ArticlePDF Available

Abstract and Figures

Purpose–This conceptual article’s primary aim is to identify the significant stakeholders of the digital identity system (DIS) and then highlight the impact of artificial intelligence (AI) on each of the identified stakeholders. It also recommends vital points that could be considered by policymakers while developing technology-related policies for effective DIS. Design/methodology/approach–This article uses stakeholder methodology and design theory (DT) as a primary theoretical lens along with the innovation diffusion theory (IDT) as a sub-theory. This article is based on the analysis of existing literature that mainly comprises academic literature, official reports, whitepapers and publicly available domain experts’ interviews. Findings–The study identified six significant stakeholders, i.e. government, citizens, infrastructure providers, identity providers (IdP), judiciary and relying parties (RPs) of the DIS from the secondary data.Also, the role of IdP becomes insignificant in the context of AI-enabled digital identity systems (AIeDIS). The findings depict that AIeDIS can positively impact the DIS stakeholders by solving a range of problems such as identity theft, unauthorised access and credential misuse, and will also open a possibility of new ways to empower all the stakeholders. Research limitations/implications–The study is based on secondary data and has considered DIS stakeholders from a generic perspective. Incorporating expert opinion and empirical validation of the hypothesis could derive more specific and context-aware insights. Practical implications–The study could facilitate stakeholders to enrich further their understanding and significance of developing sustainable and future-ready DIS by highlighting the impact of AI on the digital identity ecosystem. Originality/value–To the best of the authors’ knowledge, this article is the first of its kind that has used stakeholder theory, DT and IDT to explain the design and developmental phenomenon of AIeDIS. A list of six significant stakeholders of DIS, i.e. government, citizens, infrastructure providers, IdP, judiciary and RP, is identified through comprehensive literature analysis.
Content may be subject to copyright.
AI-enabled digital identity inputs
for stakeholders and policymakers
Umar Mir,Arpan Kumar Kumar Kar and Manmohan Prasad Gupta
Indian Institute of Technology Delhi, New Delhi, India
Abstract
Purpose This conceptual articles primary aim is to identify the signicant stakeholders of the digital
identity system (DIS) and then highlight the impact of articial intelligence (AI) on each of the identied
stakeholders. It also recommends vital points that could be considered by policymakers while developing
technology-related policies for effective DIS.
Design/methodology/approach This article uses stakeholder methodology and design theory (DT) as
a primary theoretical lens along with the innovation diffusion theory (IDT) as a sub-theory. This article is
based on the analysis of existing literature that mainly comprises academic literature, ofcial reports, white
papers and publicly available domain expertsinterviews.
Findings The study identied six signicant stakeholders, i.e. government, citizens, infrastructure
providers, identity providers (IdP), judiciary and relying parties (RPs) of the DIS from the secondary data.
Also, the role of IdP becomes insignicant in the context of AI-enabled digital identity systems (AIeDIS). The
ndings depict that AIeDIS can positively impact the DIS stakeholders by solving a range of problems such
as identity theft, unauthorised access and credential misuse, and will also open a possibility of new ways to
empower all the stakeholders.
Research limitations/implications The study is based on secondary data and has considered DIS
stakeholders from a generic perspective. Incorporating expert opinion and empirical validation of the
hypothesis could derive more specic and context-aware insights.
Practical implications The study could facilitate stakeholders to enrich further their understanding
and signicance of developing sustainable and future-ready DIS by highlighting the impact of AI on the
digital identity ecosystem.
Originality/value To the best of the authorsknowledge, this article is the rst of its kind that has used
stakeholder theory, DT and IDT to explain the design and developmental phenomenon of AIeDIS. A list of six
signicant stakeholders of DIS, i.e. government, citizens, infrastructure providers, IdP, judiciary and RP, is
identied through comprehensive literature analysis.
Keywords Stakeholder theory, Biometrics, Articial intelligence, Design theory,
Innovation diffusion theory, Digital identity
Paper type Conceptual paper
1. Introduction
Identity (ID), in general, is a very complex concept that has multiple dimensions to it. The
research community have explored the concept of identityfrom various disciplines such
as psychology, information technology, philosophy, sociology, anthropology, politics and
psychoanalysis (Camp, 2004;Ellemers et al., 2002;Jung and Mittal, 2020). The study of
identityat the core is about the answers to basic questions such as who are you?or who
am I?It is composed of elements that distinguish an individual uniquely from the rest of the
population (Olson, 2015). Digital identity (DI), sometimes also referred to as electronic
Funding: This research did not receive any specic grant from funding agencies in the public,
commercial, or not-for-prot sectors.
AI-enabled
digital identity
Received 12 September2020
Revised 15 December2020
17 March 2021
29 May 2021
Accepted 31 May 2021
Journal of Science and Technology
Policy Management
© Emerald Publishing Limited
2053-4620
DOI 10.1108/JSTPM-09-2020-0134
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2053-4620.htm
identity (eID), has revolutionised the public service delivery mechanism, e.g. public
distribution system (PDS) in India. DI provides a niche opportunity, especially to developing
countries, in accelerating the overall developmental programs(Mir et al., 2020b). DI has been
dened as the collection of individual attributes that describe an entity and determine the
transactions in which that entity can participate(Mcwaters, 2016). In another study, DI is
dened as a set of claims made by one digital subject (e.g. a user) about itself or about
another digital subject(Cameron, 2005). In simple terms, DI is the online repository of
information about a particular individual that is accumulated from multiple data sources. DI, in
general, impacts various socio-economic aspects of an individual, as shown in Figure 1.
Rapidgrowthintheeld of Information and Communication Technology (ICT) has
widened the scope for the development of people (Albiman and Sulong, 2016;Chatterjee and
Kar, 2018). In the recent past, ICT researchers have explored the impact of disruptive
technologies such as articial intelligence (AI) on the growth of nations in general and on a
citizen in particular (Agrawal et al., 2018;Helbing et al., 2019;Wirtz et al., 2019). Expectations
from emerging technologies are very high considering its potential benets in the public and
private sectors and society in general in terms of better service quality, reducing lead time and
better decision-making capabilities (Toll et al., 2020). Disruptive technologies such as
blockchain, cloud computing and AI have drastically transformed the nature of transactions
from ofine space to online space, and hence opening a new dimension wherein completing a
transaction invisibly is almost impossible (Rifkin, 2001). In the past decade, AI has evolved
rapidly, which has resulted in its application across sectors (Mir et al., 2020c). The top four
sectors, i.e. retail, logistics, travel and health care, are leading in AI application with an impact of
around $400$600bn approximately in each sector (McKinsey, 2017a). Apart from assisting
humans in medical treatment (Amato et al., 2013), manufacturing self-driving vehicles and safety
systems (Tajitsu, 2016), developing context-aware virtual assistants (Waters, 2015)and
enhancing entertainment industry (Bates, 1992), AI is also being used for identifying fraud and
help take proactive measure to mitigate possible attacks (Ryman-Tubb et al.,2018).
Providing identity that could be used for an individuals well-being is a highly complex
process (Mir et al., 2020a). This has been noticed by various bodies that operate at global
levels such as the United Nation (UN) and the World Bank. In its recently released
sustainable development goals (SDGs), UN has set a milestone of providing a legal identity
to all individuals by 2030 (SDG-16). Also, the World Bank has initiated the Identity for
Development (ID4D) project that aims to provide identity to individuals and deliver DI-
enabled services to every individual. Even after many conscious efforts made by multiple
Figure 1.
Impact of DI on socio-
economic aspects
Governance Financial Inclusion
Gender Equality
Access to Healthcare
Access to Educaon
Access to PDS
Digital
Identy
Source: Goode (2010); Kar 2020; McKinsey (2019);
Mcwaters (2016)
JSTPM
organisations, only 3% of the countries possess an identity system that could be used in
both online and ofine service delivery. Majority of the identity solutions at present are
functional, which means they are developed for a specic use case such as driving licence,
PAN card and voter ID (Mir et al., 2020a). Governments have already used AI systems as a
solution to bring efciency, and enable efcient utilisation of resources in the public sector
(Toll et al., 2020). AI could be used in public policy at various stages such as agenda-setting,
policy formulation, decision-making, policy implementation and evaluation. Each stage has
its own set of challenges that needs to be addressed beforehand such that full potential of the
technology could be leveraged (Dunn, 2017).
DI is the cornerstone of the digital economy and is believed to signicantly impact digital
development (Al-Khouri, 2014;BankWorld, 2016;OCED, 2011). This has led various
governments to opt for ICT-based solutions to solve the invisible populationproblem (i.e.
people who do not possess any form of legal identity), improve public service delivery and
leverage the benets of the digital economy. Demands for such identity systems are increasing
when there is a technological revolution happening on the supply side (Atick, 2016). Disruptive
technologies such as AI, IoT and big data have signicantly impacted almost all sectors. These
innovations could be game changers if used properly. Technologies that are used in digital
identity systems (DISs) are evolving at a rapid pace. This requires some serious efforts to
anticipate the evolution of DIS infrastructure in the next few decades. Any miss-out in this
direction could result in an ineffective DIS that will incur substantial socio-economic
losses (Beduschi et al., 2017). There is a dire need for focused research towards
developing the appropriate DI system that is future-proof, secure and trustworthy and
could be used for identicationandservicedeliverybybothpublicandprivateentities
(Bhandari et al., 2020). This study attempts to highlight the signicance of a disruptive
technology such as AI in the identication phenomena. Specically, in this conceptual
article, which is based on the secondary data, we try to analyse how AI could be
leveraged for identication and try to answer the following research questions:
RQ1. Who will be the stakeholders of AI-enabled digital identity systems?
RQ2. How can AI enhance capabilities of these stakeholders?
The subsequent sections of this article are organised as follows. Section 2 covers the
literature review. Section 3 focuses on the methodology followed in this research, and
theoretical linkages are explained in Section 4. Findings are presented in Section 5, followed
by discussion, contribution and limitations and future research directions in Section 6.
Finally, Section 7 covers the conclusion part.
2. Literature review
AI is not unusual to the government. While its utility in domains such as defence,
surveillance and intelligence have been accepted, it also has been used to lessen heavy
tasks. The recent advancements in technology, especially networking, data processing and
data storage capabilities, have opened gates for many opportunities and have broadened
the utility dimension of the technology (Dwivedi et al., 2019;Kar, 2016). This has led
various countries to be part of the digital revolution by taking the rst step in this direction
and formulating a national AI strategy, as shown in Table 1. The readiness index
highlights how prepared government is for the inclusion of AI in governance processes
(Miller and Stirling, 2019).
AI-enabled
digital identity
AI is considered a specialised science category that facilitates machines to identify a suitable
solution for solving complex problems as normal human beings do. Various researchers
from different lenses have dened AI. For example, Rai (2018) dened AI as:
[...] the ability of a machine to perform cognitive functions that we associate with human minds,
such as perceiving, reasoning, learning, interacting with the environment, problem solving,
decision-making, and even demonstrating creativity.
Some dened it as a technology that behaves intelligently, imitates humans or possesses
cognitive capabilities like humans (Flasi
nski, 2016;Robles Carrillo, 2020;Warwick, 2012).
Irrespective of which denition one uses, there is some level of agreeability among the AI
research community that AI is a mix of multiple domains that include computer science,
philosophy, linguistics, mathematics and psychology.
AI has found application in various domains such as public sector (Pan, 2016;Simon,
2019), health care (Winter and Davidson, 2019), transportation (W.Sadek, 2007), hospitality
(Buhalis et al.,2019;Li et al., 2019), education (Passonneau et al., 2017), justice (Rissland et al.,
2003), entertainment (Bowman and Banks, 2019) and retailing (Weber and Schütte, 2019).
Some researchers have focused on the specic utility of AI in a specic area (Mir et al.,
2020c). One of the most signicant contributions of AI in government is developing
intelligent agents, also called Bots. Bots have drastically improved the communication
Table 1.
National AI strategy
landscape
Year Country
Govt.
readiness
rank AI initiative
Mar-2017 Canada 6 Pan-Canadian articial intelligence strategy
Mar-2017 Japan 10 Articial intelligence technology strategy and also included
AI in its integrated innovation strategy
May-2017 Singapore 1 National AI strategy and AI Singapore
July-2017 China 20 A next-generation articial intelligence development plan
Oct-2017 UAE 19 National strategy for AI
Dec-2017 Finland 5 Finlands age of articial intelligence
Jan-2018 Kenya 52 Blockchain and articial intelligence task force
Jan-2018 Taiwan 41 Taiwan AI action plan
Jan-2018 Denmark 9 Strategy for Denmarks digital growth
Jan-2018 Estonia 23 Estonias AI task force
Mar-2018 Italy 15 AI task force
Mar-2018 France 8 National strategy for AI called AI for humanity, which is
outlined in the Villani report
Apr-2018 Tunisia 54 AI task force and steering committee
Apr-2018 UK 2 Sector deal for AI to advance the UKs ambitions in AI
May-2018 Australia 11 AI policy
May-2018 USA 4 American AI initiative
May-2018 South Korea 26 Articial intelligence information industry Development strategy
May-2018 Sweden 6 National approach for articial intelligence
June-2018 India 17 National strategy for articial intelligence #AIforAll
June-2018 Mexico 32 Towards an AI strategy in Mexico: harnessing the AI revolution
Aug-2018 Saudi Arabia 78 Saudi data and articial intelligence authority
Nov-2018 Germany 3 Articial intelligence strategy
Apr-2019 Lithuania 37 Lithuanian articial intelligence strategy
Oct-2019 The Netherlands 14 Strategic action plan for articial intelligence
Oct-2019 Russia 29 National AI strategy
Jan-2020 Norway 12 National strategy for articial intelligence
JSTPM
phenomena between citizens and governments (Androutsopoulou et al., 2019). Studies such
as Pantano and Pizzi (2020) analysed AIs application, primarily covering the development
of specialised chatbots for assisting customers in online retailing. From the governance
perspective, AI is believed to have a multi-fold impact on people, governments and public
service delivery. Researchers have explored the utility of AIin different contexts, e.g. Ayoub
and Payne (2016) discuss the impact of AI-assisted strategic decision-making, Brynjolfsson
and Mitchell (2017) highlight the implication of AI on the workforce, considering both
pros and cons of the technology, Sharma et al. (2020) studied the application of AI in public
sector and Ku and Leroy (2014) highlight the role AI in crime detection. Apart from all the
promising benets of AI, it also brings some peculiar challenges. For example, Sun and
Medaglia (2019) analyse the challenges faced by various stakeholders in the adoption of AI
in health care.
