Yojana Magazine February 2024 - 2 | Current Affairs & Hindu Analysis: Daily, Weekly & Monthly - UPSC PDF Download

 Page 1


25 February 2024
rtificial i ntelligence (Ai) is considered to 
be one of the emerging technologies 
for nations’ industrial and economic 
development. t hinking back to the way 
a steam engine and electricity played their roles in 
shaping the first industrial revolution and gradually 
becoming infrastructural transformational assets, 
Ai will also play a key role in the next industrial 
revolution and likewise gradually become 
embedded across industries. every industry and its 
employees will have to embrace Ai and leverage it 
across functions. Alan turing, the British computer 
scientist, introduced the turing Machine and 
highlighted that any problem could be solved as 
use cAses of generATIVe ArTIfIcIAL 
InTeLLIgence In goVernAnce
Intelligence in AI applications stems from having a strong ability to solve 
problems through reasoning, learning, and subsequently incorporating 
diverse human functions. Governments may embrace AI in general and 
GAI in particular in their activities. One way to do that may be through 
undertaking capacity enhancement programmes in areas like Data Science 
and Decision Science where government employees may develop a better 
understanding of AI in general and GAI as well. GAI, like other AI tools, 
could play an important and critical role in the digital transformation of 
governments and public sector undertakings. This technology will help 
governments to be nimbler and more agile in their decision-making and 
connect with stakeholders more effectively.
prof yogeSh k dwivedi prof arpan kuMar kar the author is a Professor of digital Marketing and innovation and director of digital futures for Sustainable Business & Society 
research Group at the School of Management, Swansea University, UK. email: y.k.dwivedi@swansea.ac.uk
The author is a Chair Professor at the Department of Management Studies and School of Artificial Intelligence, Indian Institute of 
technology delhi, india. email: arpankar@iitd.ac.in
A
Page 2


25 February 2024
rtificial i ntelligence (Ai) is considered to 
be one of the emerging technologies 
for nations’ industrial and economic 
development. t hinking back to the way 
a steam engine and electricity played their roles in 
shaping the first industrial revolution and gradually 
becoming infrastructural transformational assets, 
Ai will also play a key role in the next industrial 
revolution and likewise gradually become 
embedded across industries. every industry and its 
employees will have to embrace Ai and leverage it 
across functions. Alan turing, the British computer 
scientist, introduced the turing Machine and 
highlighted that any problem could be solved as 
use cAses of generATIVe ArTIfIcIAL 
InTeLLIgence In goVernAnce
Intelligence in AI applications stems from having a strong ability to solve 
problems through reasoning, learning, and subsequently incorporating 
diverse human functions. Governments may embrace AI in general and 
GAI in particular in their activities. One way to do that may be through 
undertaking capacity enhancement programmes in areas like Data Science 
and Decision Science where government employees may develop a better 
understanding of AI in general and GAI as well. GAI, like other AI tools, 
could play an important and critical role in the digital transformation of 
governments and public sector undertakings. This technology will help 
governments to be nimbler and more agile in their decision-making and 
connect with stakeholders more effectively.
prof yogeSh k dwivedi prof arpan kuMar kar the author is a Professor of digital Marketing and innovation and director of digital futures for Sustainable Business & Society 
research Group at the School of Management, Swansea University, UK. email: y.k.dwivedi@swansea.ac.uk
The author is a Chair Professor at the Department of Management Studies and School of Artificial Intelligence, Indian Institute of 
technology delhi, india. email: arpankar@iitd.ac.in
A
26 February 2024
long as it could be represented and decoded by an 
algorithm. Furthermore, we can now look back and 
see that his attention subsequently moved towards 
research in Ai when he proposed the ‘imitation 
game’ where he tested if a computer was a thinking 
machine or not.
ever since then, while Ai was in its infancy 
phase, the focus of researchers working within 
this discipline has been on real-life applications. 
in the early phase, these researchers were mostly 
mathematicians and computer scientists, but this 
evolved when information systems researchers 
started exploring the usage and impacts of this 
technology in socio-technical and industrial 
settings. in the early days, developments in Ai 
drew inspiration from biological organisms and 
the physical characteristics of nature to solve 
data-intensive problems. in fact, intelligence in 
Ai applications stems from having a strong ability 
to solve problems through reasoning, learning, 
and subsequently incorporating diverse human 
functions such as thinking, memorising, 
communicating, and planning. Models like 
‘supervised learning’ and ‘unsupervised 
learning’ emerged, which tried to replicate the 
way natural intelligence in biological systems 
operates (Kar, 2016). However, over time, 
newer models of artificial intelligence evolved 
like ‘deep learning’, ‘reinforcement learning’, 
‘federated learning’, and many other models, 
which gradually started gaining importance 
in industrial applications (Kar et al., 2022). An 
extension of these Ai algorithms is generative 
Artificial intelligence (gAi). 
gAi is currently being discussed across 
different platforms very elaborately. it can be 
thought of as an extension of existing models 
of artificial intelligence that harness advances in 
the architecture of deep learning and have led 
to the creation of very effective chatbots. in the 
background, gAi operates on Large Language 
Models, which have been trained on much 
larger datasets (such as texts and information 
garnered from an enormous world-wide web 
corpus) than in the past, leading to high-quality 
performance in an extensive variety of natural-
language tasks (including language generation, 
translation, question answering, creating logical 
essays), and even algorithmic code for computer 
programmes. 
r eviews of the scientific literature indicate 
that there are different models of gAi that are 
now deployed in different business settings 
(Dwivedi et al., 2023; Kar et al., 2023). However, 
in recent times, there have been a lot of concerns 
surrounding the capabilities and disruption this 
rapidly evolving technology may create in the 
larger socio-economic fabric of the ecosystem. 
in this context, we discuss how governments can 
use gAi and leverage its technology effectively. 
in the rest of this article, we will briefly explore 
the following three questions:
Q1: What are the different types of gAi applications 
available today?
Q2: How can governments use these gAi 
applications?
Q3: How should governments plan to counter 
adverse impacts of gAi use?
o verview of Current Gai technologies
there are many gAi technologies currently 
available (see table 1). While chatgPt continues 
to draw most attention and has brought this 
technology into everyone’s consciousness, there 
are quite a few other tools with similar capabilities. 
in this section, we provide an overview of these 
existing Ai tools. 
For a more detailed review of these 
technologies, please refer to Kar et al. (2023). While 
many of these technologies are already integrated 
into our daily lives, we may not always recognise 
them as explicitly gAi applications. 
Generative ai u se Cases for Governments
gAi presents lots of opportunities to 
governments when it comes to automating 
internal processes and enhancing the experiences 
of stakeholders through faster resolutions. For 
example, a platform for query resolutions could be 
created where citizens are able to see the status 
of their service requests (rather than having to 
speak with a government employee to find out). 
Furthermore, governments and public sector 
organisations could bring about a dramatic 
transformation in terms of their responsiveness 
and flexibility by leveraging gAi language models 
that have been extensively used to comprehend 
different stakeholder wants, successfully target 
them with suitable services, and request resolution 
in a timely manner. Moreover, gAi has the ability to 
Page 3


