Artificial Intelligence and the Future of Work: A Functional-Identity Perspective

Abstract

The impact of the implementation of artificial intelligence (AI) on workers’ experiences remains underexamined. Although AI-enhanced processes can benefit workers (e.g., by assisting with exhausting or dangerous tasks), they can also elicit psychological harm (e.g., by causing job loss or degrading work quality). Given AI’s uniqueness among other technologies, resulting from its expanding capabilities and capacity for autonomous learning, we propose a functional-identity framework to examine AI’s effects on people’s work-related self-understandings and the social environment at work. We argue that the conditions for AI to either enhance or threaten workers’ sense of identity derived from their work depends on how the technology is functionally deployed (by complementing tasks, replacing tasks, and/or generating new tasks) and how it affects the social fabric of work. Also, how AI is implemented and the broader social-validation context play a role. We conclude by outlining future research directions and potential application of the proposed framework to organizational practice.

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https://doi.org/10.1177/09637214221091823
Current Directions in Psychological
Science
2022, Vol. 31(3) 272 –279
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DOI: 10.1177/09637214221091823
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Imagine a loan consultant working in a large bank and
proud of being responsible for complex loan decisions
involving millions of euros. Then, over time, manage-
ment replaces the previously human-only task of loan
decision making with a faster, automated, more precise,
but unintelligible algorithm. Now picture a surgeon,
operating with real-time artificial intelligence (AI) anal-
ysis of operative videos. This technique reduces the
duration of surgery and improves patients’ outcomes
(examples adapted from Hashimoto etal., 2018; Strich
etal., 2021). In both cases, the workers experience
dramatic changes to core work tasks that challenge
their understandings of their work and themselves in
relation to their work—their identities (Endacott, 2021).
Identities offer a system of self-reference for attitudes
and behaviors and define an individual’s place in soci-
ety (Tajfel & Turner, 1986). Understanding how workers
react to AI-related changes requires examining how
self-understandings are affected. In this article, we
develop an integrative functional-identity framework to
expand current understanding of AI’s effects on workers
and enable a constructive implementation of AI at work.
This analysis is crucial given the rapid expansion of
AI across business sectors, including health care (e.g.,
diagnostic scanning and analysis), operations and pro-
duction management (e.g., resource optimization),
retail (e.g., chatbots), defense and security (e.g., cyber-
crime detection), banking and finance (e.g., stock-
market predictions), and human resource management
(e.g., recruitment and selection). AI implementation is
often guided by business priorities, such as enhanced
1091823CDPXXX10.1177/09637214221091823Selenko et al.Current Directions in Psychological Science 31(3)
research-article2022
Corresponding Author:
Eva Selenko, School of Business and Economics, Loughborough
University
Email: e.selenko@lboro.ac.uk
Artificial Intelligence and the Future of
Work: A Functional-Identity Perspective
Eva Selenko1, Sarah Bankins2, Mindy Shoss3,4, Joel Warburton5,
and Simon Lloyd D. Restubog6,7
1School of Business and Economics, Loughborough University; 2Department of Management,
Macquarie Business School, Macquarie University; 3Department of Psychology, University of Central Florida;
4Peter Faber Business School, Australian Catholic University; 5Lincoln International Business School, University
of Lincoln; 6School of Labor and Employment Relations, University of Illinois at Urbana-Champaign; and
7UQ Business School, The University of Queensland
Abstract
The impact of the implementation of artificial intelligence (AI) on workers’ experiences remains underexamined.
Although AI-enhanced processes can benefit workers (e.g., by assisting with exhausting or dangerous tasks), they can
also elicit psychological harm (e.g., by causing job loss or degrading work quality). Given AI’s uniqueness among other
technologies, resulting from its expanding capabilities and capacity for autonomous learning, we propose a functional-
identity framework to examine AI’s effects on people’s work-related self-understandings and the social environment
at work. We argue that the conditions for AI to either enhance or threaten workers’ sense of identity derived from
their work depends on how the technology is functionally deployed (by complementing tasks, replacing tasks, and/or
generating new tasks) and how it affects the social fabric of work. Also, how AI is implemented and the broader social-
validation context play a role. We conclude by outlining future research directions and potential application of the
proposed framework to organizational practice.