The increasing demands primarily drive the digitalisation of public services and other
types of transactions, including nancial in terms of cost reduction, efcient service delivery
and reduction in fraudulent transactions. This needs an intelligent identity system that not
only identies an individual uniquely but also addresses the requirement as mentioned
above for a better governance experience AI-enabled digital identity systems (AIeDIS). It
is proven in various studies that AI-enabled systems are good at detecting frauds (Bolton
and Hand, 2002), improve user experience through Bots (Duijst, 2017) and identify an
individual based on their biological traits such as biometrics (Liang et al., 2020). This leads
us to believe that AIeDIS can address the needs of both an individual and government and
improve the overall experience of both entities. Stakeholders of AIeDIS, which is a mix of
technology and non-technology entities, are the amalgam of stakeholders of AI and
e-government systems. Some of the primary stakeholders of AI and e-government are
citizen, government, technology providers, NGOs, judiciary and service providers (Al-
Khouri, 2014;Ashaye and Irani, 2019;Mir et al., 2020b,2020c;Rowley, 2011).
AI plays an essential role in the identication systems and has better accuracy over
traditional identication processes. With the help of AI, facial recognition can detect
between actual human and spoofed ones with higher accuracy during the KYC process.
With the continuous improvements in the AI algorithms, AI-enabled systems can detect a
forgery in identity documents with better precision than the manual screening process. Such
systems not only bring higher accuracy in the verication process but also improve identity
verication by multi-fold. There are multiple ways by which AI can improve DIS in terms of
speed, accuracy, efciency and utility. AI plays a pivotal role in determining real identity,
detects fake documents, faster identity verication and is very hard to circumvent.
Researchers (Phiri et al., 2011a) proposed an AI-enabled multifactor authentication approach
(MFA) for a secure authentication system, wherein biometric, pseudo-metric and device
metric factors are used for authentication. Phiri et al. (2011b) used the information fusion
technique for MFA. They mined multiple data sources such as nancial institutions,
education systems, health-care systems, social networks and questionnaires to compile
identity attributes. In another study (Cole, 1991), the author introduces the concept of a
virtual person based on virtual machines and concludes that no digital machine could
understand language and how articial minds are possible. From the identication
perspective, we did not nd any signicant work that has studied the impact of AI on the
DIS ecosystem. Some of the notable studies that have used AI for different identication
forms are shown in Table 2.
In general, AI for authentication services is growing, but it is still in its inception phase. It
is expected that researchers will explore multiple dimensions of AI specic to identication
services in the near future, considering the development and demand in AI and
AI-enabled
digital identity
identication, respectively. In this study, we try to address this gap by focusing on the
application of AI for the digital identication and its implications on the DIS stakeholders.
3. Methodology
The study used the stakeholder methodology to identify the primary stakeholders that
AIeDIS could impact. ST is different from others. It is very effective in addressing the needs
and interests of key entities during the planning process, which impacts the systems overall
performance. It is a procedure to systematically gather and analyse information to identify
actors or stakeholders that signicantly impact the overall system. ST is very effective in
this study because it enables policymakers and practitioners to identify the key entities and
assess their position and signicance within the system and develop relevant policies and
implementation plans accordingly. Having a prior understanding of key entities and their
impact on the overall system increases the programs chances of being successful.
The systems approach is another alternative that could have been used in this study. In
the systems approach, the focus of analysis is mainly on the holistic aspects of the system. It
analyses the interaction and inter-relationship mechanism among the parts of the system
and their impact on each other to understand the functioning of the overall system. In this
study, ST is chosen over systems approach because the intent is to identify the core entities
of the system that could be impacted by AIs inclusion at an individual level rather than
analysing the entitiesintra-dependence relation. Another important reason is that the
outcome of ST enables us to focus on each stakeholder explicitly from the design
perspective, thereby satisfying design theory (DT)s initial requirements.
All these stakeholders were identied from an in-depth analysis of various secondary
data sources that included research articles, white papers from various government and
non-government entities, news articles and publicly available interviews of experts. To
explore the relevant literature for this study, we used a rigorous methodology opted by
Abedin (2013) for identifying resources. We queried Scopus for relevant literature, which is
one of the largest abstracts repositories. Although most of the literature was taken from
Scopus, we also used Google Scholar as our second-line data source. Articles were identied
by using three key search keywords, i.e. Articial Intelligence,Digital Identityand
Electronic Identity. In the initial stage, 142 articles were identied after dropping
duplicates and entries with missing original texts. In the next stage, we read the abstract
and conclusion section of each paper and ltered out 86 articles that were deemed irrelevant
Table 2.
Application of AI for
identication
Author(s) Application of AI
Strich et al. (2021) Impact of substitutive decision-making AI systems on employeesprofessional
identity
Phiri et al. (2011a) Proposed AI-based multifactor authentication mechanism
Cole (1991) Highlighted possibility of articial minds using emerging technologies
Verma et al. (2019) Proposed a mechanism to predicted national identity of students
Lalmuanawma et al.
(2020)
Scope of AI for Covid-19 patient tracking
Horowitz et al. (2018) Identication of outliers in national security context
Agbinya (2019) Application of AI in identity management systems
Zanzotto (2019) Application of human-assisted AI systems for identifying people
Power (2016) Identication of criminals in public places
de Vries (2010) Impact on ones identity by machine-enabled proling
Phiri et al. (2011b) Mined multiple data sources using AI for compiling identity
JSTPM
for this study. Further, 37 more articles were dropped that were identied as loosely relevant
for this study after reading the complete paper. Hence, in total, 123 articles were dropped
based on the exclusion criteria such as not relevant in the context of identication, missing
full-text and published in low-quality journals. Finally, a total of 31 articles, which include
19 academic papers and 12 additional documents in the form of reports and white papers,
were considered for further analysis. The steps followed during literature selection are also
shown in Figure 2. Each article was thoroughly analysed by the authors separately. During
consensus building, each authorsndings were collated, and a total of ten stakeholders
were identied in the initial phase from the literature analysis. In the subsequent phase,
some of the closely related stakeholders were clubbed together and were replaced by a single
stakeholder category, e.g. platform and technology is replaced by infrastructure; biometric
data collectors and on-boarding of identity data are replaced by identity providers (IdPs);
and non-government organisations (NGOs), regulatory body and government were clubbed
into government. Finally, six signicant stakeholders were identied (see Table 3) and are
used for further analysis.
ST is a well-studied and extensively applied methodology in different domains of
research that focus on the category which are essential for the organisation (Mishra and
Mishra, 2013;Singh et al.,2017). Its importance, especially in the e-governance domain, has
Figure 2.
Steps followed in
identifying relevant
literature
Table 3.
Stakeholders of DIS
Stakeholder Reference
Government Dixon (2017),McKinsey (2019);Mcwaters (2016);Mir et al. (2020b);Moavenzadeh and de
Maar (2018),Open Identity Exchange (2018)
Citizens Clark et al. (2016),Mcwaters (2016);Mir et al. (2020b);Moavenzadeh and de Maar (2018),
Ronald et al. (2017);Schoemaker et al. (2020)
Infrastructure
providers
Goodell and Aste (2019),ITU-T FG-DFS (2017);Laurent and Bouzefrane (2015);Mir et al.
(2020b);Schoemaker et al. (2020)
ID providers Clark et al. (2016),Knight and Saxby (2014);Mcwaters (2016);Mir et al. (2020b);Sharma
(2016)
Judiciary Bhandari et al. (2020),Dixon (2017);Open Identity Exchange (2018)
Relying parties Bhandari et al. (2020),Clark et al. (2016);Goodell and Aste (2019),Mcwaters (2016)
AI-enabled
digital identity
been extensively studied in the literature (Janssen and Estevez, 2013;Van De Kaa et al.,
2018). Studies have observed that stakeholders are key factors in the success or failure of
implementing new technologies (Rahman and Ko, 2013). Ema et al. (2016) have used a
stakeholder approach to study the opinions of stakeholder concerning AI and human
machine interactions. ST is relevant in the context of this study as the aim of this study is to
analyse the impact of AI technology on DI systems andwhich entities are getting affected in
the existing identity system platform.
4. Theoretical lens
This study uses DT as the primary theoretical lens. DT adopted in this study is based on IS
design theory (Walls et al., 1992) and DI design theory (Mir et al., 2020b). DT requires initial
constructs to be derived using kernel theory. Hence, in this study, we have used two well-
established theories, i.e. ST and innovation diffusion theory (IDT), as fundamental kernel
theories. Each theory holds signicant importance in the context of this study. The purpose
of using ST is to identify the stakeholders of AIeDIS and IDT to identify the critical
dimensions that play a vital role in adopting innovative technologies. The application and
organisation of theories used are explained below.
4.1 Design theory
Literature is indicative of the acceptance and implementation of DT in various scenarios
(Hatchuel et al.,2016). Different thoughts of school have dened DT differently, e.g. Walls
(1992) highlights the prescriptive nature of theory, Goldkuhl (2004) focuses on practical
aspects of it and Gregor and Jones (2007) stress on the DT as a basis for action. From the IS
perspective, WallsIS DT has been widely accepted and followed by the IS community.
Walls divided DT for information systems into two major components, Design Product
and Design Processand dened DT for information systems as a prescriptive theory
based on theoretical underpinnings which says how a design process can be carried out in a
way which is both effective and feasible(Mir et al., 2020b).
DT holds two major advantages that make it more suitable for this study: 1) Its ability to
highlight the signicance of stakeholders in the system; 2) Its ability to verify both product
and processes followed. This article applies DT from the prescriptive lens and explains the
design aspects of AIeDIS (see Figure 3). Next, we brieydene six steps of DT followed in
this study.
Identication of stakeholders: ST, originally proposed by Freeman (2015), is widely
accepted and actively applied in different research domains because of its simplicity and
effectiveness in analysing the signicance of entities in a system. It focuses on the
relationships among different groups of actors both within and outside of the system. ST
helps in identifying who are the key actors within the system. ST in information system
discipline is dened as:
[...] all those who have a practical concern for the eective application of new technologies, and
who are in a position to take or to inuence decisions about why and how they are used (Mishra
and Mishra, 2013).
Some of the main advantages of ST are ensuring transparency, accountability, credibility
and enhancing quality (Motuapuaka et al., 2015). In this study, ST is used to identify
signicant stakeholders in AIeDIS. Data was collected mostly from DI literature mainly
related to IndiasDIAadhaar. Aadhaar is worlds largest biometric DI scheme based on
the number of enrolments (Mir et al., 2020b) and AI frameworks (Mir et al., 2020c). The focus
was to include all the signicant stakeholders of the DIS.
JSTPM
Meta-requirements: This component identies the list of features an AIeDIS should have,
which acts as essential requirements for applying theory. Meta-requirements (MRs) are
listed by analysing the existing DIS literature gaps and rising concerns among the
developing and less developing countries. Initially, an extensive list of requirements was
identied, which were later classied corresponding to six signicant stakeholders of the
AIeDIS, as shown in Table 3.
Meta-design: Meta-design (MD) constitutes the set of recommendations from policy and
technology perspectives corresponding to each MR. Recommendations are made keeping in
view the power and applicability of AI in DIS cycle. The design recommendations focus on
fool-proof identication, convenience, data protection and robustness of the overall system.
MD corresponding to each MR is listed in Table 5.
Innovation diffusion theory: The study uses IDT as a kernel theory, which acts as guiding
principle for DM. IDT is a seminal work in communications discipline originated from
Psychology domain. IDT is used to explain how innovations are adopted among the target
audience Although Gabriel Trade initially explored it, the nal theory came into existence in
1962 by Everett Rogers (Bhattacherjee, 2020). Application of IDT in IS and its sub-domains
have grown immensely in the past two decades (Lim et al.,2013). IDT in the context of this
study is used to enhance the probability of adoption of AIeDIS among stakeholders. Five
major innovation characteristics taken from IDT are compatibility, complexity, relative
advantage, observability and trialability. Compatibility is the extent to which innovation (AI
in the context of this study) is considered as consistent with the existing systems, user
experience and needs of the stakeholders. Complexity is the degree of the perceived
difculty in terms of developing AIeDIS, and ease of use for stakeholders. Relative
advantage is the realisation of potential benets that a new system based on innovation
brings over the existing system. This is considered to have a signicant impact on the
adoption level of an innovation-based idea. Observability is the extent to which the target
population observes the outcome of innovation. Trialability implies the possibility of testing
an innovation before the nal system. It is primarily achieved by developing a prototype of
the intended system.