25 February 2024
rtificial i ntelligence (Ai) is considered to 
be one of the emerging technologies 
for nations’ industrial and economic 
development. t hinking back to the way 
a steam engine and electricity played their roles in 
shaping the first industrial revolution and gradually 
becoming infrastructural transformational assets, 
Ai will also play a key role in the next industrial 
revolution and likewise gradually become 
embedded across industries. every industry and its 
employees will have to embrace Ai and leverage it 
across functions. Alan turing, the British computer 
scientist, introduced the turing Machine and 
highlighted that any problem could be solved as 
use cAses of generATIVe ArTIfIcIAL 
InTeLLIgence In goVernAnce
Intelligence in AI applications stems from having a strong ability to solve 
problems through reasoning, learning, and subsequently incorporating 
diverse human functions. Governments may embrace AI in general and 
GAI in particular in their activities. One way to do that may be through 
undertaking capacity enhancement programmes in areas like Data Science 
and Decision Science where government employees may develop a better 
understanding of AI in general and GAI as well. GAI, like other AI tools, 
could play an important and critical role in the digital transformation of 
governments and public sector undertakings. This technology will help 
governments to be nimbler and more agile in their decision-making and 
connect with stakeholders more effectively.
prof yogeSh k dwivedi prof arpan kuMar kar the author is a Professor of digital Marketing and innovation and director of digital futures for Sustainable Business & Society 
research Group at the School of Management, Swansea University, UK. email: y.k.dwivedi@swansea.ac.uk
The author is a Chair Professor at the Department of Management Studies and School of Artificial Intelligence, Indian Institute of 
technology delhi, india. email: arpankar@iitd.ac.in
A
26 February 2024
long as it could be represented and decoded by an 
algorithm. Furthermore, we can now look back and 
see that his attention subsequently moved towards 
research in Ai when he proposed the ‘imitation 
game’ where he tested if a computer was a thinking 
machine or not.
ever since then, while Ai was in its infancy 
phase, the focus of researchers working within 
this discipline has been on real-life applications. 
in the early phase, these researchers were mostly 
mathematicians and computer scientists, but this 
evolved when information systems researchers 
started exploring the usage and impacts of this 
technology in socio-technical and industrial 
settings. in the early days, developments in Ai 
drew inspiration from biological organisms and 
the physical characteristics of nature to solve 
data-intensive problems. in fact, intelligence in 
Ai applications stems from having a strong ability 
to solve problems through reasoning, learning, 
and subsequently incorporating diverse human 
functions such as thinking, memorising, 
communicating, and planning. Models like 
‘supervised learning’ and ‘unsupervised 
learning’ emerged, which tried to replicate the 
way natural intelligence in biological systems 
operates (Kar, 2016). However, over time, 
newer models of artificial intelligence evolved 
like ‘deep learning’, ‘reinforcement learning’, 
‘federated learning’, and many other models, 
which gradually started gaining importance 
in industrial applications (Kar et al., 2022). An 
extension of these Ai algorithms is generative 
Artificial intelligence (gAi). 
gAi is currently being discussed across 
different platforms very elaborately. it can be 
thought of as an extension of existing models 
of artificial intelligence that harness advances in 
the architecture of deep learning and have led 
to the creation of very effective chatbots. in the 
background, gAi operates on Large Language 
Models, which have been trained on much 
larger datasets (such as texts and information 
garnered from an enormous world-wide web 
corpus) than in the past, leading to high-quality 
performance in an extensive variety of natural-
language tasks (including language generation, 
translation, question answering, creating logical 
essays), and even algorithmic code for computer 
programmes. 
r eviews of the scientific literature indicate 
that there are different models of gAi that are 
now deployed in different business settings 
(Dwivedi et al., 2023; Kar et al., 2023). However, 
in recent times, there have been a lot of concerns 
surrounding the capabilities and disruption this 
rapidly evolving technology may create in the 
larger socio-economic fabric of the ecosystem. 
in this context, we discuss how governments can 
use gAi and leverage its technology effectively. 
in the rest of this article, we will briefly explore 
the following three questions:
Q1: What are the different types of gAi applications 
available today?
Q2: How can governments use these gAi 
applications?
Q3: How should governments plan to counter 
adverse impacts of gAi use?
o verview of Current Gai technologies
there are many gAi technologies currently 
available (see table 1). While chatgPt continues 
to draw most attention and has brought this 
technology into everyone’s consciousness, there 
are quite a few other tools with similar capabilities. 
in this section, we provide an overview of these 
existing Ai tools. 
For a more detailed review of these 
technologies, please refer to Kar et al. (2023). While 
many of these technologies are already integrated 
into our daily lives, we may not always recognise 
them as explicitly gAi applications. 
Generative ai u se Cases for Governments
gAi presents lots of opportunities to 
governments when it comes to automating 
internal processes and enhancing the experiences 
of stakeholders through faster resolutions. For 
example, a platform for query resolutions could be 
created where citizens are able to see the status 
of their service requests (rather than having to 
speak with a government employee to find out). 
Furthermore, governments and public sector 
organisations could bring about a dramatic 
transformation in terms of their responsiveness 
and flexibility by leveraging gAi language models 
that have been extensively used to comprehend 
different stakeholder wants, successfully target 
them with suitable services, and request resolution 
in a timely manner. Moreover, gAi has the ability to 
27 February 2024
improve several aspects of citizen interactions with 
platforms, such as citizen engagement platforms 
like Mygov. this could be achieved by creating 
communication documents on the initial phases of a 
citizen’s engagement process as well as the detailed 
interactions users may have, along with a tailored 
approach to each user in the post-interaction space 
and potential longer-term associations.
Additionally, another area of gAi application 
could be to provide real-time analytical reports 
to decision-makers. governments often need to 
handle and read large amounts of data from which 
to make inform their decision-making. gAi could 
be harnessed here to analyse the huge stream of 
documents that government departments work 
tirelessly to process to generate real-time insights, 
enabling faster and more efficient decision-making. 
t hus, the capability that gAi has when it comes to 
analysing large volumes of text, summarising them, 
or generating specific reports could become a 
very useful government tool. gAi can also present 
high-quality visualisation outputs, which makes it 
easier to comprehend complex data from multiple 
sources. 
it would be important here for the decision-
makers to be competent when it comes to the 
natural language ‘Prompts’ that they may require 
to generate meaningful reports, which otherwise 
may require complex querying and analysis. gAi 
presents an opportunity to train manpower to 
use technology through english prompts. this 
may automate and reduce time drastically for 
many activities like preparing notes of meetings, 
creating abstracts of documents, creating emails, 
and many other language generation activities. it 
can significantly reduce the time spent developing 
documentation in simple, readable language. it 
would also be possible to correct the grammatical 
errors of formal documents very easily using gAi. 
Artificial i ntelligence, including generative 
Ai has already started transforming government 
operations, as evidenced by the following 
innovative applications:
 y the governments of both the united states 
and singapore have initiated the integration of 
chatgPt into their administrative systems. 
 y similarly, in Japan, the Yokosuka city 
government has begun employing chatgPt to 
support its office operations (Yang and Wang, 
2023).
 y the government of estonia has been piloting 
several Ai-related initiatives.
1
 For example, it has 
tested machine learning software to match job 
t able 1: emerging Generative ai technologies
t ype of tool nature of data o verview of outcome it produces
chatgPt , r eplika, Jasper, Youchat, sudowrite, 
c opy.ai, Writesonic
Mostly text c an provide answers to complex 
queries based on public 
information
DALL-e, DALL-e 2, google’s imagen, stable 
Diffusion, Make-A-s cene by Meta Ai, c raiyon, 
Midjourney and MiP-nerF 
text and 
images
Produces realistic photos based 
on text input
Amper Music, Aiva, Amadeus c ode,google’s 
Magenta, ecrett Music, Humtap, Boomy, 
Melodrive, Mubert & sony’s Flow Machines 
Music Produces music based on textual 
prompts
gitHub’s c oPilot, tabnine, Deepc ode, intellicode 
by Microsoft, r eplit’s ghostwriter, Ponicode, 
sourceAi, Ai21Labs’ studio and Amazon’s c ode 
Whisperer 
software 
programmes
generates lines of code based on 
text input
google LaMDA and Bard, Apple siri, Microsoft 
c ortana, samsung Bixby, iBM Watson Assistant, 
soundHound’s Hound, Mycroft, Amazon Alexa, 
and Facebook’s Wit.ai
Audio r esponds to audio prompts and 
generates actions like starting an 
application, playing music, etc.
Page 4