Keywords
artificial intelligence, complementing tasks, generating tasks, identity threat, meaning of work, replacing tasks,
technological change

Current Directions in Psychological Science 31(3) 273
efficiency, which have been criticized for placing too
little importance on workers’ identity processes when
AI supplements work (as in the example of the sur-
geon) or takes away work (as in the example of the
loan consultant). At the same time, AI can also generate
new tasks and create new roles. These changes are situ-
ated in a currently largely polarized discourse about
AI, spanning highly optimistic views about its benefits
(e.g., freeing workers from laborious and repetitive
tasks) to more catastrophic predictions of human unem-
ployment. In this review of AI’s impact on workers’
identities and their subsequent attitudes and behaviors,
we begin by outlining the technology’s functionality.
What Is AI, and Why Is AI Different?
The term “AI” refers to “a collection of interrelated tech-
nologies used to solve problems that would otherwise
require human cognition” (Walsh etal., 2019, p. 2).
Advancements in AI are attributed to wider data access
and collection (big data), greater computational power,
and enhanced modeling approaches (e.g., neural net-
works). “AI” represents a range of technologies using
a variety of computational methods, particularly
machine learning, which involves computerized learn-
ing processes inspired by human intelligence (Walsh
etal., 2019). Through simple (e.g., decision trees) or more
complex (e.g., artificial neural networks, or deep learn-
ing) modeling methods, AI can analyze large data sets
via learning processes that are supervised (i.e., learning
guided by a human) or unsupervised (i.e., machine-
autonomous learning from the data; Walsh et al., 2019).
Common methods that are often referred to as AI also
include natural-language processing (e.g., analysis and
generation of text) and pattern recognition (e.g., identify-
ing associations in data sets; Walsh etal., 2019).
All forms of current AI fall into the category of narrow
AI. This means the technology can undertake a domain-
specific task (e.g., assessing résumés) but, unlike humans,
cannot translate its capabilities to new domains (e.g.,
driving a car after learning to assess résumés). Despite
what the term “narrow” suggests, AI already outperforms
humans in a range of functions through the speed, accu-
racy, and scale of its processing capabilities (Walsh etal.,
2019, p. 34). Debate remains regarding when (and
whether) AI will achieve human-equivalent, or general,
intelligence. Nevertheless, there is widespread societal
debate, often fearful, surrounding the rapid growth of
AI and what that means for the future. This is reflected
in, and largely influenced by, popular-culture depictions
of advancing technologies and debates regarding the
future of work (Cave etal., 2018).
Several aspects of AI differentiate it from prior tech-
nologies. The enhanced predictive and forecasting
abilities of machine-learning algorithms, particularly
through unsupervised learning, extend AI’s capabilities
into tasks traditionally viewed as human cognitive
work. As AI is trained on large data sets, the nature of
these data (e.g., their uncertain representativeness
across populations) and how they are accessed and
secured also raises questions about increasing “datafica-
tion” of workplaces and the fairness of the outcomes
that AI generates. Implementation of AI generates
implications for workers’ privacy and autonomy. The
use of neural network models means AI’s computations
are often a black box, unknowable to AI designers and
end users alike, which has implications for account-
ability and transparency when AI is used for decision
making. AI’s objective technical capabilities also gener-
ate subjective perceptions of the technology. For exam-
ple, perceptions of agency in AI processes, via its
self-learning nature and autonomous deployment, can
make it seem like a quasisocial actor that can act inde-
pendently on behalf of a human, and this has implica-
tions for workers’ self-understandings (Brunn etal.,
2020; Endacott, 2021).
A Functional-Identity Framework for AI
Examination of AI’s impact on workers should be
grounded in its functional capacities and the way the
technology affects specific work tasks (Brynjolfsson &
Mitchell, 2017; Das etal., 2020). Functionally, AI can
(a) complement and support existing human work tasks,
(b) replace existing human work, and/or (c) create new
human tasks and subsequently new work roles (Acemo-
glu & Restrepo, 2020; Brynjolfsson & Mitchell, 2017).
Depending on the nature of the tasks involved (e.g.,
their structure, repetitiveness, and outcomes), but also
economic and structural factors, different occupations
will be differentially affected by AI-related changes. For
example, occupations that rely more heavily on infor-
mation-technology tasks will be more highly exposed,
and exposed at an earlier time, to effects of task substi-
tution, task replacement, and new-task generation,
which will lead to more fundamental changes in occu-
pational structure (Das etal., 2020). AI-related changes,
supplementations, replacements, and new-task genera-
tion may also happen simultaneously in different areas
of an occupation (Brynjolfsson & Mitchell, 2017).
