By Sachethan Shankara Narayana

Abstract:
Rapid innovation in artificial intelligence, especially generative artificial intelligence, is
disrupting the shape and ethics of work systems in the global economy. This researching journal
explores the job and employment effect caused by artificial intelligence in a blend of qualitative
and case study approaches to estimate the impact of artificial intelligence on employment across
varying countries and industries. This resonance provides evidence about the variable adoption
and effect, where productive economies such as the US achieve productivity and wage
differentials in the job market for artificial intelligence professionals, whereas developing nations
are marked by hindrances in infrastructure, skills, and governmental policies, leading to potential
global and internal inequalities. Apart from the impact on the global job market, this journal
discusses internal threatening ethics such as disemployment, biased algorithms, data security,
and global disparities in the accessibility of artificial intelligence innovation. This journal
emphasizes the need for combined governmental, educational, and organizational support to
make labor systems adaptive, educational innovation systems adaptive, and internal artificial
intelligence ethics fair. This researching journal provides a balanced insight to the positive global
impact of artificial intelligence to initiate healthy global transformations in the global workforce.

Keywords:
Artificial Intelligence; Employment; Global Labor Markets; Economic

Introduction:
Artificial Intelligence (AI) is swiftly changing worldwide employment markets while
concurrently presenting significant ethical issues. Its incorporation across industries like
manufacturing, healthcare, finance, and education has boosted productivity, refined decision
making, and led to the creation of new job categories. Concurrently, these advancements have
brought forth considerable challenges, such as job loss, skill disparity, and increasing inequalities
between highly skilled and low-skilled workers in various areas.
In addition to changes related to employment, AI has further amplified ethical issues within
workplace systems. Concerns regarding algorithmic bias, insufficient transparency, data privacy
threats, and diminished human control in decision-making have sparked debates over justice and
responsibility. These obstacles emphasize the necessity for governance structures that harmonize
technological advancement with ethical responsibility and the welfare of society. In the absence
of sufficient policy measures, AI-powered systems may exacerbate current inequalities and erode
confidence in institutions.
As a result of these changes, there is an increasing demand for collaborative actions among
governments, industries, and educational organizations. Investing in reskilling and upskilling
programs, creating inclusive AI frameworks, and implementing strong regulatory measures are
crucial for distributing the advantages of AI fairly.
This research specifically investigates how Artificial Intelligence is altering job patterns and
creating ethical issues in workplace settings. Although wider concerns like inequality and
governance are recognized, the primary focus continues to be on comprehending employment
changes and related ethical challenges in AI-integrated systems.

Research Questions:
This qualitative research is designed to examine the global implications of Artificial Intelligence
on employment and ethical practices. The research is guided by the following questions:
1. In what ways is Artificial Intelligence changing employment trends across industries and
economies?
2. How much does AI influence job displacement and job creation?
3. What fresh skills and abilities are needed in a labor market influenced by AI?
4. What are the main ethical issues (bias, privacy, accountability) linked to AI in making
decisions at work?
5. To what extent are existing policies and institutional actions effective in tackling
employment and ethical issues driven by AI?

Review of Literature:
Artificial Intelligence (AI) has risen as a groundbreaking influence in worldwide job markets,
affecting employment frameworks, skill needs, and ethical oversight. Current research highlights
that changes driven by AI are not solely technological but are influenced by institutional,
economic, and socio-political contexts (Yihang Liang, 2024; Danstan Akwiri, 2024). The literature
can be generally divided into three themes: restructuring of the labor market, global inequalities
in AI implementation, and ethical issues in workplaces that incorporate AI.

