national study on gender differences insuicide intent’, BMC Psychiatry, vol. 17, Jun. 2017, doi:
10.1186/s12888-017-1398-8.
M. C. Marill, ‘Why Tracking Your Symptoms Can Make You Feel
Worse’, Wired, 30-May-2019.
S. Makin, ‘Searching for digital technology’s effects on well-being’,
Nature,28-Nov-2018.
[Online].
Available:
https://www.nature.com/articles/d41586-018-07503-w. [Accessed:
23-May-2019].
B. P. Turnwald, J. P. Goyer, D. Z. Boles, A. Silder, S. L. Delp, and A.
J. Crum, ‘Learning one’s genetic risk changes physiology
independent of actual genetic risk’, Nat Hum Behav, vol. 3, no. 1, pp.
48–56, Jan. 2019, doi: 10.1038/s41562-018-0483-4.
C. Odgers, ‘Smartphones are bad for some teens, not all’, Nature, vol.
554, no. 7693, p. 432, Feb. 2018, doi: 10.1038/d41586-018-02109-8.
J. Kaffash, ‘Average GP waiting times remain at two weeks despite
rescue measures’, Pulse Today, 02-Jun-2017. [Online]. Available:
http://www.pulsetoday.co.uk/your-practice/practicetopics/access/average-gp-waiting-times-remain-at-two-weeksdespite-rescue-measures/20034534.article. [Accessed: 01-Jul-2019].
A. Downey, ‘Funding model for GP at Hand “not appropriate”, Ipsos
Mori review finds’, Digital Health, 29-May-2019. [Online].
Available:
https://www.digitalhealth.net/2019/05/gp-at-handfunding-not-appropriate-ipsos-mori/. [Accessed: 01-Jul-2019].
J. Morley and L. Floridi, ‘Against Empowerment: How to Reframe theRole of mHealth Tools in the Healthcare Ecosystem (Draft)’. 2019.
T. Hindmarch, M. Hotopf, and G. S. Owen, ‘Depression and decisionmaking capacity for treatment or research: a systematic review’, BMC
Medical Ethics, vol. 14, no. 1, Dec. 2013, doi: 10.1186/1472-693914-54.
J. Nicholas, M. E. Larsen, J. Proudfoot, and H. Christensen, ‘Mobile
Apps for Bipolar Disorder: A Systematic Review of Features andContent Quality’, J Med Internet Res, vol. 17, no. 8, p. e198, Aug.
2015, doi: 10.2196/jmir.4581.
C. Burr and J. Morley, ‘Empowerment or Engagement? Digital HealthTechnologies for Mental Healthcare’, Social Science Research
Network, Rochester, NY, SSRN Scholarly Paper ID 3393534, May
2019.
D. C. Mohr, K. R. Weingardt, M. Reddy, and S. M. Schueller, ‘Three
Problems With Current Digital Mental Health Research . . . and ThreeThings We Can Do About Them’, PS, vol. 68, no. 5, pp. 427–429,
May 2017, doi: 10.1176/appi.ps.201600541.

This version is a preprint. Please cite published version: Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020). Digital Psychiatry: Risks
and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society, 1(1), pp. 21–33.
[95]

[96]
[97]

[98]

[99]

[100]
[101]

[102]

[103]

D. Peters, R. A. Calvo, and R. M. Ryan, ‘Designing for Motivation,
Engagement and Wellbeing in Digital Experience’, Front Psychol,vol. 9, May 2018, doi: 10.3389/fpsyg.2018.00797.
K. McVeigh, ‘Samaritans Twitter app identifying user’s moods
criticised as invasive’, The Guardian, 04-Nov-2014.
M. Taddeo, ‘Trust in Technology: A Distinctive and a Problematic
Relation’, Know Techn Pol, vol. 23, no. 3–4, pp. 283–286, Dec. 2010,
doi: 10.1007/s12130-010-9113-9.
M. Taddeo, ‘Trusting Digital Technologies Correctly’, Minds &Machines, vol. 27, no. 4, pp. 565–568, Dec. 2017, doi:
10.1007/s11023-017-9450-5.
K. Huckvale, J. T. Prieto, M. Tilney, P.-J. Benghozi, and J. Car,
‘Unaddressed privacy risks in accredited health and wellness apps: across-sectional systematic assessment’, BMC Med, vol. 13, no. 1, p.
214, Dec. 2015, doi: 10.1186/s12916-015-0444-y.
J. W. Penney, ‘Chilling effects: Online surveillance and Wikipedia use’,
Berkeley Tech. LJ, vol. 31, p. 117, 2016.
M. Taddeo and L. Floridi, ‘The Debate on the Moral Responsibilities
of Online Service Providers’, Sci Eng Ethics, vol. 22, no. 6, pp. 1575–
1603, Dec. 2016, doi: 10.1007/s11948-015-9734-1.
D. Beer, D. J. Redden, D. B. Williamson, and D. S. Yuill, ‘What isonline targeting, what impact does it have, and how can we maximise
beneﬁts and minimise harms?’, Centre for Data Ethics andInnovation, 2019.
HM Government, Online Harms White Paper. UK: Open Government,2019.