Figure 1 illustrates how changes and challenges asso-
ciated with AI implementation can be understood using
this functional-identity framework. The introduction (or
anticipated introduction) of a nonhuman “intelligent”
actor demands sensemaking, which will affect how
workers think about themselves and experience their
work—generating opportunities for both work-related
identity threat and work-related identity enhancement,

274
Work-Related Identity Functions
(e.g., Self-Esteem, Belonging,
Self-Knowledge, Self-Verification,
Self-Continuity, Self-Enhancement,
Uncertainty Reduction, Perceived
Meaningfulness)
Individual
Well-Being
AI-Related
Behavior and
Attitudes
Work-Related
Behavior and
Attitudes
Behavior and
Attitudes Toward
Society
AI That Complements Tasks
- Are New Skills Required? Do Old
Skills or Behaviors Need to Be
Unlearned?
- Do New Interactions Follow? Is the
Job Role Affected?
AI That Replaces Tasks
- Which Types of Tasks Are Being
Replaced (Arduous vs. Highly
Skilled)?
- Which Tasks Remain? How Is the
Job Role Affected?
- How Is the Social Work Context
Affected?
AI That Generates New Tasks
- Do New Tasks Require New Skills?
- Are There Other People Who Must
Carry Out the New Tasks?
- Do New Tasks Create New Roles?
What Are the Functional
Changes of AI?
How Will the Functional Changes Affect Work-Related Identity
Enactment and Functions and the Social Fabric of Work?
Social Validation
Identity Threat
Identity Expansion
Social Fabric of Work
Work-Related Identity Enactment
Social Validation From the Social Context
Social Validation
Social Validation From the Social Context
Process of AI Implementation
(e.g., Pace of Change, Number and Types of Tasks Affected,
Availability of Liminal Spaces, Uncanniness of Change)
The Broader Context of AI and Work
(e.g., Popular Narratives,
Organizational Sensemaking,
Occupational Communities)
Fig. 1. A framework of functional task changes related to implementation of artificial intelligence (AI), the effects of these changes on work-related identity, and
individual, work-related, and societal outcomes. Functional task changes caused by AI can affect identity enactment, work-related identity functions, and/or the social
fabric of work in various ways, which can lead to identity threat or enable identity expansion, depending on processes of implementation but also the broader context
of work. These identity-change processes are embedded in a changing social context, which acts as a source of social validation and can support identity change.

Current Directions in Psychological Science 31(3) 275
with subsequent effects on well-being, behavior, and
attitudes. To understand which responses are the most
likely ones, it is important to ask the following ques-
tions: (a) What are the functional work changes antici-
pated, and what challenges will arise from AI use in a
specific work context? and (b) How will these changes
and challenges affect the enactment of important work-
related identities (e.g., occupational, role, and organi-
zational identities), their functions (e.g., belonging,
self-esteem, and self-enhancement), and the social fab-
ric of work (e.g., team composition and organizational
status hierarchies)? Furthermore, responses also depend
on certain conditions. That is, the change process asso-
ciated with AI implementation will shape employees’
responses. Relevant factors include the number and
type of tasks affected, the pace of change, and the
social context of the implementation, both within and
outside work.
Work-related identities reflect “who you are” and
“what you do” regarding work. They are informed by
the social groups people feel part of and by enactment
of certain behaviors that are prototypical for those
groups, and they offer important social recognition for
those behaviors (Ashforth & Schinoff, 2016; Nelson &
Irwin, 2014). Work offers plenty of opportunities for
social self-categorization in that people can see them-
selves as part of an occupation, an organization, or a
work team. People act according to social-group norms
in their work and thereby gain social recognition. Fur-
thermore, work-related identities fulfill multiple impor-
tant identity functions. For example, they provide a
sense of self-esteem and offer opportunities to experi-
ence meaning, a sense of belonging, and competence
(see Ashforth & Schinoff, 2016). In addition, work
contexts—especially teams, colleagues, and supervi-
sors, along with their respective organizations and
occupational communities—can offer social validation
to ingrain those work-related identities and ensure that
they fulfill their functions.
To make sense of dramatic AI-induced changes,
workers will consider what these changes mean for
their work-related identities, for their ability to meet
identity functions (e.g., self-esteem), and for their
enactment of identity-relevant behaviors through work.