AI and Labour Market Restructuring
A substantial amount of research explores how AI affects jobs via automation and improvements
in productivity. Yihang Liang (2024) contends that AI could profoundly change job structures by
automating routine tasks and concurrently increasing the need for high-skilled positions. Likewise,
the World Economic Forum (2025) indicates that AI will both eliminate and generate jobs, leading
to a net change instead of merely a fall in employment.
Nonetheless, this dual-impact story is frequently conveyed in a straightforward way, presuming
that job generation will inherently balance out displacement. This assumption has been
challenged for ignoring structural obstacles like unequal access to education and opportunities
for reskilling. K. Pandey (2025) highlights that in the absence of proactive institutional intervention,
the adoption of AI could exacerbate inequalities in the labor market rather than alleviate them.
Additionally, although research recognizes productivity increases, it frequently does not
thoroughly evaluate the distribution of these gains. McKinsey & Company (2024) emphasizes
enhancements in efficiency via AI but offers scant analysis on the lasting effects on wage disparity
and employment stability. This indicates that a significant portion of the literature is concentrated
on macro-level results while not adequately addressing worker-level effects.

Uneven Global Adoption and Structural Inequalities
A different significant area of literature explores inequalities in AI implementation between nations.
Bali Sakshi (2023) notes that in India, the adoption of AI is hindered by infrastructural deficiencies
and skill shortages, limiting its inclusive capabilities. Likewise, Kwemoi Chelule Kabiga (2024)
observes that emerging economies frequently do not possess the institutional capability
necessary for efficient AI governance.
Although these studies effectively pinpoint structural issues, they frequently regard developing
nations as uniform entities. This generalization ignores internal disparities like urban-rural divides
and sectoral differences, reducing the explanatory depth of these analyses.
A. Hagerty and I. Rubinov (2019) contend that AI governance frameworks are largely influenced
by developed nations, which may exacerbate global disparities. Nonetheless, their research is
primarily theoretical, showing minimal empirical proof connecting global governance frameworks
to local labor market results.
Consequently, although the literature acknowledges worldwide inequalities, it frequently lacks
detailed, context-oriented examination, especially for developing nations.

Ethical Concerns and Governance of AI in Employment
An increasing amount of studies examines the ethical consequences of AI in work settings. Lizzie
Short (2025) emphasizes significant issues like algorithmic bias, insufficient transparency, and
threats to data privacy, contending that AI systems commonly reinforce existing social inequalities
present in training data.
Likewise, Danstan Akwiri (2024) highlights that substituting human judgment with automated
systems brings significant issues regarding accountability and equity. These studies emphasize
the importance of more robust regulatory frameworks instead of depending on voluntary ethical
guidelines.
Nonetheless, a significant portion of this literature is still prescriptive, emphasizing what ought to
be done instead of exploring how ethical principles are applied in reality. For example, although
frameworks like the EU AI Act are often referenced, there is a lack of empirical assessment of
their efficacy in practical organizational environments.
Additionally, the legal research conducted by Paul De Hert and Vagelis Papakonstantinou (2021)
emphasizes the shortcomings of current data protection frameworks in tackling AI-related risks.
Their research indicates that existing frameworks are more reactive than proactive, failing to keep
up with swift technological progress.

Synthesis and Research Gap
Overall, the research shows that the effect of AI on jobs is intricate and varies by context.
Nonetheless, three significant constraints arise.
Initially, current research frequently investigates employment, inequality, and ethics separately
instead of viewing them as related issues. Secondly, there is a significant dependence on macro
level analysis, with little focus on personal perceptions and lived experiences. Third, a significant
portion of the literature tends to be either excessively descriptive or prescriptive, lacking empirical
connection between theory and practice.
To tackle these gaps, the current research employs a survey-centered, perception-focused
methodology. By connecting employee viewpoints with wider structural and ethical issues, the
research seeks to offer a more concrete insight into how AI is transforming jobs and how these
transformations are perceived on a personal level.