Figure 3.
Components of
AIeDIS design theory
using stakeholder
and innovation
diffusion theory
adapted from Mir
et al. (2020b) and
Walls et al. (1992)
AI-enabled
digital identity
Design method: Design method provides possible scenarios highlighting how MD could
be achieved. In this conceptual article, we used DT from the prescriptive lens. We explained
how AIeDIS MR could be met by incorporating the proposed MD recommendations. This
section is the process guidelines that could be followed to achieve construction of MD
components of AIeDIS.
Meta-validation: Meta-validation is concerned with the testing of the overall system and
has two primary checks to make 1) if MD satises MR and 2) if the design method results in
the MD compatible artefacts. The MR and MD need to be tested empirically only after
developing a functional prototype. Guidelines for testing AIeDISare highlighted inTable 4.
5. Findings
This section highlights the impact of AI on the stakeholders of an identity system. Table 5
depicts the relationship between the AIeDIS requirements and the corresponding design
demands, which is further explained in this section. Shifting of functionalities from one
stakeholder to another, adding/removing functionalities and justication regarding
removing some existing stakeholder are considered an impact in the subsequent sections of
this article. Table 6 presents a snapshot of the degree of impact of AIeDIS on the
stakeholders corresponding to ve IDT characteristics that have been identied as critical
for adoption (Bhattacherjee, 2020).
5.1 Government
AIs application rst started when the Enygma machine was used for decoding encrypted
Nazis communications during World War II. Since then, digital technologies such as AI
have evolved signicantly in terms of effectiveness and efciency. At present, AI has
touched almost all the major sectors such as automobile, ntech, education, health care and
logistics. Implementation of AI in delivering eGovernment services is picking up rapidly.
Researchers have used AI as a baseline technology to develop domain-specic solutions.
Androutsopoulou et al. (2019) have developed a chatbot using AI to communicate between
citizens and government. This chatbot not only enables citizens to interact with the
government but also facilitates information seeking in a native language and conducting an
online transaction.
Policymaking is a daunting task for any government. It has an impact on the social,
economic and environmental aspects of society. AI could be implemented in the public
service sector for developing effective policies (Lauterbach, 2019). As per the recent report
from World Economic Forum, systematic adoption of AI in government faces ve major
hindrances related to the use of data, AI skills, AI ecosystem and legacy culture and
procurement mechanism (Santeli and Gerdon, 2019). There are different types of AI-
associated techniques that could help in solving some of these issues. For example, from a
Table 4.
Testing of AIeDIS
1 It feasible to develop AIeDIS
2 It is feasible to address gaps of existing DIS via AIeDIS
It is feasible to develop completely automatic identication system with present available technologies
3 It is feasible to develop consent-based DIS
4 AIeDIS should be compatible with existing DIS
5 It is possible to develop AIeDIS with minimum amount of personal data
6 It is feasible to develop laws exclusively for AI systems
7 AIeDIS prototype helps achieve MR
JSTPM
policy perspective, data mining, decision support systems, game theory and simulations
techniques could be used in policy development (Milano et al.,2014).
Identifying people has been a complicated task for the governments to tackle various
pressing issues such as illegal migrations, terrorism, citizenship and fraudstersdetection.
These issues are not new and have been there for ages. Governments in the past have taken
measures to deal with such issues by identifying people through various means such as
Table 5.
Design theory
constructs
Stakeholder Meta-requirements Meta-design Explanation
Government MR-1: AIeDIS should
support real-time
identication
MD-1: Automatic
identication at entry
and exit points
Use of biometric scanning
technologies at entry and exit points
MR-2: AIeDIS should
support citizen-centric
policy development
MD-2: Consent-based
analysis of non-personal
data
Opt-in approach prior to any
data collection and processing
activity
MR-3: AIeDIS should be
affordable for masses
MD-3: Use of commodity
technologies and
availability of
government subsidies
Apply cost reduction techniques
at both product and process
level; support for plug-n-play
development approach with
focus on reusability
Citizens MR-4: AIeDIS should
enhance better access to
public and private
services
MD-4: Uniform
identication platform
for access control
mechanism
24 7 easy-to-access platform
with yesno authentication; focus
on on-boarding of service
providers
MR-5: AIeDIS should
mitigate identity theft
MD-5: zero cognitive load Issues no physical cards, unique
numbers, username etc.; use
biometrics of a person only
MR-6 AIeDIS should
support both monetary
and non-monetary
transactions
MD-6: Common
minimum identication
standards
Exclusively yesno
authentication will shun the need
of transaction constrained
authentication
Infrastructure
providers
MR-7: AIeDIS should
support plug-n-play of
AI-friendly technologies
MD-7: Quality control
board
Standardised techniques and
technologies will enhance
seamless integration
ID providers MR-8: AIeDIS should
minimise human
intervention in
identication process
MD-8: Induction of
intelligent systems
Use of sensors for data collection
removes the need of human
intervention
Judiciary MR-9: Need for AI-
compatible laws and
regulations
MD-9: Inclusion of
experts in law-making
bodies
Domain experts consider
technology-specic issues that
could have signicant impact in
developing technology-related
policies
MR-10: AIeDIS should
support in nalising
verdict
MD-10: AI-compatible
data repositories
Better decision-making could be
facilitated by analysing all the
relevant data that must be in
machine readable form
Relying
parties
MR-11: AIeDIS should
support quick and hassle-
free identication
MD-11: Automatic
identication
Fully technology-assisted
authentication could be free from
humanly bottlenecks
MR-12: AIeDIS should
support customer-centric
services to legitimate
users only
MD-12: Consent-based
analysis of non-personal
data for service
recommendation
Exclusive consent for data
processing could facilitate better
access control and user-centric
services recommendations
AI-enabled
digital identity
domicile certicates, national IDs, unique identity credentials and undertaking by
competent authorities. However, all these measures have one or the other loopholes that
have been exploited to gain unauthorised benets by the people with ill intentions (Newman
and McNally, 2005). Earlier identity proofs were formulated primarily for verifying people in
physical space. As digital technologies evolved and disrupted almost all sections of society,
there is a need for an identity suitable for both online and ofine space as earlier identity
proofs posed serious challenges (Allison et al., 2005;Belk, 2013).
AI could play a signicant role in identifying people in the online space. The
advancements in AI subelds (machine learning, image processing, video processing and
pattern recognition) could solve a diverse set of need-based problems. For example, real-time
image and video processing could be used to identify suspected criminals, rash driving and
unauthorised trespassing by saving time and efforts. Face recognition will enable
governments to fool-proof attendance systems at entry and exit points. It will also help in
case of theft and illegal access. AIeDIS could achieve all these and much more. Some
countries such as China have already started using AI for various use cases such as home
security, border security and criminal identication; however, an identity system that is
fully AI-based is still missing in the present times (Larson, 2018). Such an identity system
will lead towards smart governance that will be less costly, removes human errors and
checks on corruption (de Sousa et al.,2019). Besides, it will also facilitate personalised
service delivery with full support for interoperability acrosssectors.
5.2 Citizens
Individuals are using AI services in their routine activities directly or indirectly, e.g. online
shopping, reading news, using social media, etc. AI-based technologies have been applied in
different use cases solving a diverse set of problems concerning the general public. Liu et al.
(2010) have highlighted how AI could be leveraged in mobile learning and identied ve
specic application of AI used in mobile learning in terms of expert systems, decision
support systems, information retrieval system, induct-learning system and intelligent
hardware. Studies such as Shank et al. (2019) analysed the peoples perception towards AI
systems wherein users perceived possession of mental capabilities in AI systems during the
interaction. Irrespective of different types of interactions with AI systems, people develop
emotional connect when perceiving mind in AI. Authors classify these emotions into seven
categories, i.e. surprise, amazement, amusement, unease, happiness, disappointment and
confusion, and analyse their impact in developing these mental perceptions.
Perks of AI-enabled solutions must be available to all. They should be easy to use so that
the majority of the population could leverage them. It has the potential to break some of the
long lingering problems of the society such as inequalities in education, health care,
nancial services and human rights. Identity has been one of the primary reasons for such
problems. According to the World Bank report, Desai et al. (2018) at present, there are more
than 1 billion people globally without any legal identity. In such circumstances, using AI for
Table 6.
Degree of impact of
AIeDIS on
stakeholders
Government Citizens Infrastructure Judiciary Relying parties
Compatibility Low Low Medium Low Low
Complexity High Low High Low Medium
Relative advantage High High Moderate Moderate High
Observability High High High Medium Medium
Trialability High Low Low Medium Low
JSTPM
providing identity to people will solve many problems, especially in developing countries
where the marginalised population is on the higher side and at high risk. Identity based on
the amalgamation of AI and biometrics is not very easy to compromise. It enables an
individual to perform transactions and avail various services condently without worrying
much about frauds. Further, such an identity system not only saves time and is hassle-free but
also saves costs that otherwise would have been incurred by an individual in case of an ofine
identity system. The best example of such an identity system is the immigration checking
centres of various European countries wherein travellers are identied through a face
recognition system installed at various entrance points. On the contrary, AI is also facing some
challenges in terms of credibility, transparency and ethics. Discussion in this direction is
beyond the scope of this article. However, the government and technology providers
responsibility is to ensure AI-based solutions are explainable, inclusive, non-discriminatory,
available and bias-free (Bhandari et al., 2020).
5.3 Infrastructure providers
In the context of this study, infrastructure is a collective name given to four interdependent
entities, i.e. experts who develop softwares and algorithms, manufacturers who
manufacture specialised hardware devices, network providers that facilitate data exchange
and regulatory body that formulates rules and regulations for the overall technical system.
In general, any information system (IS) is composed of ve essential components, i.e. raw
data, manpower, hardware, software and procedures and is treated distinctly from its
working environment (Silver et al.,1995). The simplistic methodology of the interaction of IS
with the environment is the inputprocessoutput mechanism. Paschen et al. (2020)
identied six basic building blocks, i.e. structured data, unstructured data, pre-processes,
main processes, knowledge base and information in developing an AI system, and
demarcated their corresponding roles within the AI system. All these building blocks are
closely concerned with the development and processing of software and algorithms. Roff
(2019) highlights why AI is different from automation and scope of application of AI
depends on the clarity in understanding what the components of AI are. The study stresses
upon four basic components that are vital to developing an AI-based solution for any given
scenario and, i.e. computational power of hardware, domain expert, data and algorithm.
These identied factors cover technical aspects of the AI system. As mentioned above, there
are other factors, including manufacturing, networking and regulatory bodies that drive an
overall system.
Demands for AI-based solutions to solve different socio-economic and political problems
are growing steadily, and this is because of the remarkable development in AI technologies
in the past two decades. Using AI for identifying people is not something which has notbeen
achieved yet. Many private and public sector organisations have implemented AI-based
systems for the identication purpose. Although such systems have shown descent
performance, in practice, there are additional factors that need to be taken into consideration
while developing an AI-enabled identication system for a larger population. Factors such
as parallel computing, scalability, context-aware specialised algorithms, storage capability,
data capture and data processing are some of the parameters that will drive effectiveness
and efciency of AI-based identity systems. Privacy and security have been identied as
one of the most critical factors in identity systems (Mir et al., 2019,2020b). To support that,
either existing AI techniques have to be tailored if needed, or new techniques need to be
developed that guarantee user-data protection at all levels.
AI-enabled
digital identity
5.4 ID providers
Capability to identify an entity uniquely in the fastest way possible is one of the many pros
of AI technology. Researchers in the past have used AI-based techniques to identify
different types of information from the pool of data. Becerra-Fernadez (2000) used AI for
identifying a particular individual from a knowledge repository based on the expertise of an
individual. The study focuses on the utility of AI in developing People-Finder knowledge
management systems (PFKMS). PFKMS, with the help of AI, identies the best individual
with the required skill set by querying intellectual capital managed in PFKMS knowledge
base. In another study, Singh (2018) analyses how AI could be used in tracking farmers and
identifying plant diseases through mix mobile and non-mobile devices. Studies such as
Merad et al. (2016) have proposed an AI-based multiple object-tracking solution that
identies a particular individual from the group. An individual is identied using
behavioural analysis by segmenting target into head, torso and legs and classication of
front and back poses of an individual. Purgason and Hibler (2012) have used pivotal interval
time behavioural biometric data for user identication. The study concludes that the
capability of AI to process and extract unique patterns from biometric data is vital in terms
of identication and classication of individuals. Literature indicates that signicant
numbers of identication systems based on AI techniques are primarily using facial
recognition and its variants for identication (Andrejevic and Selwyn, 2019;Beymer and
Poggio, 1995;Okumura et al., 2019,2020).
The best thing about AIeDIS is that there is no need of designated bodies that provide
identity like in traditional identity systems, e.g. nancial and educational institutions,
private and public organisations. Identity of a particular individual is the direct outcome of
an AI engine, and that could be dependent on one or more biometric traits of an individual,
e.g. face, voice, eyes,etc. or based on behavioural aspects such as body gestures and walking
style. One of the signicant impacts of AI on ID providers is that the AI system itself will
handle most of the existing tasks performed by ID provider and hence makes ID provider
replaceable. AI-based identity system could be free from various human errors that usually
arise because of fatigue. It could also save lots of resources for both individual and
governments in terms of time, cost and efforts.