For this reason, consequences of AI-induced work
changes depend on whether they generate threats or
enhancements to identities and their functions. If identi-
ties and their functions are threatened, undermined, or
lost, this not only is upsetting for the individual, whose
well-being is affected, but also will result in a variety
of identity-protection responses (Petriglieri, 2011). Con-
versely, if AI-induced change supports identity func-
tions, and brings people closer to their ideal work
selves, people can restructure, adapt, and expand their
work identity (Endacott, 2021). Theoretically, all of this
will have consequences for the individual as well as for
the individual’s attitudes and behaviors toward AI,
toward the changed workplace, and perhaps toward
society at large (Craig etal., 2019; Nelson & Irwin, 2014;
Petriglieri, 2011). Individuals may vary in their identity
responses, and additional variation can result from the
specific process by which AI is implemented, as noted
earlier.
Sensemaking is not a singular process but rather
happens in a social context that offers validation for
new behaviors and new definitions of identity that
will make identity changes stick or unstick (Ashforth
& Schinoff, 2016). AI changes to key tasks can affect
occupational boundaries and consequential team and
organizational structures, thereby changing the social
fabric of work (Craig et al., 2019). Moreover, the
broader organizational, occupational, and societal con-
text will matter for identity changes, as it provides a
system of norms and expectations as reference points
for sensemaking (Endacott, 2021). In the following sec-
tions, we leverage this framework to examine potential
identity consequences associated with three main
workplace functions of AI.
AI that complements and supports
existing human tasks
AI offers new tools to complement and support existing
work, such as through real-time monitoring or interven-
tion in work environments (e.g., analyzing smart phone
data to identify workplace hazards; Howard, 2019) or
providing and structuring informational inputs (e.g.,
improving scheduling; Endacott, 2021). Workers using
AI may need to acquire new skills (e.g., fluency in data
analytics and evaluating data outputs) or unlearn old
routines, as the demand for certain tasks in their jobs
shifts (Lanzolla etal., 2020). Any resulting changes in
identity functions (e.g., self-esteem, belonging), in turn,
may affect work-related identities (Ashforth & Schinoff,
2016).
Task-related changes will also affect the social fabric
of work. For example, some researchers propose that
the usage of AI in psychiatry requires data-management
skills and reliance on close collaborations with software
engineers, thereby redefining organizational hierarchies
and what it means to be a (competent) medical profes-
sional (Brunn etal., 2020). Interview studies indicate
that imposed work changes in general are initially per-
ceived as identity threats, but workers can gradually
move toward acceptance if they manage to adapt to the
changes and modify their identities (Chen & Reay, 2021).
This outcome has been shown to depend on the manner
in which AI is implemented, such as whether people

276 Selenko et al.
have a voice in the implementation and whether there
is a gradual experience of change. Another relevant fac-
tor is the availability of liminal, or transitional, safe
spaces that allow for new learning and competency gain,
which can facilitate adaptation toward new work-related
self-understandings (e.g., seeing oneself as an informa-
tion specialist rather than a radiologist; Jha & Topol,
2016). Also, if AI improves people’s ability to enact cer-
tain identity motives (e.g., to become better in their jobs
and thereby gain self-enhancement), work-related identi-
ties can be extended, and “working with AI” can become
a positive identity category (Endacott, 2021).
AI that replaces human tasks
AI-enhanced processes can also replace various cogni-
tive and manual tasks previously done by humans,
including (a) arduous and repetitive tasks (e.g., pattern
recognition, stock refilling), (b) other routine tasks (e.g.,
scheduling, diagnostics, data search), and (c) more
highly skilled tasks associated with complex decision
making (e.g., AI-automated financial, legal, or policing
decisions; customer service). Such replacement brings
additional identity challenges, over and above those pre-
sented by AI that complements current tasks. When AI
replaces tasks, workers are no longer able to enact task-
related professional self-understandings. This can disrupt
a sense of self-continuity and possibly frustrate the sat-
isfaction of other related identity functions that carrying
out the replaced tasks previously served (e.g., gaining
self-esteem, certainty, meaning; Endacott, 2021). How-
ever, if the replacement of certain tasks by AI enables
workers to get closer to their aspired identities (e.g.,
because it removes an obstacle to accessing identity-
relevant functions by ameliorating a high failure rate or
social stigma), workers will find it easier to change their
identities, and the replacement will be more readily
accepted (Endacott, 2021). The replaced tasks may also
reshape the organization of remaining work. For exam-
ple, interacting with or being managed by a self-learning,
unintelligible algorithmic process that acts in a quasihu-
man way may feel uncanny (Schafheitle etal., 2020).