Methodology:
This study avails a mixed method approach with a strong qualitative emphasis to explore the
worldwide effects of artificial intelligence, unemployment patterns, and ethical practices. The
approach brings together original data from an online survey with in-depth qualitative review of
existing literature and this combined strategy suits the topic particularly well since Artificial
Intelligence‘s influence on labour markets extends beyond quantifiable job shifts to incorporate
individual perceptions, ethical dilemmas and institutional responses that call for careful
interpretative work.
Primary data has been collected from a structured questionnaire built and distributed via the online
platform of Google forms and the instrument relied mainly upon is close ended and multiple choice
items accompanied by a few open oriented questions to gauge participants views on job
displacement and creation, the growing importance of certain skills in an AI shaped economy,
ethical risks tied to AI deployment and the readiness of government and organisations to address
these changes. The questions were crafted to align closely with the study’s core research objectives
and drew on prominent themes from articles and policy work in this area with particular care being
taken to make the wording clear, neutral and approachable, allowing respondents from diverse
educational and professional backgrounds to respond thoughtfully.
The survey link was shared across various online channels and other social media platforms with
this distribution strategy allowing access to a broad geographically dispersed set of participants
despite constraints on time and resources. In total 28 valid responses were gathered and this group
included a mix of ages, genders and occupations. Even though the sample is relatively small it tries
to provide valuable explanatory perspectives on how people across various sectors view AI related
employment and ethical issues.
In an effort to complement the survey the analysis draws on qualitative examination of secondary
materials, including peer reviewed articles, research papers, policy reports, and publications from
relevant institutions and these sources were chosen to capture a wide spectrum of viewpoints on
AI driven changes in labour markets, global disparities and ethical oversight. By positioning the
survey results within this broad literature, the study tries to connect personal perceptions to larger
theoretical and policy discussions.
The analysis proceeds in two main phases: initially survey data underwent descriptive review to
cover prominent patterns concerning job insecurity, evolving skill needs, ethical worries and
institutional preparedness with simple percentages, helping to summarise responses and spotlight
areas of agreement or difference among participants. In the secondary phase a more interpretative,
qualitative approach integrated these findings with key points from the literature revealing areas
where respondents views reinforced or departed from established academic accounts of AI labour
impacts.

Survey Results:
Descriptive Summary
The primary survey received a total of 28 responses from participants across different age groups,
countries, and occupational backgrounds. The majority of respondents belonged to the 25–34 age
group, followed by respondents aged 18–24, indicating strong participation from younger
individuals likely to be directly impacted by AI-driven workplace changes. In terms of gender
distribution, 71.4% of respondents identified as male, while 28.6% identified as female, ensuring
representation across genders.
Respondents were geographically diverse, with participants from India, the United States, France,
and Nigeria, reflecting a global perspective on AI’s impact. Occupational backgrounds included
students, managers, product managers, civil servants, self-employed professionals, and individuals
working in technical and analytical roles, providing insights from multiple sectors of the global
workforce.
When asked about the negative effects of AI, respondents identified job displacement (37%) as the
most significant concern, followed by data privacy & cybersecurity (29.6%), misinformation and
reputation damage (25.9%), and smaller proportions noted reduced originality / free thinking
(3.7%) and reliability of AI outcomes (3.7%). This indicates that employment security and ethical
risks are prominent issues associated with AI adoption.
Regarding the positive impact of AI on organizations, respondents highlighted automation (51.9%)
as the leading benefit, followed by efficient customer service / availability (25.9%), personalized
content (18.5%), and faster work completion (3.7%), suggesting that AI is largely viewed as a tool
for improving efficiency and productivity.
In terms of employment outcomes, 70.4% of respondents expected AI to eliminate certain job
roles, while 48.1% believed it would create new opportunities, increase productivity, and require
new skills. Additionally, 51.9% anticipated lower wages for routine jobs, whereas 48.1% expected
higher wages for tech-skilled workers, indicating concerns about unequal wage distribution.