5.5 Judiciary
ICT, in general, has had a signicant impact on the public administration and judicial
system. The literature has supported this wherein the application and implication of digital
technologies in the judicial system have been highlights. For example, Cusatelli and
Giacalone (2015) have studied how ICT is being used in the EU judiciary system. EU has
used ICT to analyse court orders, information exchange, video conferencing between courts,
development of websites, online processing of claims and submission of applications. It not
only has made EU judiciary more efcient but has also empowered common people who can
now see the progress of their pending cases and hence brings transparency in the system.
Overall, it facilitates judges and lawyers in better decision-making. Kiškis and Petrauskas
(2004) have analysed how classication and categorisation problem in the judiciary could be
solved using AI. The authors have chosen Lithuania as a case study, where people do not
have trust in the judiciary because of missing public connect and ambiguity in the working
of the judiciary system. Many researchers are working in understanding the impact of AI on
different sectors and how it will replace humans in the near future. As per McKinsey report,
22% of judicial jobs can be automated (McKinsey, 2017b). In this direction, Sourdin (2018)
studied what roles and responsibilities of judges will be taken over by AI-enabled robots in
coming days. AI technologies facilitate judges in decision-making process, and based on the
JSTPM
complexity of a dispute, it can support or even replace judges in less complex cases. In
another study (Aletras et al., 2016), AI has also been used to predict judicial decisions. The
authors have trained AI system based on the cases processed by the European Court of
Human Rights and proposed a binary classication task that predicts judicial decision with
up to 79% accuracy.
AI also has some peculiar problems that need to be addressed to realise the full potential
of the technology. Levmore and Fagan (2019) have shown that AI systems trained on
historical data may need not be relevant in the future instance. It depends on the coherence
of past and present data variables. It is highly likely that the variables that become relevant
in the future context were missing from the existing AI system and vice versa. Further, the
systems that are formed by combining human and AI deliver superior performance when
compared with only the AI system delivering bail and sentencing decision in the judiciary
system. The main reason that stops authoritiesto hand over full control to AI-based systems
is that AI still operates as black box. Researchers have justied why transparency in AI is
a must. The phenomena of justifying why the AI system has taken a particular decision
have been given a specic term called explainable AI(xAI). Deeks (2019) studies why xAI
is important in the judiciary and how does it impact in decision-making. AI will bring
efciency, exibility and accuracy in various judicial processes including but not limited to
increasing the trial speed, effective judicial resource allocation, interpretation of laws, check
on judicial errors and malpractices and facilitate understanding of judiciary decisions
(Sartor and Branting, 1998).
Any identity system involves collecting, processing and storing personal user data. The
same applies to the AI-enabled identity system. Existing data protection and privacy laws
need to be modied, or new laws need to be enacted such that issues specictoAI
technology such as credibility and ownership could be addressed. Another important aspect
of an AI-based identity system is to dene the legitimate use of user data in online space
when users are availing services from different service providers. To have a robust
regulatory mechanism in place, it is necessary to take regulatory bodies, data protection
authorities, standardising committee and technology providers on-board for effective
regulations.
5.6 Relying parties
Relying parties (RPs) are the entities that provide services to the people by authorising them
using an ID that is provided by a third-party IdP. Identity-related fraud cases are growing
continuously (Pascual et al.,2018) and cyber criminals continuously attack the electronic
communication channel between an IdP and the RP. The fundamental motivation behind
identity theft is to gain access to the services that one is not eligible for. Developing trust in
online communication is a challenging task in the present circumstances when intruder
attacks are becoming common. In the context of electronic communication, Balboni (2004)
analysed the signicance of having a liability mechanism in place for certication authority,
and its impact on building trust in online communication. Further certication mechanism
developed by European legislators has been analysed and key points such as promoting
signatory body as RP and removal of reasonable reliance criteria could make certication
mechanism clearer, predictable and easier to dene responsibilities. Selection of IdPs by RPs
can have substantial implications for an end-user in terms of data privacy and security.
Vapen et al. (2016) have analysed current third-party identity management environment and
identied what are the most commonly acceptable IdPs by RPs, and how different classes of
RPs vary in selecting their IdPs. Further, it is observed that existing RPs are mostly
websites from North America; however, new websites that are hosted and serving Asian
AI-enabled
digital identity
population and feature in most popular sites are likely to act as replying parties in future.
This bias towards using a few top IdPs makes users RP account vulnerable in case users
identity credentials are compromised from IdP end.
To solve such problems, many researchers have recommended multi-factor
authentication (MFA) (Bhargav-Spantzel et al.,2007). MFA does solve this problem to a
greater extent, but it also has a downside in terms of user inconvenience and time taken to
log in. In MFA, users are asked to provide additional details in terms of one-time-password,
security question, etc. during login which takes extra time to login and that in turn causes
inconvenience to the end-user (Weir et al.,2009). With AI-enabled authentication, such
problems could be avoided, thereby improving usersoverall experience and bringing
efciency in service delivery of RPs. According to (Columbus, 2019), AI can prevent
negative customers experience by enabling service providers to reduce false-positive cases
and quick approval of the transactions. AI-based identity will enable RPs a safe and secure
mechanism to deliver services to the authenticated users in a timely manner. However, a
transition from current certication-based authentication to authentication of AI-based
identities will require some technological upgradation in the current RP platform to make it
interoperable.
Impact of AI on stakeholders of DI systems and change in roles of each stakeholder are
summarised in Table 7.
6. Discussion
This section highlights the main observations that are synthesised from this conceptual
article. Specic to AIeDIS, we identied ve prominent stakeholders, as shown in Figure 4.
It can be concluded that AI can play a vital role in the effectiveness and efciency of modern
Table 7.
AI vs non-AI identity
systems
Stakeholder Non-AI-enabled identity systems AI-enabled identity systems
Government Public service delivery
Policy development
Regulating overall operations
Customised e-services delivery
Effective policy development based on user
requirements
Identication of marginalised people
User Register by providing personal details
Use unique ID for accessing services
Ensure safe possession of ID
Stress-free registration process
Convenient automatic verication
Scope for user as credentials
Infrastructure Availability of hardware and software
Interconnected devices and databases
Attribute exchange mechanism
Support for large user base and
transactions
Ensure security and privacy of user
credentials
Inclusion of different types of sensors
Inclusion of smart devices for data collection
High-speed connectivity
ID providers Attribute collection
Secure storage of veried user data
Provide unique credentials to users
Approve transactions on behalf of user
Not required
Judiciary Laws to ensure basic human rights are
not violated
Prevent miss use of ID
New laws addressing AI technology concerns
Relying party Provides required set of services to
authorised users
Target-oriented services
Improved access control by constantly
monitoring user interaction patterns
JSTPM
DIS, primarily because of its impact on the identity systems stakeholders. The reasons why
AI-based identity system is desirable is depicted in Figure 5, highlighting issues with
existing identity system and corresponding solution supported by AI-based identity system.
AI has created much hype among business across sectors, and that isbecause of its potential
to yield massive prots and gain a competitive edge in the highly competitive market space.
AI literature is proliferating, and the majority of the studies are mainly focused on the
technical, ethical and application side of it. Similarly, DI has also gained much attention
recently, and the main driving force could be UNs SDGs-16 i.e. legal identity to all by 2030.
The growth in AI technology and the urgency of achieving UNs SDG-16 pave the way for
using disruptive technology for solving societal problems. It has been observed that
disruptive technologies, leaders along with inuencers could play a signicant role in
achieving SDGs, thereby improving access to services like health care, education, banking,
etc. (Grover et al.,2018,2019). To the best of the authorsknowledge, no study was found in
the literature that has analysed the impact of AI on the stakeholders of the DI system. The
closest has been the studies highlighting the application of biometrics for identication,
authentication and authorisation from a technological perspective (Jacobsen, 2012;Nair,
2018). Besides, some of the studies have attempted to apply AI for different cases of
identication and the same are mentioned in Table 2. It has also been observed that AI-
enabled identity systems increase job performance (Alahmad and Robert, 2020). Hence, in
Figure 4.
Stakeholders of
AIeDIS
Figure 5.
Possible solutions
offered by AI-enabled
identity systems
AI-enabled
digital identity
this conceptual article, we have tried to look into the unexplored dimension of AI technology
and understand how AI could transform DI ecosystem in general and stakeholder
involvement in particular.
According to MacInnis (2011), the contribution of conceptual articles can be made
concerning constructs, relationship or theories, procedures, domains, disciplines and science
and can be classied into four major themes, i.e. envisioning, explicating, relating and
debating. Conceptualisation has been dened as a procedure of abstract thinking that
involves understanding of the mental model of an idea (MacInnis, 2011). This article
contributes to two perspectives, i.e. delineating (explicating) and advocating (debating) at
the procedure level, which includes the nature and range of problems that AIeDIS could
solve.
6.1 Implications to theory
This section highlights the contribution made by this study from a theoretical perspective.
We observed that developing AIeDIS is not only about the technology but the amalgamation
of non-technological factors also. We have tried to extend the DIS literature that primarily
focuses on the issues related to authentication processes.
This conceptual article takes a fresh perspective towards futuristic DIS, which is based
on the impact assessment of AIeDIS on stakeholders. The most important theoretical
implications come in the form of the theoretical model that captures the complex interaction
among various factors of AIeDIS. The theoretical model developed with the help of ST and
IDT may contribute towards a better understanding of the design requirements of AIeDIS.
This is the earliest attempt to use ST, IDT and DT to explain the design and developmental
phenomenon of AIeDIS. Considering the massive budget required in developing DIS, it is
necessary to evaluate each aspect of AIeDIS beforehand so that majority of the risks could
be mitigated at the earliest. For example, in the case of Aadhaar, the total developmental
cost is around 60,00070,000 crore (McKinsey, 2010;Mir et al., 2020b;Venkatanarayanan,
2018). A proper DIS could save £5bn£510bn by reducing identity thefts and improving
overall system efciency (Open Identity Exchange, 2018). This study further strengthens
the understanding regarding the critical factors that could play a vital role in deciding the
success of an AIeDIS.
6.2 Implications to practice
In this article, six main stakeholders of the DI system were identied. These stakeholders
form a complete DI ecosystem. Using AI in DI ecosystem has an impact on all the identied
stakeholders in one way or the other. For example, governments will have to amend existing
policies or devise new ones considering AI capabilities. Governments require a shift in the
policymaking process and adapt to criteria such as customised services; proactive
identication of user requirements that will serve as input to the policy development team;
and distinct governance approach towards the marginalised population. Based on these
transformations, it can be seen that AI could facilitate user empowerment and increased
user participation in government (Carr, 2007;Fischer and Nakakoji, 1992). AI-based
identities will liberate users from the cognitive load, which is the outcome of having to
remember different credentials details for different service providers. Researchers have
identied that because of cognitive load, a user tends toreplicate the same credentials across
service providers and hence makes the system vulnerable to unauthorised access (Horcher
and Tejay, 2009).
Further, AI-based identity has the potential to make identication process frictionless
with the help of real-time behavioural analysis or automatic user verication (e.g. facial
JSTPM
recognition systems in European immigration centres) that in turn could increase user
satisfaction and trust in the overall system. Infrastructure is vital in realising the AI-based
identity system and needs major revamp to make it AI-friendly. Inclusion of sensors for data
collection, uninterrupted high-speed connectivity and support for large user base are what
needs to be ensured for effective AI-based identity system. Upgrading infrastructure would
require a signicant chunk of the investment and hence will be a pivotal factor for budget-
constrained countries to implement such a system. Once theproper infrastructure is in place,
it could make IdP redundant as most of the responsibilities of IdPs in traditional identity
system could be handled by infrastructure with least human intervention. In traditional
identity systems, IdP has been the source of many types of errors, including anomalies in
attribute collection, duplicity in assigning credentials and malicious transaction approval
(Henneman et al., 2008). Having these tasks as part of infrastructure could result in a better
and efcient identity system as all these activities will be handled by machines and sensors
with almost zero human intervention and hence reduces chances of human-originated
errors. There is a continuous debate going on among lawyers, researchers, academicians,
NGOs, technologists and governments regarding AI and its ownership. Questions such as
who should be responsible for the consequences of AI-based system; and is it the
technologist who developed sensors or programmer who wrote algorithms or government
that allowed commercial use of such technology are still actively debated (Robles Carrillo,
2020). AI-based identity systems pose novel challenges for the judiciary that needs to be
addressed clearly through the rule of law. Laws that could ensure human rights are not
violated by AI-enabled identity system need to be approved before commercialising it. The
biggest challenge for judiciary is regarding the geo-location of the user database that
contains all the critical credential details about the user and its access rights (Selby, 2017).
Judiciary and AI in itself is a separate area, and many researchers are actively working in
this direction. Detailed analysis in this direction is outside the scope of this paper, but some
researchers such as Barredo Arrieta et al. (2020),Kaplan and Haenlein (2020) and Wang et al.
(2015) have addressed some of the recent challenges that AI-based technologies pose. AI-
enabled identity systems will open new revenue channels for RPs. Having a better
understanding of the user base will enable RPs to serve its customers better by
recommending tailored services based on usersneeds and preferences. Further, better
access control mechanism will avoid malicious access requests and, in turn, will lead to
service availability that could improve the quality of service and increase in user trust.
The article highlights the details of AI-based identity and its impact on the stakeholders.