25 February 2024
rtificial i ntelligence (Ai) is considered to 
be one of the emerging technologies 
for nations’ industrial and economic 
development. t hinking back to the way 
a steam engine and electricity played their roles in 
shaping the first industrial revolution and gradually 
becoming infrastructural transformational assets, 
Ai will also play a key role in the next industrial 
revolution and likewise gradually become 
embedded across industries. every industry and its 
employees will have to embrace Ai and leverage it 
across functions. Alan turing, the British computer 
scientist, introduced the turing Machine and 
highlighted that any problem could be solved as 
use cAses of generATIVe ArTIfIcIAL 
InTeLLIgence In goVernAnce
Intelligence in AI applications stems from having a strong ability to solve 
problems through reasoning, learning, and subsequently incorporating 
diverse human functions. Governments may embrace AI in general and 
GAI in particular in their activities. One way to do that may be through 
undertaking capacity enhancement programmes in areas like Data Science 
and Decision Science where government employees may develop a better 
understanding of AI in general and GAI as well. GAI, like other AI tools, 
could play an important and critical role in the digital transformation of 
governments and public sector undertakings. This technology will help 
governments to be nimbler and more agile in their decision-making and 
connect with stakeholders more effectively.
prof yogeSh k dwivedi prof arpan kuMar kar the author is a Professor of digital Marketing and innovation and director of digital futures for Sustainable Business & Society 
research Group at the School of Management, Swansea University, UK. email: y.k.dwivedi@swansea.ac.uk
The author is a Chair Professor at the Department of Management Studies and School of Artificial Intelligence, Indian Institute of 
technology delhi, india. email: arpankar@iitd.ac.in
A
26 February 2024
long as it could be represented and decoded by an 
algorithm. Furthermore, we can now look back and 
see that his attention subsequently moved towards 
research in Ai when he proposed the ‘imitation 
game’ where he tested if a computer was a thinking 
machine or not.
ever since then, while Ai was in its infancy 
phase, the focus of researchers working within 
this discipline has been on real-life applications. 
in the early phase, these researchers were mostly 
mathematicians and computer scientists, but this 
evolved when information systems researchers 
started exploring the usage and impacts of this 
technology in socio-technical and industrial 
settings. in the early days, developments in Ai 
drew inspiration from biological organisms and 
the physical characteristics of nature to solve 
data-intensive problems. in fact, intelligence in 
Ai applications stems from having a strong ability 
to solve problems through reasoning, learning, 
and subsequently incorporating diverse human 
functions such as thinking, memorising, 
communicating, and planning. Models like 
‘supervised learning’ and ‘unsupervised 
learning’ emerged, which tried to replicate the 
way natural intelligence in biological systems 
operates (Kar, 2016). However, over time, 
newer models of artificial intelligence evolved 
like ‘deep learning’, ‘reinforcement learning’, 
‘federated learning’, and many other models, 
which gradually started gaining importance 
in industrial applications (Kar et al., 2022). An 
extension of these Ai algorithms is generative 
Artificial intelligence (gAi). 
gAi is currently being discussed across 
different platforms very elaborately. it can be 
thought of as an extension of existing models 
of artificial intelligence that harness advances in 
the architecture of deep learning and have led 
to the creation of very effective chatbots. in the 
background, gAi operates on Large Language 
Models, which have been trained on much 
larger datasets (such as texts and information 
garnered from an enormous world-wide web 
corpus) than in the past, leading to high-quality 
performance in an extensive variety of natural-
language tasks (including language generation, 
translation, question answering, creating logical 
essays), and even algorithmic code for computer 
programmes. 
r eviews of the scientific literature indicate 
that there are different models of gAi that are 
now deployed in different business settings 
(Dwivedi et al., 2023; Kar et al., 2023). However, 
in recent times, there have been a lot of concerns 
surrounding the capabilities and disruption this 
rapidly evolving technology may create in the 
larger socio-economic fabric of the ecosystem. 
in this context, we discuss how governments can 
use gAi and leverage its technology effectively. 
in the rest of this article, we will briefly explore 
the following three questions:
Q1: What are the different types of gAi applications 
available today?
Q2: How can governments use these gAi 
applications?
Q3: How should governments plan to counter 
adverse impacts of gAi use?
o verview of Current Gai technologies
there are many gAi technologies currently 
available (see table 1). While chatgPt continues 
to draw most attention and has brought this 
technology into everyone’s consciousness, there 
are quite a few other tools with similar capabilities. 
in this section, we provide an overview of these 
existing Ai tools. 
For a more detailed review of these 
technologies, please refer to Kar et al. (2023). While 
many of these technologies are already integrated 
into our daily lives, we may not always recognise 
them as explicitly gAi applications. 
Generative ai u se Cases for Governments
gAi presents lots of opportunities to 
governments when it comes to automating 
internal processes and enhancing the experiences 
of stakeholders through faster resolutions. For 
example, a platform for query resolutions could be 
created where citizens are able to see the status 
of their service requests (rather than having to 
speak with a government employee to find out). 
Furthermore, governments and public sector 
organisations could bring about a dramatic 
transformation in terms of their responsiveness 
and flexibility by leveraging gAi language models 
that have been extensively used to comprehend 
different stakeholder wants, successfully target 
them with suitable services, and request resolution 
in a timely manner. Moreover, gAi has the ability to 
27 February 2024
improve several aspects of citizen interactions with 
platforms, such as citizen engagement platforms 
like Mygov. this could be achieved by creating 
communication documents on the initial phases of a 
citizen’s engagement process as well as the detailed 
interactions users may have, along with a tailored 
approach to each user in the post-interaction space 
and potential longer-term associations.
Additionally, another area of gAi application 
could be to provide real-time analytical reports 
to decision-makers. governments often need to 
handle and read large amounts of data from which 
to make inform their decision-making. gAi could 
be harnessed here to analyse the huge stream of 
documents that government departments work 
tirelessly to process to generate real-time insights, 
enabling faster and more efficient decision-making. 
t hus, the capability that gAi has when it comes to 
analysing large volumes of text, summarising them, 
or generating specific reports could become a 
very useful government tool. gAi can also present 
high-quality visualisation outputs, which makes it 
easier to comprehend complex data from multiple 
sources. 
it would be important here for the decision-
makers to be competent when it comes to the 
natural language ‘Prompts’ that they may require 
to generate meaningful reports, which otherwise 
may require complex querying and analysis. gAi 
presents an opportunity to train manpower to 
use technology through english prompts. this 
may automate and reduce time drastically for 
many activities like preparing notes of meetings, 
creating abstracts of documents, creating emails, 
and many other language generation activities. it 
can significantly reduce the time spent developing 
documentation in simple, readable language. it 
would also be possible to correct the grammatical 
errors of formal documents very easily using gAi. 
Artificial i ntelligence, including generative 
Ai has already started transforming government 
operations, as evidenced by the following 
innovative applications:
 y the governments of both the united states 
and singapore have initiated the integration of 
chatgPt into their administrative systems. 
 y similarly, in Japan, the Yokosuka city 
government has begun employing chatgPt to 
support its office operations (Yang and Wang, 
2023).
 y the government of estonia has been piloting 
several Ai-related initiatives.
1
 For example, it has 
tested machine learning software to match job 
t able 1: emerging Generative ai technologies
t ype of tool nature of data o verview of outcome it produces
chatgPt , r eplika, Jasper, Youchat, sudowrite, 
c opy.ai, Writesonic
Mostly text c an provide answers to complex 
queries based on public 
information
DALL-e, DALL-e 2, google’s imagen, stable 
Diffusion, Make-A-s cene by Meta Ai, c raiyon, 
Midjourney and MiP-nerF 
text and 
images
Produces realistic photos based 
on text input
Amper Music, Aiva, Amadeus c ode,google’s 
Magenta, ecrett Music, Humtap, Boomy, 
Melodrive, Mubert & sony’s Flow Machines 
Music Produces music based on textual 
prompts
gitHub’s c oPilot, tabnine, Deepc ode, intellicode 
by Microsoft, r eplit’s ghostwriter, Ponicode, 
sourceAi, Ai21Labs’ studio and Amazon’s c ode 
Whisperer 
software 
programmes
generates lines of code based on 
text input
google LaMDA and Bard, Apple siri, Microsoft 
c ortana, samsung Bixby, iBM Watson Assistant, 
soundHound’s Hound, Mycroft, Amazon Alexa, 
and Facebook’s Wit.ai
Audio r esponds to audio prompts and 
generates actions like starting an 
application, playing music, etc.
28 February 2024
seekers with employers, 
developed a machine 
vision Ai solution for better 
traffic management, and 
piloted a programme 
under the Ministry of 
Justice to integrate gAi for 
processing judgements in 
small claims disputes where 
the payment amounts to a 
maximum of 7000 euros.
2
 