Cross-Sectoral Patterns
Across different sectors, respondents expressed significant concern about ethical issues related to
AI. The most frequently cited challenge was decision-making without human judgment (59.3%),
followed by bias and discrimination (48.1%), job displacement (44.4%), data privacy (40.7%), and
lack of transparency (37%), highlighting ethical vulnerabilities across industries. Lesser concerns
included surveillance (22.2%) and dependency on AI tools (3.7%).
When asked about skill relevance in an AI-driven economy, 44.4% believed their skills would
definitely remain relevant, 37% felt their skills would remain relevant to some extent, and 7.4%
were unsure, suggesting cautious optimism alongside recognition of the need for upskilling.
Regarding government preparedness, only 3.7% of respondents believed governments are very
prepared, 11.1% felt somewhat prepared, 25.9% were neutral, 18.5% considered them slightly
unprepared, and 40.7% felt governments are not prepared at all, indicating skepticism about
institutional readiness.
Opinions on whether AI’s benefits outweigh its risks were divided: 33.3% agreed, 25.9% were
neutral, and 29.6% disagreed, reflecting mixed perceptions across sectors. Additionally, 44.4% of
respondents agreed that AI could widen economic inequality, while 18.5% disagreed. Regarding
responsibility for addressing AI-related ethical issues, respondents indicated technology
companies (33.3%), governments (22.2%), civil society/academia (14.8%), international
organizations (3.7%), and shared responsibility (25.9%), emphasizing the need for collaborative
governance.
Finally, on supporting displaced workers, 25.9% strongly agreed that retraining or social security
should be provided, while 18.5% were neutral and 14.8% disagreed. Specific ethical concerns were
highlighted, with 48.1% agreeing that AI systems can be biased or unfair, and 59.3% agreeing that
AI threatens individual privacy.
Overall, the survey results highlight both the opportunities and challenges associated with AI
integration, particularly in terms of employment restructuring, skill requirements, ethical
governance, and equitable distribution of benefits across the global workforce.