Moreover, if decisions are perceived as being made with-
out appropriate contextual information, or if they are
perceived as incorrect or arbitrary, they may not be
trusted (Raisch & Krakowski, 2021), which can result in
feelings of alienation or dehumanization.
If the replacement of tasks is accompanied by the
replacement of humans, this will also alter the social
fabric of work, which, in turn, will affect how remaining
workers can validate their existing work-related identi-
ties (Endacott, 2021). Workers who lose significant
aspects of their jobs, or their job roles, will face the
greatest identity challenge. How can they protect their
self-esteem and achieve a sense of self-continuity and
self-verification if the social self-categorizations
enabling those functions no longer exist?
AI that generates new human work tasks
Despite its opportunities for human replacement, the
implementation of AI can also create new tasks and job
roles. Various “algorithmic occupations” are emerging,
focused on training AI (e.g., getting tasks ready for
automatization, teaching the algorithm), explaining the
changes to workers (e.g., convincing them to use algo-
rithmic outputs), and sustaining the use of AI (e.g.,
considering its ongoing ethical implications; Wilson
etal., 2017). More small-scale changes due to AI will
also create new tasks for workers, which might demand
new skills. These new tasks are likely to be met with
a variety of reactions. For example, people have been
found to mourn the loss of changed work, try to con-
serve existing professional identities, and avoid new
tasks (Chen & Reay, 2021). However, if liminal spaces
are created for people to engage in learning and in
identity restructuring, then identity expansion and
adjustment to the changes is more likely to happen.
Identity conditionality
Whether functional changes lead to identity threat or
enhancement will depend not only on how they affect
workers’ self-understanding and their ability to enact
work-related identities and to enjoy their identity func-
tions, but also on (a) how AI-related task changes are
implemented (e.g., the pace or pervasiveness of change)
and (b) the broader social-validation context. The social
groups people feel part of provide a system of norms
and values that guide how they make sense of AI inter-
ventions at work and how they behave in regard to AI
implementation. For example, workers who feel that a
new AI tool runs against professional norms may report
frustration and show resistance (Chen & Reay, 2021;
Strich etal., 2021). This sensemaking will happen in a
changed work context, as the functional task changes
might recompose teams and organizational hierarchies
by creating new roles and replacing old ones. The
functional change may also shift the norms of what
constitutes esteemed, desirable, and knowledgeable
behavior in the eyes of other people. This change will
foster identities that have been expanded and changed
and threaten identities that are no longer adaptive. Also,
the wider popular narrative surrounding AI technolo-
gies will be of influence. Currently, popular opinions
on AI tend to fall into two camps: those that foretell
doom (i.e., opinions that are overly skeptical and dis-
trusting toward AI) versus those that foretell utopia (i.e.,

Current Directions in Psychological Science 31(3) 277
opinions that are overly excited and overly trusting
toward AI; Raisch & Krakowski, 2021). Both positions
can be problematic (Craig etal., 2019), and whether
workers are more likely to experience identity threat or
identity expansion will depend on which position more
closely resonates with them. Thus, organizational
attempts at sensemaking can be helpful in solidifying
the expansion of new identities (Ashforth & Schinoff,
2016). Also, occupational communities for new or
changed occupations can assist with collective sense-
making and redefining professional roles that will enable
gradual identity development (Chen & Reay, 2021).
A Way Forward: Recommendations
for Future Research and Practice
Our framework shows the importance of identity for
understanding workers’ reactions toward AI implemen-
tation and the outcomes of such implementation. If
AI-related changes modify or remove work that reflects
valued components of people’s identities or reduces
the opportunity to enact these identities, AI implemen-
tation creates a greater risk of identity threat (Craig
etal., 2019; Petriglieri, 2011). Conversely, if AI-related
changes bring people closer to their ideal work selves
or enable better job-related coping and positive self-
definitions, then positive work-related identity change
is more likely (Endacott, 2021).
Although we have identified several factors that influ-
ence workers’ reactions to AI implementation and the
outcomes of such implementation, more research is
needed to specify when, where, and by whom AI-
related changes are assessed as irrelevant, supportive,
or threatening for work-related identities and their func-
tions. For example, workers who are entrenched in stan-
dard procedures are likely to experience threat after AI
implementation (Nelson & Irwin, 2014), whereas those
with a more playful frame of reference (e.g., high levels
of openness to experience) are more likely to experi-
ence positive identity growth (Schneider & Sting, 2020).