estonia has also introduced 
‘suve’, a digital assistant 
developed to offer precise 
and trustworthy answers 
to queries from the public.
3
 
 y in singapore, the smart nation initiative utilises 
Ai to optimise traffic management, improving 
urban planning and public transportation. 
s ome elements of service generation is used for 
recommending traffic flow and control through 
the use of mobile crowdsensing.
4
 
 y the us FeMA employs Ai for critical satellite 
imagery analysis to bolster disaster response 
and resource allocation.
5
 
 y the uK’s nHs leverages Ai to inform 
healthcare policies and manage resources 
effectively. Further nHs will soon deploy gAi 
on top of existing Ai tools for diagnosis and 
recommending possible treatments for critical 
illnesses, which require complex detections 
to be made quickly like heart disease and 
strokes. t his initiative of uK is funded by the 
government’s new Ai Diagnostic Fund.
6
 y in the us, a lot of Ai is already being used across 
government functions. the city of seattle has 
released its gAi Policy to signal opportunities 
as well as highlight possible concerns with 
strong guardrails to ensure gAi applications 
are used responsibly and accountability. the 
seven overarching goals are transparency and 
Accountability , explainability and interpretability , 
innovation and sustainability, Bias and Harm 
r eduction and Fairness, validity and r eliability, 
Privacy preservation, security and r esiliency.
7
 