Global Impact:
The international economy is undergoing a rapid transformation due to the rapid implementation
of Artificial Intelligence (AI) technologies, especially those based on Generative AI (GAI) into
the workplace. However, while many people’s lives will improve as a result of AI and GAI
technology, its effects on work, work ethic and employment are vastly unequal between regions
of the world. Developed nations are well positioned to take advantage of the productivity
benefits, new forms of work and AI regulation, while developing nations lack basic digital
infrastructure, access to the internet and adequate laws regulating the use of AI. As a result of the
disparity in AI adoption between developed and developing nations, the gap between rich and
poor will continue to increase between nations, and even within nations.
The widespread use of AI in the US is also evident in many sectors including finance, software
development, healthcare, logistics, etc. The US is predicted to invest approximately $109 billion
USD in private sector AI investments in 2023 and has become the world’s leader in developing
GAI technology. According to research, approximately 60% of the workforce in the US will be
affected by the transformation of AI, with a mix of positive and negative effects. For example,
even though AI has the ability to create additional productivity capacity through augmentation, it
has been shown that consultants who utilized AI-based tools to complete work on projects were
able to perform tasks 20-25% more quickly than those who did not use AI-based tools. On the
other hand, the increasing rate of job polarization has begun to develop.
On the contrary, job polarisation is occurring – primarily, young people working in software
development or technical positions that involve AI are seeing fewer jobs available than they
previously had because their skill sets have become outmoded due to technological
advancements or higher levels of competition. In the United States, ethical issues surrounding
the AI field are increasingly involving algorithmic discrimination in hiring new employees, the
use of surveillance tools, and the application of AI within the criminal justice system. These
concerns have led to an increase in the amount of legislative activity and regulatory oversight of
the AI field being called for by civil rights organisations and others.
Germany is following a more regulation-oriented model, operating under the oversight of the
European Union as stated in the EU AI Act, which creates safeguards for the use of high-risk AI
(e.g., tools used for biometric identification, recruitment, and algorithm-driven decisions). 50%
of the largest German corporations use AI throughout their industries, most significantly in the
manufacturing and automotive sectors. By training workers through specialised vocational
education, Germany has focused on providing educational reskilling for those who will be
working with AI-operated technologies. Lastly, Germany’s corporate governance structure also
includes provisions for ethical considerations and enforces transparency and auditing of
algorithms through established law. Given these factors, while both countries have similar
amounts of exposure to AI, Germany has actively developed rules for the ethical use of these
technologies.
India’s future with Artificial Intelligence (AI) appears quite attractive; however, it has a
considerable number of infrastructure barriers to overcome. The National Strategy for AI
outlines the Indian government’s plan that AI will be a key driver for inclusive growth under the
slogan “AI for All”. AI Applications have already gained traction in many sectors in India,
especially health tech, education, financial services and a few others. Unfortunately, as of 2023,
approximately 60% of the population still does not have access to the internet. The Urban-Rural
divide in India is significant as over 80% of the people living in urban areas now have access to
the internet, while only 35-40% in rural regions do. (Sources: World Bank, Internet World Stats).
There is also an estimated 27% of employment opportunities in India that will be eliminated or
transformed into a new service job category by the introduction of AI technology (mostly in the
service and retail industries). Some Digital Skills initiatives exist currently, however, only 48%
of youth in India have basic digital skills in their communities. An additional concern in India is
the absence of a National Data Protection Law. India faces many challenges in the domains of
data privacy, data surveillance, and algorithmic bias, especially with the use of sensitive systems
such as Aadhar NFT and the Government Digital Welfare Delivery Systems.
Brazil is a middle-income country with moderate but increasing adoption levels for AI
technologies. Brazil has implemented AI in several industries such as agriculture, finance and
public health, however, access to AI varies considerably by region. The majority of users are
located in urban centres and elite institutions that have a greater ability to invest in cutting-edge
tools, technologies, and training; therefore, the first groups using and adopting AI will be urban
based higher-income individuals and organizations. Brazil has established ethical principles
regarding Fairness and non-discrimination for their AI Strategy; however, there is a lack of
strong mechanisms to enforce these principles at this time. Afrobrazilian and Indigenous
communities do not represent adequately in the data sets used by AI systems; thereby creating
algorithmic exclusion.
The dangers of adopting AI technology without modifications for compatibility with local
demographics and social environments are illustrated by the situation in low-income nations like
Nigeria, where only thirty-six percent of Nigerians used the Internet as of 2023; furthermore,
most of Nigeria’s rural communities do not have access to digital resources necessary for
integration within the AI economy. Projects using AI operate primarily as standalone “proof-of
concept” applications (typically funded by philanthropic organizations) for healthcare diagnosis,
agricultural development, and educational support. Job loss has not yet been adversely affected;
though, there is speculation regarding potential significant long-term job loss among workers in
urban service and manufacturing industries due to displacement from the use of artificial
intelligence. Nigeria does not maintain a national policy regarding artificial intelligence and does
not have laws governing data protection, nor does it have any type of regulatory body that
establishes or regulates algorithmic transparency. The country is lacking in ethical governance
preparedness and institutional capability and currently ranks among the lowest worldwide.
There is a recurring trend evident in all of these case studies; the adoption of and the effects of
artificial intelligence are inconsistent, and without a coordinated approach to governance and the
building of institutional capacities, the existing inequities that are a product of globalization will
continue to grow. Nations with an existing digital information infrastructure and an adequate
supply of skilled labour and established regulatory frameworks for business operations (e.g., the
United States and Germany) are currently receiving the majority of the financial benefits
associated with the development of artificial intelligence while emerging economies such as
India and Brazil are likely to encounter obstacles to further development due to their respective
challenges in developing educational systems and increasing individual economic equity.
Finally, low-income countries are at risk for being completely excluded from the ongoing
development of the global economy. Shared concerns exist in the areas of ethical considerations,
including algorithmic bias, privacy concerns and/or exclusion of particular populations within
AI-generated models; responses to these concerns vary widely by Country and community,
depending on the level of congressional action, public outcry and the maturity of legislation.
Creating general international guidance and creating appropriate legal and regulatory
mechanisms for guaranteeing compliance requires prolonged global dialogues between interested
Stakeholders and careful decision making within each Country.
As noted previously, the global impact of AI is not a technological issue alone, but also a
developmental and ethical issue. As generative AI becomes an increasing driving force for a
restructuring of the nature or style of work, it will be critical to take proactive steps to ensure
broad The benefits of AI will also be distributed equitably, while minimising the potential for
increasing existing inequalities within and between Countries.