It is observed that all stakeholders could be impacted and could bring efciency in the
working of stakeholders. For example, governments will have a better understanding about
its citizens and their demands in advance, which will enable them to be proactive in their
approach to avoid any uprising that could be possible in the near future (Kar, 2020;Mohan
and Kar, 2017). If done right, it could make both government and citizens empowered and
will eventually result in increased trust in government. Inclusion of intelligent devices as
part of infrastructure will increase the capability of the system and can handle tasks that
were treated by separate entities such as IdPs in this case. Most of the activities of the IdPs
will be conveniently handled as one sub-activity of infrastructure, resulting in more nuanced
access control mechanisms (Phiri and Agbinya, 2006). For judiciary, AI-based identity
system has opened an entirely new dimension of justice justice for and from autonomous
systems. In one way, AI-based systems will facilitate judiciary to make better decisions by
avoiding human bias in sentencing processes and, on the other hand, requires the judiciary
to devise new laws that addressthe challenges posed by AI-based technology.
AI-enabled
digital identity
AI brings multi-fold benets for RPs in terms of fool-proof access control, customised
service delivery, better knowledge about customers and robustness in service delivery.
Apart from contributing to the DIS literature and practice, the research also contributes to
the methodology, as it is for the rst time that three prominent theories, i.e. ST, DT and IDT,
were used to study the design aspects of DIS in general and AIeDIS in particular. The proposed
AIeDIS DT could be used as an initial framework for developing new-age innovation-based
identity systems in future. It could enable concerned authorities to have an idea about the type
of issues innovations can address and at what cost. Needless to mention that the experience
gained may be useful in analysing other large-scale innovation-driven studies in future.
Finally, based on this studysndings, integrating AI with DI systems is advocated
considering the range of benets it might yield for all the concerned stakeholders of the
system. Based on the authorsperception, a comparative analysis between AI-based identity
system, traditional and non-AI DI system is shown in Table 8, highlighting in what aspects
AI enhances existing identity systems. Hence, it is recommended that the inclusion of AI
technology for identication should be commercialised with mandatory regulations in place
such that the risks associated with current identity systems such as identity theft, duplicity,
unauthorised access, reply attacks and bias could be addressed in addition to opening a new
horizon of intelligent service-delivery mechanisms.
6.3 Limitations and future research
The study is based on secondary data and has considered DI stakeholders from a generic
perspective. It will be further beneted by incorporating primary data for empirical analysis
that focuses on the impact of AI on each stakeholders sub-processes and testing the
hypothesis identied in Table 4. Directions for future research are to validate this studys
results by incorporating expert opinion for analysis and developing a prototype of AIeDIS
for better visualisation.
7. Conclusion
In this conceptual article, we identied six signicant stakeholders of general-purpose DIS
and ve stakeholders of AIeDIS from the secondary data sources that primarily included
research articles, ofcial reports, white papers and publically available interviews of domain
experts. The ndings depict that AIeDIS will not only solve some crucial problems such as
identity theft, unauthorised access and credential misuse but will also open a possibility of
new ways to empower all stakeholders in one way or the other. The inclusion of AI in DIS
makes it easy to determine true identity, hard to circumvent and forge and promptly process
a large number of requests.
Table 8.
Comparative
analysis of different
variants of identity
systems
Factor Traditional identity Digital identity AI-enabled digital identity
Type Functional Functional/foundational Foundational
Application Service-specic Diverse Across sectors
Suitable for online No Yes Yes
Implementation Easy Moderate Complex
Security Low Medium High
Interoperability Low Medium High
Developmental cost Low High High
On-boarding cost High Low Low
Duplicates High Low Very low
Scalability Low Medium High
JSTPM
From the theoretical perspective, the study explores ST and IDTs utility in the design
and development process outlined in DT. To the best of the authorsknowledge, this is the
rst instance wherein three prominent theories, i.e. DT, ST and IDT, have been used to
highlight the complex interaction mechanism among the stakeholders of AIeDIS. Apart
from all the potential advantages of AIeDIS, there are still some issues such as explanation
and ownership of decisions that are yet to be addressed and have been the main pain points
for the authorities to call on the commercialisation of AIeDIS for large scale. This study acts
as an aid for the decision makers to decide whether to include AI as a core component in the
DIS or not primarily for the governments in the initial phase of DIS development or are
attempting to retrot it in the existing functional DIS for better utility.
References
Abedin, B., Abedin, B., Khoei, T.T. and Ghapanchi, A.R. (2013), A review of critical factors for
communicating with customers on social networking sites,The International Technology
Management Review, Vol. 3 No. 4, p. 208.
Agbinya, J.I. (2019), 16 Digital Identity Management System Using Articial Neural Networks (Applied
Da), River Publishers.
Agrawal, A.K., Gans, J. and Goldfarb, A. (2018), Economics of articial intelligence, available at:
https://conference.nber.org/conferences/2017/AIf17/summary.html
Alahmad, R. and Robert, L. (2020), Articial intelligence (AI) and IT identity: antecedents identifying
with AI applications,Proceedings of the 26th Americas Conference on Information Systems,
pp. 13-15.
Albiman, M.M. and Sulong, Z. (2016), The role of ICT use to the economic growth in Sub Saharan
African region (SSA),Journal of Science and Technology Policy Management, Vol. 7 No. 3,
pp. 306-329.
Aletras, N., Tsarapatsanis, D., Preotiuc-Pietro, D. and Lampos, V. (2016), Predicting judicial decisions
of the European court of human rights: a natural language processing perspective,PeerJ
Computer Science, Vol. 2, p. e93.
Al-Khouri, A.M. (2014), Digital identity: transforming GCC economies,Innovation, Vol. 16 No. 2,
pp. 184-194.
Allison, A., Currall, J., Moss, M. and Stuart, S. (2005), Digital identity matters,Journal of the American
Society for Information Science and Technology, Vol. 56 No. 4, pp. 364-372.
Amato, F., L
opez, A., Peña-Méndez, E.M., Vanõhara, P., Hampl, A. and Havel, J. (2013), Articial
neural networks in medical diagnosis,Journal of Applied Biomedicine, Vol. 11 No. 2, pp. 47-58.
Andrejevic, M. and Selwyn, N. (2019), Facial recognition technology in schools: critical questions and
concerns,Learning, Media and Technology, Vol. 45 No. 2, pp. 1-14.
Androutsopoulou, A., Karacapilidis, N., Loukis, E. and Charalabidis, Y. (2019), Transforming the
communication between citizens and government through AI-guided chatbots,Government
Information Quarterly, Vol. 36 No. 2, pp. 358-367.
Ashaye, O.R. and Irani, Z. (2019), The role of stakeholders in the effective use of e-government resources
in public services,International Journal of Information Management, Vol. 49, pp. 253-270.
Atick, J. (2016), Digital identity: the essential guide, available at: www.id4africa.com/prev/%0Aimg/
Digital_Identity_The_Essential_Guide.pdf
Ayoub, K. and Payne, K. (2016), Strategy in the age of articial intelligence,Journal of Strategic
Studies, Vol. 39 Nos 5/6, pp. 793-819.
Balboni, P. (2004), Liability of certication service providers towards relying parties and the need for a
clear system to enhance the level of trust in electronic communication,Information and
Communications Technology Law, Vol. 13 No. 3,pp. 211-242.
AI-enabled
digital identity
BankWorld (2016), World development report 2016: digital dividends, The World Bank, available at:
https://doi.org/10.1596/978-1-4648-0671-1
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S.,
Gil-L
opez, S., Molina, D., Benjamins, R. and Herrera, F. (2020), Explainable articial intelligence
(XAI): concepts, taxonomies, opportunities and challenges toward responsible AI,Information
Fusion, Vol. 58, pp. 82-115.
Bates, J. (1992), Virtual reality, art, and entertainment,Presence: Teleoperators and Virtual
Environments, Vol. 1 No. 1, pp. 133-138.
Becerra-Fernadez, I. (2000), The role of articial intelligence technologies in the implementation of
people-nder knowledge management systems,Knowledge-Based Systems, Vol. 13 No. 3,
pp. 315-320, doi:10.1016/S0950-7051(00)00091-5.
Beduschi, A. Cinnamon, J. Langford, J. Luo, C. and Owen, D. (2017), Building digital identities,
Economic and Social Research Council, Vol. 40.
Belk, R.W. (2013), Extended self in a digital world,Journal of Consumer Research, Vol. 40 No. 3, pp. 477-500.
Beymer, D. and Poggio, O. (1995), Face recognition from one example view,Proceedings of IEEE
International Conference on Computer Vision,IEEE, pp. 500-507.
Bhandari, V., Trikanad, S. and Sinha, A. (2020), Governing ID: a framework for evaluation of digital
identity. The Centre for internet and society, available at: https://digitalid.design/docs/
CIS_DigitalID_EvaluationFrameworkDraft02_2020.01.pdf
Bhargav-Spantzel, A., Squicciarini, A. and Bertino, E. (2007), Privacy preserving multi-factor
authentication with biometrics,Journal of Computer Security, Vol.15 No. 5, pp. 529-560.
Bhattacherjee (2020), Social Science Research, Principles, Methods, and Practices, LibreTexts, FL.
Bolton, R.J. and Hand, D.J. (2002), Statistical fraud detection: a review,Statistical Science, Vol. 17 No. 3,
pp. 235-249.
Bowman, N. and Banks, J. (2019), Social and entertainment gratications of videogame play
comparing robot, AI, and human partners,2019 28th IEEE International Conference on Robot
and Human Interactive Communication (RO-MAN),New Delhi, pp. 1-6.
Brynjolfsson, E. and Mitchell, T. (2017), What can machine learning do? Workforce implications,
Science, Vol. 358 No. 6370, pp. 1530-1534.
Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S. and Hofacker, C. (2019), Technological
disruptions in services: lessons from tourism and hospitality,Journal of Service Management,
Vol. 30 No. 4, pp. 484-506.
Cameron, K. (2005), The laws of identity, available at: http://myinstantid.com/laws.pdf
Camp, L.J. (2004), Digital identity,IEEE Technology and Society Magazine, Vol. 23 No. 3, pp. 34-41.
Carr, S. (2007), Participation, power, conict and change: theorizing dynamics of service user participation
in the social care system of England and Wales,Critical Social Policy, Vol. 27 No. 2, pp. 266-276.
Chatterjee, S. and Kar, A.K. (2018), Effects of successful adoption of information technology enabled
services in proposed smart cities of India: from user experience perspective,Journal of Science
and Technology Policy Management, Vol. 9 No. 2, pp. 189-209.
Clark, J. Dahan, M. Desai, V. Ienco, M. De Labriolle, S. Pellestor, J.-P. Varuhaki, Y. (2016), Digital
identity: towards shared principles for public and private sector cooperation, (July), pp. 1-44,
available at: www.worldbankgroup.org/id4d
Cole, D. (1991), Articial intelligence and personal identity,Synthese, Vol. 88 No.3, pp. 399-417.
Columbus, L. (2019), Top 9 ways articial intelligence prevents fraud, available at: www.forbes.com/
sites/louiscolumbus/2019/07/09/top-9-ways-articial-intelligence-prevents-fraud/#3748f57f14b4
Cusatelli, C. and Giacalone, M. (2015), ICT use by judiciary systems in European union,Journal of
Applied Quantitative Methods, Vol. 10 No. 2, pp. 1-6.
JSTPM
de Sousa, W.G., de Melo, E.R.P., Bermejo, P.H.D.S., Farias, R.A.S. and Gomes, A.O. (2019), How and
where is articial intelligence in the public sector going? A literature review and research
agenda,Government Information Quarterly, Vol. 36 No. 4, p. 101392.
de Vries, K. (2010), Identity, proling algorithms and a world of ambient intelligence,Ethics and
Information Technology, Vol. 12 No. 1, pp. 71-85.
Deeks, A. (2019), The judicial demand for explainable articial intelligence, (August 1, 2019). 119
Colum. L. Rev. (2019 Forthcoming), Virginia Public Law and Legal Theory Research Paper
No. 2019-51, available at: https://ssrn.com/abstract=3440723
Desai, V.T., Diofasi, A. and Lu, J. (2018), The global identication challenge: who are the 1 billion
people without proof of identity?, available at: https://blogs.worldbank.org/voices/global-
identication-challenge-who-are-1-billion-people-without-proof-identity
Dixon, P. (2017), A failure to do no harm Indias Aadhaar biometric ID program and its inability to protect
privacy in relation to measures in Europe and the US,Health and Technology, Vol. 7 No. 4, pp. 539-567.
Duijst, D. (2017), Can we Improve the User Experience of Chatbots withPersonalisation?, University of
Amsterdam.
Dunn, W. (2017), Public Policy Analysis: An Integrated Approach, 6th ed., Routledge, New York, NY.
Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R.,
Edwards, J., Eirug, A. and Williams, M.D. (2019), Articial intelligence (AI): multidisciplinary
perspectives on emerging challenges, opportunities, and agenda for research, practice and
policy,International Journal of Information Management, Vol. 57, p. 101994.
Ellemers, N., Spears, R. and Doosje, B. (2002), Self and social identity,Annual Review of Psychology,
Vol. 53 No. 1, pp. 161-186.
Ema,A.,Akiya,N.,Osawa,H.,Hattori,H.,Oie,S.,Ichise,R.,Kanzaki,N.,Kukita,M.,Saijo,R.,Takushi,O.
and Yashiro, Y. (2016), Future relations between humans and articial intelligence: a stakeholder
opinionsurveyinJapan,IEEE Technology and Society Magazine, Vol. 35 No. 4, pp. 68-75.
Fischer, G. and Nakakoji, K. (1992), Beyond the macho approach of articial intelligence: empower
human designers do not replace them,Knowledge-Based Systems, Vol. 5 No. 1, pp. 15-30.
Flasi
nski, M. (2016), Introduction to Articial Intelligence, Springer.