Challenges for Governments
s everal studies have examined the ethical side 
of gAi, like chatgPt , in terms of how ethically it 
responds to specific issues. 
With these in mind, there 
are a few challenges that 
governments will also have to 
tackle if they are to harness the 
capabilities of gAi mindfully 
and safely (Yang and Wang, 
2023). 
one challenge of using 
gAi is the veracity of its 
outputs. the quality of 
the data it ingests plays a 
large role in the credibility 
of the outputs it prepares. 
Furthermore, the responses 
of gAi to factual prompts are 
relatively accurate. However, for prompts that 
require subjective deliberation, gAi applications 
often fail to provide satisfactory responses. While 
for factual queries, responses could have high 
reliability, deliberative queries may need greater 
specification of the context and extensive training 
of the models based on relevant datasets. 
similarly, the use of gAi requires organisations 
to expose their data to gAi systems. t his activity 
has to be done carefully so that the internal 
information assurance protocols and privacy of 
the data do not get breached. cases have been 
witnessed when the internal data of organisations 
was exposed during external queries because the 
data lakes and data warehouses were onboarded 
to gAi platforms. Privacy preservation protocols 
may also have to be developed before exposing 
the government’s data to these large language 
models. 
Like any other Ai technology, gAi systems 
need to establish how they can address the 
principles of FAte , namely Fairness, Accountability, 
transparency, and ethics in Ai. Addressing 
these FAte principles requires investment in 
the governance of these platforms. However, 
addressing these platforms has been seen to 
have significant positive impacts on stakeholder 
experiences when interacting with them (Malik et 
al., 2023).
For example, new Zealand’s government has 
adopted Ai tools extensively to analyse public 
feedback, ensuring citizen-centric policymaking. 
However, an advisory policy has also been 
Like any other AI 
technology, GAI systems 
need to establish how 
they can address the 
principles of FATE, 
namely Fairness, 
Accountability, 
Transparency, and 
Ethics in AI.
Page 5