Discussion:
The results show that growth of AI technologies has both negative and positive effects, especially focusing on employment and ethical concerns, which has been significantly studied by researchers over the last few years. What this paper focuses on is the concern of people studied through survey results across age groups, genders, developed and developing countries and occupational backgrounds. Our survey results and findings clearly indicate that out of 28 responses, higher percentage of the participants show serious concerns regarding job displacement considering Automation which on one side benefits companies/organisations improve efficiency, while on the other side expresses a fear, as per the survey, 70.4%, about the elimination of certain job roles.

According to the survey as discussed in the results, 33.3% claimed that the technology companies should be primarily responsible for ethical problems caused by AI along with some shared responsibilities by stakeholders, the need to do so rises from the concern of AI making biased decisions, also claimed by over 48% of the participants as well as threat to privacy claimed by over 70% of the participants. Over 40% are concerned about the growing inequalities which underlines the rising concerns of people on how the governments will handle the declining economic situation of the working population and thus over 60% agree that the displaced working class should get support from social security schemes. Notwithstanding the distress, a percentage of the participants are hopeful about the benefits outweighing its risks, with the current skills that
will remain relevant in an AI driven economy as per the 44.4%.

The recent surge in AI and automation has drastically affected the various industries with AI both revolutionizing and giving rise to new forms of labour, allowing machines to assume increasingly complex jobs previously performed by humans. Along with the development it also delves into the issues of morality, and how companies walk tightrope between innovation and ethical accountability. Customers reap benefits, shareholders reap the award, but employees and working class tend to bear the cost (Yogalakshmi & Maruthavijayan, IJAR, 2025). The research survey conducted benefited in ways, to not provide superficial interpretations but make detailed analysis depending on what people across borders are practically concerned about. Although there has been certain limitations to our research study, firstly, the survey conducted was through online medium shared across various social media platforms, which segregated participants to only those who have access to digital media. Having mentioned the limitation, adopting this method of survey helped us reach people miles away and take into account a broader perception. Secondly, due to the constraints of time, not much time was invested in waiting for survey responses thus getting to only 28 of them spread across countries (India, Nigeria, France, USA). Although, those 28 responses were observed with previous research studies in details to maintain objectivity and inclusivity. While mentioning this, heterogeneity in survey methods including ground surveys or dyadic communication methods would have reduced the chances of research gaps. Notwithstanding these limitations, the survey-based research includes valuable insights from
published, well researched articles and papers, referred as secondary sources.

The growth of AI and the concerns with job security took a huge toll on the majority of the population during COVID-19 pandemic, leading to the present condition of a decreasing demand for working classes, affecting the middle class. This technological shift has extended its influence to specific administrative and clerical roles (Yihang Liang, 2024). This ties on to the occupational backgrounds of the survey participants who expressed concerns regarding the same. 25-34 years of age constitutes 42.9% who participated in the survey and 18-24 years old as 35.7%, indicating the majority of the working population. The occupational backgrounds participated were dominated by students, civil servants and advocates; engineers, product managers, self-employed
showing a low participation, thus the primary concern of job displacement revolves around this group. A concern for lower wages for routine jobs and increasing wages for tech skilled people, highlights the growing inequalities affecting wage workers. Estimates suggest that AI adoption could potentially automate up to one billion jobs globally, millions of jobs becoming obsolete in the coming decade. The emergence of these high paying positions will widen the income gap (Yihang Liang, 2024). 40.7% consider that governments are currently not at all prepared for the rising tide of deprivation of lives.