Freeman, R.E. (2015), Stakeholder theory,Wiley Encyclopedia of Management, John Wiley and Sons,
Chichester, pp. 1-6, doi: 10.1002/9781118785317.weom020179.
Goldkuhl, G. (2004), Design theories in information systems a need for Multi-Grounding,Journal of
Information Technology Theory and Application (JITTA), Vol. 6 No. 2, pp. 59-72, available at:
https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1127&context=jitta
Goode, J. (2010), The digital identity divide: how technology knowledge impacts college students,
New Media and Society, Vol. 12 No. 3, pp. 497-513.
Goodell, G. and Aste, T. (2019), A decentralized digital identity architecture,Frontiers in Blockchain,Vol.2.
Gregor, S. and Jones, D. (2007), The anatomy of a design theory,Journal of the Association for
Information Systems, Vol. 8 No. 5, pp. 312-335, available at: http://citeseerx.ist.psu.edu/viewdoc/
download?doi=10.1.1.232.743&rep=rep1&type=pdf
Grover, P., Kar, A.K. and Davies, G. (2018), “‘Technology enabled health’–insights from twitter analytics with
a socio-technical perspective,International Journal of Information Management, Vol. 43, pp. 85-97.
Grover, P., Kar, A.K. and Ilavarasan, P.V. (2019), Impact of corporate social responsibility on
reputationinsights from tweets on sustainable development goals by CEOs,International
Journal of Information Management, Vol. 48, pp. 39-52.
Hatchuel, A., Le Masson, P., Weil, B., Agogué, M., Kazakçi, A. and Hooge, S. (2016), Multiple forms of
applications and impacts of a design theory -ten years of industrial applications of C-K theory,Impact
of Design Research on Industrial Practice -Tools, Technology, and Training, Vol. 1 No. 1, pp. 189-208.
AI-enabled
digital identity
Helbing, D., Frey, B.S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., Van Den Hoven, J.,
Zicari, R.V. and Zwitter, A. (2019), Will democracy survive big data and articial intelligence?,
Towards Digital Enlightenment, Springer International Publishing, Cham, pp. 73-98,
doi: 10.1007/978-3-319-90869-4_7.
Henneman, P.L., Fisher, D.L., Henneman, E.A., Pham, T.A., Mei, Y.Y., Talati, R., Nathanson, B.H. and
Roche, J. (2008), Providers do not verify patient identity during computer order entry,
Academic Emergency Medicine, Vol. 15 No.7, pp. 641-648.
Horcher, A.-M. and Tejay, G.P. (2009), Building a better password: the role of cognitive load in
information security training,2009 IEEE International Conference on Intelligence and Security
Informatics, IEEE, pp. 113-118.
Horowitz, M.C., Allen, G.C., Saravalle, E., Cho, A., Frederick, K. and Scharre, P. (2018), Articial
Intelligence and International Security, Center for a New American Security.
ITU-T FG-DFS (2017), Review of national identity programs,Itu-T Fg-Dfs.
Jacobsen, E.K.U. (2012), Unique identication: inclusion and surveillance in the Indian biometric
assemblage, available at: https://doi.org/10.1177/0967010612458336
Janssen, M. and Estevez, E. (2013), Lean government and platform-based governance doing more
with less,Government Information Quarterly, Vol. 30, pp. S1-S8.
Jung, J. and Mittal, V. (2020), Political identity and the consumer journey: a research review,Journal of
Retailing, Vol. 96 No. 1,pp. 55-73.
Kaplan, A. and Haenlein, M. (2020), Rulers of the world, unite! The challenges and opportunities of
articial intelligence,Business Horizons, Vol. 63 No. 1, pp. 37-50.
Kar, A.K. (2016), Bio inspired computing a review of algorithms and scope of applications,Expert
Systems with Applications, Vol. 59, pp. 20-32.
Kar, A.K. (2020), What affects usage satisfaction in mobile payments? Modelling user generated
content to develop the digital service usage satisfaction model’”,Information Systems Frontiers,
doi: 10.1007/s10796-020-10045-0.
Kiškis, M. and Petrauskas, R. (2004), ICT adoption in the judiciary: classifying of judicial information,
International Review of Law, Computers and Technology, Vol. 18 No.1, pp. 37-45.
Knight, A. and Saxby, S. (2014), Identity crisis: global challenges of identity protection in a networked
world,Computer Law and Security Review, Vol. 30No. 6, pp. 617-632.
Ku, C.-H. and Leroy, G. (2014), A decision support system: automated crime report analysis and
classication for e-government,Government Information Quarterly, Vol. 31 No. 4, pp. 534-544.
Lalmuanawma, S., Hussain, J. and Chhakchhuak, L. (2020), Applications of machine learning and
articial intelligence for covid-19 (SARS-CoV-2) pandemic: a review,Chaos, Solitons and
Fractals, Vol. 139, p. 110059.
Larson, C. (2018), Chinas AI imperative,Science, Vol. 359 No. 6376, pp. 628-630, doi:10.1126/
science.359.6376.628.
Laurent, M. and Bouzefrane, S. (2015), Digital identity management. ISTE press and Elsevier,
available at: https://doi.org/10.1007/978-3-319-08231-8
Lauterbach, A. (2019), Articial intelligence and policy: quo vadis?,Digital Policy, Regulation and
Governance, Vol. 21No. 3, pp. 238-263.
Levmore, S. and Fagan, F. (2019), The impact of articial intelligence on rules, standards, and judicial
discretion,SSRN Electronic Journal, Vol. 93 No. 1, doi: 10.2139/ssrn.3362563.
Li, J.(J.)., Bonn, M.A. and Ye, B.H. (2019), Hotel employeesarticial intelligence and robotics
awareness and its impact on turnover intention: the moderating roles of perceived
organizational support and competitive psychological climate,Tourism Management,
Vol. 73, pp. 172-181.
JSTPM
Liang, Y., Samtani, S., Guo, B. and Yu, Z. (2020), Behavioral biometrics for continuous authentication
in the internet-of-things era: an articial intelligence perspective,IEEE Internet of Things
Journal, Vol. 7 No. 9, pp. 9128-9143.
Lim, S., Saldanha, T.J.V., Malladi, S. and Melville, N.P. (2013), Theories used in information system
research: insights from complex network analysis,Journal of Information Technology Theory
and Application, Vol. 14 No. 2, pp. 5-46.
Liu, Q., Diao, L. and Tu, G. (2010), The application of articial intelligence in mobile learning,
2010 International Conference on System Science, Engineering Design and Manufacturing
Informatization,IEEE, pp. 80-83.
McKinsey (2010), Inclusive growth and nancial security, available at: http://ccmrm.org/wp-content/
uploads/2015/05/McKinsey-2010-inclusive-growth-report.pdf
McKinsey (2017a), Articial intelligencethe next digital frontier?, Available at: www.mckinsey.
com//media/McKinsey/Industries/AdvancedElectronics/OurInsights/Howarticialintelligencecan
deliverrealvaluetocompanies/MGI-Articial-Intelligence-Discussion-paper.ashx
McKinsey (2017b), Jobs lost, jobs gained: workforce transitions in a time of automation, available at:
www.mckinsey.com//media/mckinsey/featuredinsights/FutureofOrganizations/Whatthefutureof
workwillmeanforjobsskillsandwages/MGI-Jobs-Lost-Jobs-Gained-Report-December-6-2017.ashx
McKinsey (2019), Digital identication: a key to inclusive growth, available at: www.mckinsey.
com//media/mckinsey/featuredinsights/innovation/thevalueofdigitalidfortheglobaleconomy
andsociety/mgi-digital-identication-a-key-to-inclusive-growth.ashx
Mcwaters, R.J. (2016), A blueprint for digital identity the role of nancial institutions in building
digital identity,World Economic Forum, Future of Financial Services Series, (August),
pp. 1-108, available at: www3.weforum.org/docs/WEF_A_Blueprint_for_Digital_Identity.pdf
MacInnis, D.J. (2011), A framework for conceptual contributions in marketing,Journal of Marketing,
Vol. 75 No. 4, pp. 136-154.
Merad, D., Aziz, K.-E., Iguernaissi, R., Fertil, B. and Drap, P. (2016), Tracking multiple persons under
partial and global occlusions: application to customersbehavior analysis,Pattern Recognition
Letters, Vol. 81, pp. 11-20.
Milano, M., OSullivan, B. and Gavanelli, M. (2014), Sustainable policy making: a strategic challenge
for articial intelligence,AI Magazine, Vol. 35 No. 3, p. 22.
Miller, H. and Stirling, R. (2019), Government articial intelligence readiness index 2019, available at:
www.oxfordinsights.com/ai-readiness2019
Mir, U.B., Kar, A.K. and Gupta, M.P. (2020a), Digital identity evaluation framework for social welfare,
IFIP Advances in Information and Communication Technology, Vol. 617, pp. 401-414,
doi: 10.1007/978-3-030-64849-7_36.
Mir, U.B., Kar, A.K., Gupta, M.P. and Sharma, R.S. (2019), Prioritizing digital identity goals the case
study of Aadhaar in India,Digital Transformation for a Sustainable Society in the 21st Century,
pp. 489-501, doi: 10.1007/978-3-030-29374-1_40.
Mir, U.B., Sharma, S., Kar, A.K. and Gupta, M.P. (2020c), Critical success factors for integrating
articial intelligence and robotics,Digital Policy, Regulation and Governance, Vol. 22 No. 4.
Mir, U.B., Kar, A.K., Dwivedi, Y.K., Gupta, M.P. and Sharma, R.S. (2020b), Realizing digital identity in
government: prioritizing design and implementation objectives for Aadhaar in India,
Government Information Quarterly, Vol. 37 No. 2, p. 101442.
Mishra, A. and Mishra, D. (2013), Applications of stakeholder theory in information systems and
technology,Engineering Economics, Vol. 24 No. 3.
Moavenzadeh, J. and de Maar, L. (2018), The known traveller: unlocking the potential of digital
identity for secure and seamless travel,World Economic Forum, (January), available at: www3.
weforum.org/docs/WEF_The_Known_Traveller_Digital_Identity_Concept.pdf
AI-enabled
digital identity
Mohan, R. and Kar, A.K. (2017), #demonetization and its impact on the Indian economy insights from
social media analytics, pp. 363-374, available at: https://doi.org/10.1007/978-3-319-68557-1_32
Motuapuaka, M., Whitlock, E., Kato, E., Uhl, S., Belinson, S., Chang, C., Hoomans, T., Meltzer, D.O.,
Noorani, H., Robinson, K.A. and Cottrell, E. (2015), Dening the benets and challenges of
stakeholder engagement in systematic reviews,Comparative Effectiveness Research, Vol. 13,
doi: 10.2147/CER.S69605.
Nair, V. (2018), An eye for an I: recording biometrics and reconsidering identity in postcolonial India,
Contemporary South Asia, Vol. 26 No. 2, pp. 143-156.
Newman,G.R.andMcNally,M.M.(2005),Identity theft literature review, No. 210459, United States of America,
available at: https://www.ojp.gov/ncjrs/virtual-library/abstracts/identity-theft-literature-review
OCED (2011), Digital Identity Management: Enabling Innovation and Trust in The Internet Economy,
Organisation for Economic Co-operation and Development (OECD), United States.
Okumura, A., Komeiji, S., Sakaguchi, M., Tabuchi, M. and Hattori, H. (2019), Identity verication using face
recognition for articial-intelligence electronic forms with speech interaction,HCI for Cybersecurity,
Privacy and Trust, pp. 52-66, available at: https://doi.org/10.1007/978-3-030-22351-9_4
Okumura, A., Handa, S., Hoshino, T., Tokunaga, N. and Kanda, M. (2020), Identity verication using
face recognition improved by managing check-in behavior of event attendees,Advances in
Articial Intelligence, pp. 291-304, available at: https://doi.org/10.1007/978-3-030-39878-1_26
Olson, E.T. (2015), Personal identity, available at: https://plato.stanford.edu/entries/identity-personal/
(accessed 21 April 2019).
Open Identity Exchange (2018), Digital Identity in The UK : The Cost of Doing Nothing, United Kingdom.
Pan, Y. (2016), Heading toward articial intelligence 2.0,Engineering, Vol. 2 No. 4, pp. 409-413.
Pantano, E. and Pizzi, G. (2020), Forecasting articial intelligence on online customer assistance: evidence
from chatbot patents analysis,Journal of Retailing and Consumer Services, Vol. 55, p. 102096.
Paschen, U., Pitt, C. and Kietzmann, J. (2020), Articial intelligence: building blocks and an innovation
typology,Business Horizons, Vol. 63 No. 2, pp. 147-155.
Pascual, A., Marchini, K. and Miller, S. (2018), 2018 Identity fraud: fraud enters a new era of
complexity, available at: www.javelinstrategy.com/coverage-area/2018-identity-fraud-fraud-
enters-new-era-complexity
Passonneau, R.J., McNamara, D., Muresan, S. and Perin, D. (2017), Preface: special issue on
multidisciplinary approaches to AI and education for reading and writing,International
Journal of Articial Intelligence in Education, Vol. 27 No. 4, pp. 665-670.
Phiri, J. and Agbinya, J.I. (2006), Modelling and information fusion in digital identity management
systems,International Conference on Networking, International Conference on Systems
and International Conference on Mobile Communications and Learning Technologies
(ICNICONSMCL06), pp. 181-181.
Phiri, J., Zhao, T.-J. and Agbinya, J.I. (2011a), Biometrics, device metrics and pseudo metrics in a multifactor
authentication with articial intelligence,Proceedings of the 6th International Conference on
Broadband Communications and Biomedical Applications,IEEE,Melbourne,pp.1-6.