25 February 2024
rtificial i ntelligence (Ai) is considered to 
be one of the emerging technologies 
for nations’ industrial and economic 
development. t hinking back to the way 
a steam engine and electricity played their roles in 
shaping the first industrial revolution and gradually 
becoming infrastructural transformational assets, 
Ai will also play a key role in the next industrial 
revolution and likewise gradually become 
embedded across industries. every industry and its 
employees will have to embrace Ai and leverage it 
across functions. Alan turing, the British computer 
scientist, introduced the turing Machine and 
highlighted that any problem could be solved as 
use cAses of generATIVe ArTIfIcIAL 
InTeLLIgence In goVernAnce
Intelligence in AI applications stems from having a strong ability to solve 
problems through reasoning, learning, and subsequently incorporating 
diverse human functions. Governments may embrace AI in general and 
GAI in particular in their activities. One way to do that may be through 
undertaking capacity enhancement programmes in areas like Data Science 
and Decision Science where government employees may develop a better 
understanding of AI in general and GAI as well. GAI, like other AI tools, 
could play an important and critical role in the digital transformation of 
governments and public sector undertakings. This technology will help 
governments to be nimbler and more agile in their decision-making and 
connect with stakeholders more effectively.
prof yogeSh k dwivedi prof arpan kuMar kar the author is a Professor of digital Marketing and innovation and director of digital futures for Sustainable Business & Society 
research Group at the School of Management, Swansea University, UK. email: y.k.dwivedi@swansea.ac.uk
The author is a Chair Professor at the Department of Management Studies and School of Artificial Intelligence, Indian Institute of 
technology delhi, india. email: arpankar@iitd.ac.in
A
26 February 2024
long as it could be represented and decoded by an 
algorithm. Furthermore, we can now look back and 
see that his attention subsequently moved towards 
research in Ai when he proposed the ‘imitation 
game’ where he tested if a computer was a thinking 
machine or not.
ever since then, while Ai was in its infancy 
phase, the focus of researchers working within 
this discipline has been on real-life applications. 
in the early phase, these researchers were mostly 
mathematicians and computer scientists, but this 
evolved when information systems researchers 
started exploring the usage and impacts of this 
technology in socio-technical and industrial 
settings. in the early days, developments in Ai 
drew inspiration from biological organisms and 
the physical characteristics of nature to solve 
data-intensive problems. in fact, intelligence in 
Ai applications stems from having a strong ability 
to solve problems through reasoning, learning, 
and subsequently incorporating diverse human 
functions such as thinking, memorising, 
communicating, and planning. Models like 
‘supervised learning’ and ‘unsupervised 
learning’ emerged, which tried to replicate the 
way natural intelligence in biological systems 
operates (Kar, 2016). However, over time, 
newer models of artificial intelligence evolved 
like ‘deep learning’, ‘reinforcement learning’, 
‘federated learning’, and many other models, 
which gradually started gaining importance 
in industrial applications (Kar et al., 2022). An 
extension of these Ai algorithms is generative 
Artificial intelligence (gAi). 
gAi is currently being discussed across 
different platforms very elaborately. it can be 
thought of as an extension of existing models 
of artificial intelligence that harness advances in 
the architecture of deep learning and have led 
to the creation of very effective chatbots. in the 
background, gAi operates on Large Language 
Models, which have been trained on much 
larger datasets (such as texts and information 
garnered from an enormous world-wide web 
corpus) than in the past, leading to high-quality 
performance in an extensive variety of natural-
language tasks (including language generation, 
translation, question answering, creating logical 
essays), and even algorithmic code for computer 
programmes. 
r eviews of the scientific literature indicate 
that there are different models of gAi that are 
now deployed in different business settings 
(Dwivedi et al., 2023; Kar et al., 2023). However, 
in recent times, there have been a lot of concerns 
surrounding the capabilities and disruption this 
rapidly evolving technology may create in the 
larger socio-economic fabric of the ecosystem. 
in this context, we discuss how governments can 
use gAi and leverage its technology effectively. 
in the rest of this article, we will briefly explore 
the following three questions:
Q1: What are the different types of gAi applications 
available today?
Q2: How can governments use these gAi 
applications?
Q3: How should governments plan to counter 
adverse impacts of gAi use?
o verview of Current Gai technologies
there are many gAi technologies currently 
available (see table 1). While chatgPt continues 
to draw most attention and has brought this 
technology into everyone’s consciousness, there 
are quite a few other tools with similar capabilities. 
in this section, we provide an overview of these 
existing Ai tools. 
For a more detailed review of these 
technologies, please refer to Kar et al. (2023). While 
many of these technologies are already integrated 
into our daily lives, we may not always recognise 
them as explicitly gAi applications. 
Generative ai u se Cases for Governments
gAi presents lots of opportunities to 
governments when it comes to automating 
internal processes and enhancing the experiences 
of stakeholders through faster resolutions. For 
example, a platform for query resolutions could be 