The key strategies include – enacting governmental policies for AI research and development, corporate responsibility includes challenging organizations to
implement AI applications by providing affordable reskilling and upskilling platforms for workers displaced in the job market (Danstan Akwiri, 2024). AI proliferation on employment security and ethics has mixed conclusions. 33.3% agrees, as per the survey, as opposed to 29.6% disagreeing to it, that benefits of AI outweigh it’s risks. The
positive scenario could elevate labour productivity, including demand shifts within sectors, prompting workforce reallocation. High skilled positions like prompt engineering, data science will increase wages for high skilled professionals. It is important that governments take appropriate measures to provide relevant basic education and skill-based programs to all to adapt to technological changes and ensure fair compensation and social welfare. Digital literacy and AI related competencies are paramount (Yihang Liang, 2024). The Future of Jobs Report 2025, World Economic Forum, stated that ‘broadening digital access is expected to be the most transformative
trend, both across technology related jobs and overall, with 60% employers expecting it to transform their business by 2030. Advancements in AI and information processing 86%, automation and robotics 58%, energy generation storage and distribution 41%, are expected to be transformative.’ These trends are expected to have a diverse impact on employment, initiating fastest growing and also fastest declining roles, differing across economies, and a need for mitigating cybersecurity risks will rise. While discussing cybersecurity risks, privacy of individuals and organisations gets hampered the
most. According to the survey, 59.3% agree and 18.5% strongly agree if the AI intruding into lives and hampering privacy is a growing concern or not. Privacy is one of the main ethical concerns under the growth of AI. From deepfakes to digital arrest to the rising concerns against the surveillance system, as George Orwell in 1984 made a remark about Big Brother, somehow ruining lives got accompanied with development. According to UNESCO, the rapid changes raise ethical concerns. Problems arising from potential AI systems with biases, mostly created by developed countries, raise questions about western biased perspectives, contributing to existing inequalities, resulting in further harm to marginalised groups suffering from poverty and are thus, also digitally
unskilled. AI creates avenues to gain access to company’s sensitive information potentially exposing business to litigation (Lizzie Short, Harvard Division of Continuing Education). Thus, the academic survey on Global Impact of Artificial intelligence on Employment and Ethics, takes into account the concerns of people and pledges the authorities, governments, technological companies and conglomerates, to raise awareness about the growing concerns of AI and providing benefits to lower income groups, reducing the exploitation of developing and underdeveloped countries for technological innovation and protect individual lives.

Recommendations:
Studying the impact of AI on workplaces, many researchers have outlined the basic concerns whose resolution require an integrated approach of government regulation, corporate governance and labour protection. Pandey (2025) proposes three broad categories of concerns to be tackled: algorithmic bias, privacy and surveillance, and job displacement. When looking at possible solutions, it is necessary to keep in mind that any potential solution must guarantee transparency and accountability in an AI-enabled workplace.
1. Basic framework for Workplace AI Adoption Regulation policy: The survey results clearly indicate that à large portion of respondents fear job displacement/loss, and expect that certain jobs will be eliminated by AI. In the face of this apprehension, it is recommended for AI-adoption to be preceded by dialogue with worker unions or employee unions, to ensure that employees can be protected and prepared in the scenario of à transition. The EU AI Act, the world’s first comprehensive AI law (Walter, 2024), uses à risk classification system to classify AI systems by risk level. Adoption of this system by firms is another recommendation, in light of the survey result wherein the challenge that most
respondents faced was AI decision making without human judgement. The system
classifies AI technology into Unacceptable Risk, High Risk, Limited Risk and Minimal
Risk . Using this system, firms can avoid adoption of harmful and disruptive AI technology,
especially in areas which require human diligence, like hiring, firing, scheduling,
performance evaluation and wage setting.
Pandey (2025) further finds that 64% of employees enrolled in reskilling programs
expressed confidence in their ability to transition to new roles. Hence, it is recommended
that workplaces introduce employer-funded reskilling opportunities especially in fields
where introduction of AI significantly alters job expectations, to enable employees to
remain capable in the face of changing technology and tools. AI is also expected to create
many new roles, in à variety of fields such as AI Security Analyst (Cybersecurity), AI
Recruitment Specialist (HR), AI Content Strategist (Marketing), AI Risk Management
Analyst (Finance) as outlined in Niketa; Priyal Sharma; Ritika; Samrity (2025). These
emerging opportunities provide à guide for employers in terms of creating reskilling
opportunities for employees.
2. Transparency and Accountability recommendations:
The survey results clearly demonstrate that employees have significant concerns of data
privacy, cybersecurity and surveillance. Ethical risks are products of opaque processes that
do not disclose to stakeholders the inputs and thus, create gaps in awareness and pose risks
to them. It is necessary to create open and accountable processes that develop AI
integration in the workplace; one way this can be done is to make standard procedure the
disclosure of usage of AI in workplace hiring, monitoring and evaluation; this corresponds
to the EU’s classification of high-risk AI, ensuring that employees, applicants and management are all informed of AI usage.
Simultaneously, human oversight is also necessary even with the presence of AI systems.
This may take the shape of a ‘right to review’ wherein human review is applied to contested
decisions, and also for record-keeping of à model’s logic and data sources to ensure
continued fairness and explainability.
Further keeping with the mandate of transparency, it is recommended that workplaces
conduct an independent assessment pre-deployment of AI, assessing possible job
displacement, discrimination risks, ethical risks, and data-protection compliance, and
necessarily disclose the results of their assessment to employees. In this regard,
Singapore’s AI legislation can be taken as an example: Model AI Governance Framework
‘offers detailed guidance for the responsible deployment of AI technologies, emphasizing
principles like fairness, transparency, and explainability’ (Walter, 2024)
Data protection must be à priority in workplaces and stringent data-protection laws
enshrined by the state are necessary in this regard. Lawmakers can look to the
comprehensive AI-related legislation of countries like Brazil, Singapore (Personal Data
Protection Act (PDPA)) and the EU (General Data Protection Regulation (GDPR))
3. Future Scope for policy
Given the evolving nature of AI technologies, application and implementation, it is clear
that agile, adaptive and coordination policy is the need of the hour, while also ensuring that
workers’ rights remain in central purview. Sunset reviews are à useful tool in this context,
applied for periodic re-evaluation of AI-related labour and cybersecurity laws.
In the future, there will be à need for global co-operation for AI governance, especially for
the creation of basic standards in terms of non-discrimination, transparency and human
rights. In recognition of the varied globalscape, and the specific contexts of each country,
states must also develop relevant protections. In India, the government faces the challenge
of à complex AI landscape where regulation is constrained by social concerns like the caste
system, and economic diversity (such as large numbers of informal workers).