Phiri, J., Zhao, T.-J., Zhu, C.H. and Mbale, J. (2011b), Using articial intelligence techniques to
implement a multifactor authentication system,International Journal of Computational
Intelligence Systems, Vol. 4 No. 4, pp. 420-430.
Power, D.J. (2016), “‘Big brothercan watch us,Journal of Decision Systems, Vol. 25 No. sup1, pp. 578-588.
Purgason, B. and Hibler, D. (2012), Security through behavioral biometrics and articial intelligence,
Procedia Computer Science, Vol. 12, pp. 398-403.
Rahman, M.S. and Ko, M. (2013), Toward systematic identication of stakeholders for healthcare
information systems: a feature-based method,Proceedings of the Nineteenth Americas
Conference on Information Systems,Chicago, IL.
JSTPM
Rai, A., Constantinides, P. and Sarker, S. (2018), Editors comments: next-generation digital platforms:
toward humanAI hybrids,MIS Quarterly, Vol. 43 No. 1, pp. 3-9.
Rifkin, J. (2001), The Age of Access: The New Culture of Hypercapitalism Where All of Life is a
Paid-For Experience, Putnam Publishing Group, available at: https://dl.acm.org/citation.
cfm?id=518530
Rissland, E.L., Ashley, K.D. and Loui, R.P. (2003), AI and law: a fruitful synergy,Articial Intelligence,
Vol. 150 Nos 1/2, pp. 1-15.
Robles Carrillo, M. (2020), Articial intelligence: from ethics to law,Telecommunications Policy,
Vol. 44 No. 6, p. 101937.
Roff, H.M. (2019), Articial intelligence: power to the people,Ethics and International Affairs, Vol. 33
No. 2, pp. 127-140.
Ronald, A. Elizabeth, S.B. Noopur, S. and Neil, S.B. (2017), State of Aadhaar report 2016-17, India.
Rowley, J. (2011), e-Government stakeholders who are they and what do they want?,International
Journal of Information Management, Vol. 31 No. 1, pp. 53-62.
Ryman-Tubb, N.F., Krause, P. and Garn, W. (2018), How articial intelligence and machine learning
research impacts payment card fraud detection: a survey and industrybenchmark,Engineering
Applications of Articial Intelligence, Vol. 76,pp. 130-157.
Santeli, J.T. and Gerdon, S. (2019), 5 Challenges for government adoption of AI, available at: www.
weforum.org/agenda/2019/08/articial-intelligence-government-public-sector/
Sartor, G. and Branting, L. (1998), Judicial Applications of Articial Intelligence, in Sartor, G. and
Branting, K. (Eds), Springer Netherlands, Dordrecht, doi: 10.1007/978-94-015-9010-5.
Schoemaker, E., Baslan, D., Pon, B. and Dell, N. (2020), Identity at the margins:data justice and refugee
experiences with digital identity systems in Lebanon, Jordan, and Uganda,Information
Technology for Development, Vol. 27 No. 1, pp. 13-36.
Selby, J. (2017), Data localization laws: trade barriers or legitimate responses to cybersecurity risks, or
both?,International Journal of Law and Information Technology, Vol. 25 No. 3, pp. 213-232.
Shank, D.B., Graves, C., Gott, A., Gamez, P. and Rodriguez, S. (2019), Feeling our way to machine
minds: peoples emotions when perceiving mind in articial intelligence,Computers in Human
Behavior, Vol. 98, pp. 256-266.
Sharma, R.S. (2016), UIDAIs public policy innovations, National Institute of Public Finance and
Policy, No. 176, pp. 1-19.
Sharma, G.D., Yadav, A. and Chopra, R. (2020), Articial intelligence and effective governance: a
review, critique and research agenda,Sustainable Futures, Vol. 2, p. 100004.
Silver, M.S., Markus, M.L. and Beath, C.M. (1995), The information technology interaction model: a
foundation for the MBA core course,MIS Quarterly, Vol. 19 No. 3, p. 361.
Simon, J.P. (2019), Articial intelligence: scope, players, markets and geography,Digital Policy,
Regulation and Governance, Vol. 21 No. 3, pp. 208-237.
Singh, K.K. (2018), An articial intelligence and cloud based collaborative platform for plant disease
identication, tracking and forecasting for farmers,2018 IEEE International Conference on
Cloud Computing in Emerging Markets (CCEM), IEEE, pp. 49-56.
Sourdin, T. (2018), Judge v. Robot: articial intelligence and judicial Decision-Making,UNSW Law
Journal, Vol. 4 No. 4, pp. 1114-1133.
Singh, H., Kar, A.K. and Ilavarsana, P.V. (2017), Performance assessment of e-government projects:
a multi-construct, multi-stakeholder perspective,Proceedings of the 10th International
Conference on Theory and Practice of Electronic Governance, pp. 558-559.
Strich, F., Mayer, A.-S. and Fiedler, M. (2021), What do I do in a world of articial intelligence? Investigating
the impact of substitutive decision-making AI systems on employeesprofessional role identity,
Journal of the Association for Information Systems,Vol.22No.2,doi:10.17705/1jais.00663.
AI-enabled
digital identity
Sun, T.Q. and Medaglia, R. (2019), Mapping the challenges of articial intelligence in the public sector:
evidence from public healthcare,Government Information Quarterly, Vol. 36 No. 2, pp. 368-383.
Tajitsu, N. (2016), Toyota to build articial intelligence-based driving systems in ve years, available
at: https://in.reuters.com/article/us-toyota-ai/toyota-to-build-articial-intelligence-based-driving-
systems-in-ve-years-idINKCN0Z60BE
Toll,D.,Lindgren,I.,Melin,U.andMadsen,C.Ø.(2020),Values, benets, considerations and risks of AI in
government,JeDEM - EJournal of EDemocracy and Open Government, Vol. 12 No. 1, pp. 40-60.
Van De Kaa, G., Janssen, M. and Rezaei, J. (2018), Technological forecasting and social change
standards battles for business-to-government data exchange: identifying success factors for
standard dominance using the best worst method,Technological Forecasting and Social
Change, Vol. 137, pp. 182-189.
Vapen, A., Carlsson, N., Mahanti, A. and Shahmehri, N. (2016), A look at the third-party identity
management landscape,IEEE Internet Computing, Vol. 20 No. 2, pp. 18-25.
Venkatanarayanan, A. (2018), Aadhaar enrolment costs, available at: https://medium.com/karana/
aadhaar-enrolment-costs-bc17f0d30018 (accessed 4 July 2019).
Verma, C., Tarawneh, A.S., Illes, Z., Stoffova, V. and Singh, M. (2019), National identity predictive
models for the real time prediction of European schools students: preliminary results,
2019 International Conference on Automation, Computational and Technology Management
(ICACTM), IEEE, pp. 418-423.
W.Sadek, A. (2007), Articial intelligence applications in transportation,Articial Intelligence in
Transportation: Information for Application, pp. 1-6.
Walls, J.G., Widmeyer, G.R. and El Sawy, O.A. (1992), Building an information system design theory
for vigilant EIS,Information Systems Research, Vol. 3 No. 1, pp. 36-59.
Wang,X.,Li,X.andLeung,V.C.M.(2015),Articial intelligence-based techniques for emerging heterogeneous
network: state of the arts, opportunities, and challenges,IEEE Access, Vol. 3, pp. 1379-1391.
Warwick, K. (2012), Articial Intelligence: The Basics, 1st ed., Routledge, London.
Waters, R. (2015), Articial intelligence: a virtual assistant for life, available at: www.ft.com/content/
4f2f97ea-b8ec-11e4-b8e6-00144feab7de
Weber, F.D. and Schütte, R. (2019), State-of-the-art and adoption of articial intelligence in retailing,
Digital Policy, Regulation and Governance, Vol. 21 No. 3, pp. 264-279.
Weir,C.S.,Douglas,G.,Carruthers,M.andJack,M.(2009),User perceptions of security, convenience and
usability for ebanking authentication tokens,Computers and Security, Vol. 28 Nos 1/2, pp. 47-62.
Winter, J.S. and Davidson, E. (2019), Governance of articial intelligence and personal health
information,Digital Policy, Regulation and Governance, Vol. 21 No. 3, pp. 280-290.
Wirtz, B.W., Weyerer, J.C. and Geyer, C. (2019), Articial intelligence and the public sector 4applications
and challenges,International Journal of Public Administration, Vol. 42 No. 7, pp. 596-615.
Zanzotto, F.M. (2019), Viewpoint: human-in-the-loop articial intelligence,Journal of Articial
Intelligence Research, Vol. 64, pp. 243-252.
Corresponding author
Umar Mir can be contacted at: mirumar.iitd@gmail.com
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
JSTPM
... NDIV platform acceptance is the degree to which a person consents to the NDIV platform (Chong et al., 2021). There were several terms for the NDIV platform used in previous research studies, including digital identity (Rivera et al., 2017;Engeness, 2021;Korać et al., 2021;Madon and Schoemaker, 2021;Sule et al., 2021); privacy-preserving authentication technology (Harbach et al., 2013); digital identity system (Mir et al., 2021;; and biometric facial recognition system (Hizam et al., 2021). The different countries had different names for their digital identity verification platform. ...
... The NDIV platform was viewed as beneficial not only to citizens of a country but also to refugees in refugee management (Madon and Schoemaker, 2021;; education; health services, and other social journal.ump.edu.my/jgi ◄ benefits (Sule et al., 2021); stakeholders and policymakers (Mir et al., 2021); and for better information security in elearning systems (Korać et al., 2021;Engeness, 2021). The implementation of the NDIV platform showed numerous success stories in the countries it was implemented. ...
Article
Full-text available
This study aims to investigate the determinants of NDIV platform acceptance among young investors in Malaysia within the context of protection motivation theory and uncertainty reduction theory. The study proposed a conceptual model comprising five hypotheses tested using structural equation modelling-partial least squares. Data was collected through an online questionnaire survey from 361 young investors in Malaysia. The results show that acceptance of the NDIV platform is directly influenced by perceived severity, response efficacy, self-efficacy, and transparency. This study extended the protection motivation theory by incorporating uncertainty reduction theory to strengthen the predictions of NDIV platform acceptance among young investors in Malaysia. The study brought meaning by filling the theoretical, empirical, and methodological gaps in the body of knowledge. This research contributes to the administrators, regulators, industry practitioners, and government to improve the platforms' strategies and increase citizen engagement conclusions.
... The declarations of European countries on Cooperation in the Artificial Intelligence sector have also clearly followed the qualifications between human and technological innovations and human instrumental interests in a friendly and sophisticated framework (Mir et al., 2021). Because of that, they assure that humans and machines do the critical work in changing times and applications. ...
Article
Full-text available
Efforts to educate the millennial generation can be made in many ways. One of the points is using digital application devices because millennials are the most involved in various virtual worlds. We carried out this study to obtain scientific evidence on how to improve the legal compliance of millennial citizens through the use of virtual applications, which are now increasingly becoming phenomenal. We get supporting data from various scientific applications in secondary data, including books, scientific publications, legal learning websites, and point technology. It is an efficient approach or strategy to encourage the younger generation to care about the importance of the law; this method is carried out with the consideration that the younger generation are millennials who are very close to the virtual world—technology applications.
... Accordingly, we see research in the area of security and standardization of decentralized technological infrastructures as an important aspect of future research [86]. Another point is the potential and challenges of the inclusion of artificial intelligence in digital identity systems for its various stakeholders [111]. ...
Article
In recent years, potentially disruptive identity-related topics emerged, such as digital twin technology for product lifecycle management or self-sovereign identity (SSI) for sovereign data control. In this study, we identify research streams and emerging trends in academic research on digital identity through a bibliometric analysis of 1,395 peer-reviewed articles and their 44,412 references. We derive seven distinct research streams and their interrelations by means of co-citation analysis. We name the seven research streams: i) Digital twin technology for smart manufacturing and industrial health monitoring, ii) identity-based signcryption schemes, iii) distributed networks and user privacy, iv) user authentication in wireless sensor networks, v) attribute-based encryption schemes, vi) secure data exchange in the Internet of Things and vii) blockchain and smart contracts for secure data management. Each stream’s high-impact publications and its development over time are reviewed and the interrelation between publications and streams are visualized. In addition, we extract directions for future research from the field’s most influential publications. The results offer a comprehensive and systematic overview of publications and discourses in digital identity research.
... Furthermore, it should be robust to fraudulent behaviour and cyberattacks. Moreover, AI also can both identify and verify a person's image as well as speed, accuracy, efficiency and utility ( Mir, Kar & Gupta, 2021 ). However, for this to occur, the AI must need to learn and improve its decision-making ability in face detection ( Mir et al., 2020b ) ...
Article
Biometrics in an airport environment can provide a contactless way of identity verification. U.S. Department of Homeland Security (DHS) has been trialling and implementing the Biometric Entry Exit Program at U.S. Customs and Border Control (CBP). Using the Traveller Verification System (TVS), the program biometrically confirms the traveller's identity and their entry or exit, with an increased ability to detect fraudulent documents and visa overstays. This paper assesses the Biometric Exit Program to analyse the use of biometrics at airports and identify the challenges faced. An analysis is conducted on the Entry Exit Program at Dublin Airport, including facial recognition boarding gates. Pilot test results from Dublin Airport and other U.S. airports are used to identify challenges. These included a gap in stakeholder support, low biometric matching rate, infrastructure and network connectivity issues, privacy concerns amongst travellers, and heavy reliance on airlines. Recommendations and solutions for advancement are provided.