Research confirms that senior experts tend to experience
greater identity threat from task replacement by AI than
beginners do (Strich etal., 2021). More research is also
needed to examine how workers dynamically respond
to AI-induced demands to adjust and recraft their identi-
ties, by redefining what they do and who they are in
relation to it (Strich etal., 2021), as well as to investigate
the consequences of AI implementation for workers’
well-being, attitudes and behavior toward AI, and work-
related outcomes (e.g., performance, commitment,
engagement; Craig etal., 2019). Future research may
also extend our framework to the team level to allow
an examination of disruption and the process of recov-
ery among teams during AI implementation.
Our proposed framework also offers several practical
recommendations for organizations. Best practices in AI
implementation often focus on identifying salient stake-
holders, such as workers, and their expectations and
needs (Wright & Schultz, 2018). If workers’ needs are
to be taken seriously, and if identity threat is particularly
likely to occur in situations of distrust, of “black-
box-ism” (in which algorithmic decisions appear to be
unintelligible), and of replacement, leaders should adopt
approaches to AI implementation that identify, mitigate,
and compensate for these issues. For example, research
shows that employers can assist workers in forming new
identities conducive to acceptance and mastery of AI by
providing narratives that focus on sensemaking and
identity development (e.g., “we are on the advanced side
of technology”), and help reduce workers’ fears or aver-
sion to AI (Tong etal., 2021). Employers must also
appropriately retool, retrain, and reskill workers (Brunn
etal., 2020), so that they can interact with AI in ways
that get them closer to their ideal work selves (Endacott,
2021). Offering social validation and a safe liminal space
to restructure and enact new identities can also help
sustain these efforts (Chen & Reay, 2021). Organizational
leaders also need to be mindful of social relationships
at work and beyond, as these will shape how people
see themselves and evaluate how AI may remove or
reconfigure social connections (Endacott, 2021).
As for the pace of AI implementation, identity
research suggests that workers would benefit from
paced replacement that is limited to particular tasks
(ideally those not relevant to identity), rather than
radical changes that affect aspects central to the job
(Ashforth & Schinoff, 2016). Replacement will be faster
when new technology can simply be “plugged in” and
slower when new technology would demand a redesign
of the work environment (Brynjolfsson & Mitchell,
2017). More research is needed to systematically com-
pare workers’ outcomes across different kinds of AI
implementation.
In conclusion, AI-related changes to work affect
workers’ understanding of work, of themselves in rela-
tion to work, and of their social environment. As the
use and capabilities of AI expand, workers, organiza-
tions, and broader society must manage these changes
to enable workers to grow and develop toward satisfy-
ing and meaningful work selves.
Recommended Reading
Ashforth, B. E., & Schinoff, B. S. (2016). (See References).
A useful starting point on work and identity that offers
insights into identity processes in organizations, particu-
larly in times of change and sensemaking.
Jha, S., & Topol, E. J. (2016). (See References). A discussion of
how two clinicians must focus on their core and imitable

278 Selenko et al.
skills and shape their understandings of self in relation to
work when adapting to the use of artificial intelligence
(AI) that replaces and complements human tasks.
Nelson, A. J., & Irwin, J. (2014). (See References). A context-
specific account of the dynamics of identity change
among librarians as a form of technology (not AI in this
case) serves to replace, complement, and generate new
work within their occupation.
Strich, F., Mayer, A. S., & Fiedler, M. (2021). (See References).
An in-depth empirical examination of how workers expe-
rience the implementation of an AI system and the mecha-
nisms through which they both protect and strengthen
their professional identities.
Walsh, T., Levy, N., Bell, G., Elliott, A., Maclaurin, J., Mareels,
I., & Wood, F. (2019). (See References). A thoughtful,
extensive, and accessible overview of the nature of AI
technologies, the sectors to which they are being applied,
and their wider implications for individuals, society, and
work.
Transparency
Action Editor: Robert L. Goldstone
Editor: Robert L. Goldstone
Declaration of Conflicting Interests
The author(s) declared that there were no conflicts of
interest with respect to the authorship or the publication
of this article.
ORCID iDs
Eva Selenko https://orcid.org/0000-0002-9579-9200
Joel Warburton https://orcid.org/0000-0002-5638-1514
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