created where citizens are able to see the status 
of their service requests (rather than having to 
speak with a government employee to find out). 
Furthermore, governments and public sector 
organisations could bring about a dramatic 
transformation in terms of their responsiveness 
and flexibility by leveraging gAi language models 
that have been extensively used to comprehend 
different stakeholder wants, successfully target 
them with suitable services, and request resolution 
in a timely manner. Moreover, gAi has the ability to 
27 February 2024
improve several aspects of citizen interactions with 
platforms, such as citizen engagement platforms 
like Mygov. this could be achieved by creating 
communication documents on the initial phases of a 
citizen’s engagement process as well as the detailed 
interactions users may have, along with a tailored 
approach to each user in the post-interaction space 
and potential longer-term associations.
Additionally, another area of gAi application 
could be to provide real-time analytical reports 
to decision-makers. governments often need to 
handle and read large amounts of data from which 
to make inform their decision-making. gAi could 
be harnessed here to analyse the huge stream of 
documents that government departments work 
tirelessly to process to generate real-time insights, 
enabling faster and more efficient decision-making. 
t hus, the capability that gAi has when it comes to 
analysing large volumes of text, summarising them, 
or generating specific reports could become a 
very useful government tool. gAi can also present 
high-quality visualisation outputs, which makes it 
easier to comprehend complex data from multiple 
sources. 
it would be important here for the decision-
makers to be competent when it comes to the 
natural language ‘Prompts’ that they may require 
to generate meaningful reports, which otherwise 
may require complex querying and analysis. gAi 
presents an opportunity to train manpower to 
use technology through english prompts. this 
may automate and reduce time drastically for 
many activities like preparing notes of meetings, 
creating abstracts of documents, creating emails, 
and many other language generation activities. it 
can significantly reduce the time spent developing 
documentation in simple, readable language. it 
would also be possible to correct the grammatical 
errors of formal documents very easily using gAi. 
Artificial i ntelligence, including generative 
Ai has already started transforming government 
operations, as evidenced by the following 
innovative applications:
 y the governments of both the united states 
and singapore have initiated the integration of 
chatgPt into their administrative systems. 
 y similarly, in Japan, the Yokosuka city 
government has begun employing chatgPt to 
support its office operations (Yang and Wang, 
2023).
 y the government of estonia has been piloting 
several Ai-related initiatives.
1
 For example, it has 
tested machine learning software to match job 
t able 1: emerging Generative ai technologies
t ype of tool nature of data o verview of outcome it produces
chatgPt , r eplika, Jasper, Youchat, sudowrite, 
c opy.ai, Writesonic
Mostly text c an provide answers to complex 
queries based on public 
information
DALL-e, DALL-e 2, google’s imagen, stable 
Diffusion, Make-A-s cene by Meta Ai, c raiyon, 
Midjourney and MiP-nerF 
text and 
images
Produces realistic photos based 
on text input
Amper Music, Aiva, Amadeus c ode,google’s 
Magenta, ecrett Music, Humtap, Boomy, 
Melodrive, Mubert & sony’s Flow Machines 
Music Produces music based on textual 
prompts
gitHub’s c oPilot, tabnine, Deepc ode, intellicode 
by Microsoft, r eplit’s ghostwriter, Ponicode, 
sourceAi, Ai21Labs’ studio and Amazon’s c ode 
Whisperer 
software 
programmes
generates lines of code based on 
text input
google LaMDA and Bard, Apple siri, Microsoft 
c ortana, samsung Bixby, iBM Watson Assistant, 
soundHound’s Hound, Mycroft, Amazon Alexa, 
and Facebook’s Wit.ai
Audio r esponds to audio prompts and 
generates actions like starting an 
application, playing music, etc.
28 February 2024
seekers with employers, 
developed a machine 
vision Ai solution for better 
traffic management, and 
piloted a programme 
under the Ministry of 
Justice to integrate gAi for 
processing judgements in 
small claims disputes where 
the payment amounts to a 
maximum of 7000 euros.
2
 
estonia has also introduced 
‘suve’, a digital assistant 
developed to offer precise 
and trustworthy answers 
to queries from the public.
3
 
 y in singapore, the smart nation initiative utilises 
Ai to optimise traffic management, improving 
urban planning and public transportation. 
s ome elements of service generation is used for 
recommending traffic flow and control through 
the use of mobile crowdsensing.
4
 
 y the us FeMA employs Ai for critical satellite 
imagery analysis to bolster disaster response 
and resource allocation.
5
 
 y the uK’s nHs leverages Ai to inform 
healthcare policies and manage resources 
effectively. Further nHs will soon deploy gAi 
on top of existing Ai tools for diagnosis and 
recommending possible treatments for critical 
illnesses, which require complex detections 
to be made quickly like heart disease and 
strokes. t his initiative of uK is funded by the 
government’s new Ai Diagnostic Fund.
6
 y in the us, a lot of Ai is already being used across 
government functions. the city of seattle has 
released its gAi Policy to signal opportunities 
as well as highlight possible concerns with 
strong guardrails to ensure gAi applications 
are used responsibly and accountability. the 
seven overarching goals are transparency and 
Accountability , explainability and interpretability , 
innovation and sustainability, Bias and Harm 
r eduction and Fairness, validity and r eliability, 
Privacy preservation, security and r esiliency.
7
 