... One group (red colour) comprises authors located in Australia [28,29] dealing with the use of immersive technologies (e.g., VR) for destination experiences, in situations of over-tourism leading to deterioration of the sites. Another group (in green), aggregates researchers from different countries, such as USA, France, or England, [30][31][32][33], and deals with AI for sustainable purposes at a firm level but considering different external stakeholders. The blue group also examines AI and other related technologies, for sustainable issues (e.g., France, England, Australia, Malaysia), but more focused in internal stakeholders [34,35]. ...
Article
Full-text available
To date, tourism is the fastest growing industry globally, but one of the least developed in terms of environmentally sustainable practices. However, only a small portion of documents elaborate on how the introduction of new technologies can impact a more sustainable development route for tourism. This study’s objective is to provide an overview on literature state-of-the-art related to sustainable tourism and technological innovations, offering insights for further advancing this domain. We employ a bibliometric analysis and a comprehensive review of 139 articles, collected from Web of Science and Scopus databases, for the purpose of: (i) exploring and discussing the most relevant contributions in the publication network: (ii) highlighting key issues and emerging topics; (iii) uncovering open questions for the future. Our findings reveal contradictory views on the risks and benefits of technology adoption. Artificial intelligence, internet of things, circular economy, big data, augmented and virtual reality emerge as major trends. Five work streams are identified and described, leading to a broader perspective on how technology can shape the future of sustainable tourism. Relevant theoretical and managerial implications are derived. Finally, a research agenda is proposed as guidance for future studies addressing the outcomes of digital disruption on sustainable tourism.
Article
Sentiment analysis of the text deals with the mining of the opinions of people from their written communication. With the increasing usage of online social media platforms for user interactions, abundant opinionated textual data emerges. Therefore, it leads to increased mining of opinions and sentiments and hence greater interest in sentiment analysis. The article introduces the novel Lexico-Semantic features and their use in the sentiment polarity task of English language text. These features are derived using the semantic extension of the lexicons by employing sentiment lexicons and semantic models. These features make data sample size consistent when used in deep learning settings, thereby eliminating the zero padding. For evaluation, we use different semantic models and lexicons to determine the role and impact of Lexico-Semantic features in classification performance. These features, along with the other features, are used to train the different classifiers. Our experimental evaluation shows that introducing Lexico-Semantic features to various state-of-the-art methods of both machine and deep learning improves the overall performance of classifiers.
Article
With growing trade volumes and more stringent regulations, digital transformation (DT) is crucial for container lines to keep abreast with the new trends in the container shipping industry. The objective of this research is to theoretically identify the crucial success elements for DT in container lines by evaluating four main theories: (1) innovation diffusion theory, (2) resource-based view theory, (3) stakeholder theory, and (4) competence motivation theory. Accordingly, the CSFs identified were ‘stakeholder expectations’, ‘organisational competency’, ‘technology acceptance’, and ‘individual motivation’. A survey was then crafted and handed out to major container lines. The fuzzy analytic hierarchy process (FAHP) was then conducted to analyse the collected data. The results show that organisational competency is the most important CSF, followed by technology acceptance, stakeholders’ expectations, and individual motivation. The top three sub-CSFs are financial stability, regulatory bodies’ expectations, and digital readiness. This research paper contributes to theory and management practices by providing a holistic theoretical framework to identify and rank the CSFs of DT in container lines. Overall, this research paper enriches current literature on the DT of container lines and offers new insights into container lines in the CSFs in the implementation of DT.
Article
Full-text available
This study attempts to explore the contextual factors that play a significant role in promoting collaborative governance using mobile phones in developing countries. The study utilises review of academic literature and experts’ opinion to identify critical conversion factors and their interrelationship. Affordance Theory is used as a theoretical lens to identify eight significant factors covering development of infrastructure, citizen up-skilling, cost of access, ease of use, reliable infrastructure, ensured privacy & security, process accountability and a standardised m-governance policy. A combination of Total Interpretative Structure Modelling (TISM) and Cross-impact matrix multiplication applied to classification (MICMAC) analysis is employed to prioritise these conversion factors and classify them based on their dependence and driving power. A priority-based hierarchical model is proposed for establishing a sustainable m-governance ecosystem.
Article
Purpose The purpose of this study was to ascertain how real options investment perspective could be applied towards monetization of customer futures through the deployment of machine learning (ML) and artificial intelligence (AI)-based persuasive technologies. Design/methodology/approach The authors embarked on a theoretical treatise as advocated by scholars (Cornelissen, 2019; Barney, 2018; Cornelissen, 2017; Smithey Fulmer, 2012; Bacharach, 1989; Whetten, 1989; Weick,1989). Towards this end, theoretical argumentative logic was incrementally used to build an integrated perspective on the deployment of learning and AI-based persuasive technologies. This was carried out with strategic real options investment perspective to secure customer futures on m-commerce apps and e-commerce sites. Findings M-commerce apps and e-commerce sites have been deploying ML and AI-based tools (referred to as persuasive technologies), to nudge customers for increased and quicker purchase. The primary objective was to increase engagement time of customers (at an individual level), grow the number of customers (at market level) and increase firm revenue (at an organizational level). The deployment of any persuasive technology entailed increased investment (cash outflow) but was also expected to increase the level of revenue and margin (cash inflow). Given the dynamics of market and the emergent nature of persuasive technologies, ascertaining favourable cash flow was challenging. Real options strategy provided a robust theoretical perspective to time the persuasive technology-related investment in stages. This helped managers to be on time with loading customer purchase with increased temporal immediacy. A real options investment space involving six spaces has also been developed in this conceptual work. These were Never Invest, Immediately Investment, Present-day Investment Possibility, Possibly Invest Later, Invest Probably Later and Possibly Never Invest. Research limitations/implications The foundations of this study domain encompassed work done by an eclectic mix of scholars like from technology management (Siggelkow and Terwiesch, 2019a; Porter and Heppelmann, 2014), real options (Trigeorgis and Reuer, 2017; Luehrman, 1998a, 1998b), marketing intelligence and planning (Appel et al. , 2020; Thaichon et al. , 2019; Thaichon et al. , 2020; Ye et al. , 2019) and strategy from a demand positioning school of thought (Adner and Zemsky, 2006). Practical implications The findings would help managers to comprehend what level of investments need to be done in a staggered manner. The phased way of investing towards the deployment of ML and AI-based persuasive technologies would enable better monetization of customer futures. This would aid marketing managers for increased customer engagement at the individual level, fast monetization of customer futures and increased number of customers and consumption on m-commerce apps and e-commerce sites. Originality/value This was one of the first studies to apply real options investment perspective towards the deployment of ML and AI-based persuasive technologies for monetizing customer futures.
Conference Paper
Full-text available
Identification systems are vital in improving efficiency and enabling innovation for public and private-sector services, such as greater efficiency in the delivery of social safety nets and facilitating the development of digital economies. With all these benefits along with the rapid improvement in the technology has led many countries to adopt a new foundational digital identity system (DIS) or retrofit the existing paper-based identity system especially in the developing economies. Apart from all these benefits, DISs has also been criticized for issues related to the security, privacy, surveillance and exclusion of people from various services they are entitled to. Considering the significant impact of DIS on the people, it is necessary to have an evaluation framework that could help understand the suitability of a DIS in a particular context. In this study, we propose a conceptual evaluation framework specifically for DISs based on the processes followed, regulations and technologies employed.
Article
Full-text available
Purpose This paper aims to enlighten stakeholders about critical success factors (CSFs) in developing intelligent autonomous systems (IASs) by integrating artificial intelligence (AI) with robotics. It suggests a prioritization hierarchy model for building sustainable ecosystem for developing IASs. Design/methodology/approach This paper is based on the existing literature and on the opinion of 15 experts. All the experts have minimum of eight years of experience in AI and related technologies. The CSF theory is used as a theoretical lens and total interpretative structure modelling (TISM) is used for the prioritization of CSFs. Findings Developing countries like India could leverage IASs and associated technologies for solving different societal problems. Policymakers need to develop basic policies regarding data collection, standardized hardware, skilled manpower, funding and start-up culture that can act as building blocks in undertaking sustainable ecosystem for developing IASs and implementing national AI strategy. Clear-cut regulations need to be in place for the proper functioning of the ecosystem. Any technology that can function properly in India has better chances of working at the global level considering the size of the population. Research limitations/implications This paper had all its experts from India only, and that makes the limitation of this paper, as there is a possibility that some of the factors identified may not hold same significance in other countries. Practical implications Stakeholders will understand the critical factors that are important in developing sustainable ecosystem for IASs and what should be the possible order of activities corresponding to each CSF. Originality/value The paper is the first of its kind that has used the CSF theory and TISM methodology for the identification and prioritization of CSFs in developing IASs. Further, eight significant factors, that is, emerging economy multinational enterprises (EMNEs), governance, utility, manpower, capital, software, data and hardware, have come up as the most important factors in integrating AI with robotics in India.
Article
Full-text available
Mobile payment services have become increasingly important in daily lives in India due to multiple planned and unplanned events. The objective of this study is to identify the determinants of usage satisfaction of mobile payments which could enhance service adoption. The “Digital Service Usage Satisfaction Model” has been proposed and validated by combining technology adoption and service science literature. First the data was extracted from Twitter based on hashtags and keywords. Then using sentiment mining and topic modelling the large volumes of text were analysed. Then network science was also used for identifying clusters among associated topics. Then, using content analysis methodology, a theoretical model was developed based on literature. Finally using multiple regression analysis, we validated the proposed model. The study establishes that cost, usefulness, trust, social influence, credibility, information privacy and responsiveness factors are more important to increase the usage satisfaction of mobile payments services. Also methodologically, this is an endeavour to validate a new approach which uses social media data for developing a inferential theoretical model.
Article
Full-text available
There is currently an ongoing, global race to develop, implement, and make use of AI in both the private and public sectors. How AI will affect responsibilities and public values to be upheld by government remains to be seen. This paper analyzes how AI is portrayed in Swe-dish policy documents and what values are attributed to the use of AI, based on an established e-government value framework. Statements are identified in policy documents and are coded into one of four value ideals, as well as being either a benefit, a consideration, or a risk. We conclude that there is discrepancy in the policy level discourse concerning AI between the different value ideals and that the discourse surrounding AI is overly optimistic. A more nuanced view of AI in government is needed to create realistic expectations.
Article
To adequately estimate the beneficial and harmful effects of artificial intelligence (AI), we must first have a clear understanding of what AI is and what it is not. We need to draw important conceptual and definitional boundaries to ensure we accurately estimate and measure the impacts of AI from both empirical and normative standpoints. This essay argues that we should not conflate AI with automation or autonomy but keep them conceptually separate. Moreover, it suggests that once we have a broad understanding of what constitutes AI, we will see that it can be applied to all sectors of the economy and in warfare. However, it cautions that we must be careful where we apply AI, for in some cases there are serious epistemological concerns about whether we have an appropriate level of knowledge to create such systems. Opening the aperture to include such questions allows us to further see that while AI systems will be deployed in a myriad of forms, with greater or lesser cognitive abilities, these systems ought never to be considered moral agents. They cannot possess rights, and they do not have any duties.
Article
As technology continues to change the way in which we work and function, there are predictions that many aspects of human activity will be replaced or supported by newer technologies. Whilst many human activities have changed over time as a result of human advances, more recent shifts in the context of technological change are likely to have a broader impact on some human functions that have previously been largely undisturbed. In this regard, technology is already changing the practice of law and may for example, reshape the process of judging by either replacing, supporting or supplementing the judicial role. Such changes may limit the extent to which humans are engaged in judging with an increasing emphasis on artificial intelligence to deal with smaller civil disputes and the more routine use of related technologies in more complex disputes.
Article
Artificial intelligence (AI) systems in the workplace increasingly substitute for employees' tasks, responsibilities, and decision-making. Consequently, employees must relinquish core activities of their work processes without the ability to interact with the AI system (e.g., to influence decision-making processes or adapt or overrule decision-making outcomes). To deepen our understanding of how substitutive decision-making AI systems affect employees' professional role identity and how employees adapt their identity in response to the system, we conducted an in-depth case study of a company in the area of loan consulting. We qualitatively analyzed more than 60 interviews with employees and managers. Our research contributes to the literature on IS and identity by disclosing mechanisms through which employees strengthen and protect their professional role identity despite being unable to directly interact with the AI system. Further, we highlight the boundary conditions for introducing an AI system and contribute to the body of empirical research on the potential downsides of AI.
Article
This paper examines refugees' experiences with and perspectives on the digital identity systems used by humanitarian organizations to collect, manage, and share their personal data. Through a qualitative study with 198 refugees in Lebanon, Jordan, and Uganda, we show how existing humanitarian identity systems present numerous challenges for refugees. For example, we find that refugees have little to no knowledge of the institutional systems and processes through which their personal data are managed and used. In addition, refugees are typically not able to exercise agency with regard to data that are collected about them (e.g. given choices about the data collected). At the same time, we show how refugees make active efforts to negotiate the various identities available to them, consciously weighing the benefits and constraints associated with different statuses to maximize their access to services, eligibility for employment, and spatial mobility. We engage with Taylor's lens of data justice to make sense of our findings and conclude by highlighting the potential of feminist science and technology study frameworks to further develop theories of data justice that can support analysis of identification systems that serve the interests of the most vulnerable.
Article
Background and objective : During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic. Method A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic. Result This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead. Conclusion The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.