Challenges for Governments
s everal studies have examined the ethical side 
of gAi, like chatgPt , in terms of how ethically it 
responds to specific issues. 
With these in mind, there 
are a few challenges that 
governments will also have to 
tackle if they are to harness the 
capabilities of gAi mindfully 
and safely (Yang and Wang, 
2023). 
one challenge of using 
gAi is the veracity of its 
outputs. the quality of 
the data it ingests plays a 
large role in the credibility 
of the outputs it prepares. 
Furthermore, the responses 
of gAi to factual prompts are 
relatively accurate. However, for prompts that 
require subjective deliberation, gAi applications 
often fail to provide satisfactory responses. While 
for factual queries, responses could have high 
reliability, deliberative queries may need greater 
specification of the context and extensive training 
of the models based on relevant datasets. 
similarly, the use of gAi requires organisations 
to expose their data to gAi systems. t his activity 
has to be done carefully so that the internal 
information assurance protocols and privacy of 
the data do not get breached. cases have been 
witnessed when the internal data of organisations 
was exposed during external queries because the 
data lakes and data warehouses were onboarded 
to gAi platforms. Privacy preservation protocols 
may also have to be developed before exposing 
the government’s data to these large language 
models. 
Like any other Ai technology, gAi systems 
need to establish how they can address the 
principles of FAte , namely Fairness, Accountability, 
transparency, and ethics in Ai. Addressing 
these FAte principles requires investment in 
the governance of these platforms. However, 
addressing these platforms has been seen to 
have significant positive impacts on stakeholder 
experiences when interacting with them (Malik et 
al., 2023).
For example, new Zealand’s government has 
adopted Ai tools extensively to analyse public 
feedback, ensuring citizen-centric policymaking. 
However, an advisory policy has also been 
Like any other AI 
technology, GAI systems 
need to establish how 
they can address the 
principles of FATE, 
namely Fairness, 
Accountability, 
Transparency, and 
Ethics in AI.
29 February 2024
developed for gAi applications, whereby the 
government has highlighted guidance for how 
gAi tools may be used without compromising 
governance. For example, an advisory surrounding 
the usage of gAi as shadow it is provided. Further, 
any information under the purview of the o fficial 
information Act should not be onboarded in gAi. 
An advisory is also provided on how government 
departments should avoid using genAi for 
business-critical information, systems, or public-
facing channels.
Further, the government needs to employ both 
automated and human surveillance mechanisms 
to protect against illegal content and misuse. 
Misinformation is increasingly becoming difficult 
to detect. Deepfakes, for instance, become very 
difficult to detect by a normal, untrained person 
ignorant of the nuances and capabilities of Ai. 
implications for practice and policy 
governments need to embrace Ai in general 
and gAi in particular in their activities. t his means 
governments need to sensitise their employees 
towards upskilling, where the employees 
understand how to act on data and how to 
leverage these gAi platforms for operational 
activities. Facilitating and creating the appropriate 
and supportive conditions required to empower 
public service employees to be embedded in an 
organisational learning environment whereby they 
are able to embrace Ai and other digital technologies 
in this journey towards digital transformation (Patre 
et al., 2023). one way to do that may be through 
undertaking capacity enhancement programmes 
in areas like Data science and Decision science 
where government employees may develop a 
better understanding of Ai in general and gAi 
as well. skill enhancement through exposure to 
prompt engineering would also be helpful to cater 
to this fast-evolving ecosystem. governments can 
partner with academia to upskill their employees to 
leverage Ai platforms and applications better.  
Conclusion
gAi, like other Ai tools, could play an important 
and critical role in the digital transformation of 
governments and public sector undertakings. 
this technology will help governments to be 
nimbler and more agile in their decision-making 
and connect with stakeholders more effectively. 
the public sector and governments can benefit 
immensely in productivity, efficiencies, and 
effectiveness through the adoption of gAi. While 
the benefits are immense, the journey needs to be 
planned carefully to avoid disruptions from adverse 
outcomes. ?
endnotes
1. https://automatingsociety.algorithmwatch.org/
report2020/estonia/
2. https://automatingsociety.algorithmwatch.org/
report2020/estonia/
3. https://eebot.ee/en/
4. https://www.smartnation.gov.sg/what-we-do/transport
5. https://www.planet.com/pulse/planet-and-new-light-
technologies-deliver-satellite-imagery-to-power-rapid-
disaster-response-at-fema/
6. https://www.gov.uk/government/news/21-million-to-
roll-out-artificial-intelligence-across-the-nhs
7. https://harrell.seattle.gov/2023/11/03/city-of-seattle-
releases-generative-artificial-intelligence-policy-defining-
responsible-use-for-city-employees/
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