Limitations:
This study was affected by certain limitations that may restrict its applicability.
Firstly, the study uses à self-administered survey, wherein respondents may develop various
biases. Moreover, the survey was directed at employees in STEM fields which reduces the
generalizability of the outcomes to other fields. The sample size of the survey was also limited, at
28, which means that results may not be fully representative of the population, restricts statistical
robustness and underrepresents informal workers.
Another limitation was the lack of detailed research on the function of AI models, LLM models,
generative AI and more. This constrains the understanding of AI models and the opportunities and
risks they actually pose.
The medium of the survey was online which means that only digitally-literate, relatively educated
participants were reached, meaning that concerns of those vulnerable to digital exclusion were
excluded.
Since the survey reports on the perceived fears of employees in relation to AI, namely job
displacement, hiring bias etc., there is no actual objective and technical metric to capture actual
impact of AI.
Conclusion:
In conclusion, Artificial Intelligence (AI) is changing the job market worldwide and raising
important ethical issues that we need to address together. AI’s integration into different sectors,
like manufacturing, healthcare, finance, and education, shows a clear potential to improve
productivity, streamline decision-making, and create new job categories. However, this
technological shift also brings significant challenges. These include the loss of routine and some
skilled jobs, increased inequality between high- and low-skilled workers, and uneven economic
results across regions.
The ethical issues surrounding AI go beyond disruptions in the job market. Key concerns include
bias in algorithms, data privacy, accountability for decisions made by AI, and the complexity of
AI systems. These problems highlight the need for strong governance that balances progress with
human rights, fairness, and the well-being of society. Without proactive policies, AI might worsen
existing social inequalities and erode trust in institutions.
To reduce negative effects while maximizing benefits, we need a comprehensive approach. This
should involve investing in retraining and lifelong learning programs, creating social safety nets
for workers affected by these changes, and fostering international cooperation on ethical standards
and regulations. Businesses should implement inclusive design and ethical AI practices.
Governments need to ensure fair access to the opportunities arising from AI advancements.
Ultimately, AI’s impact on jobs and ethics will not depend solely on technology. It will be shaped
by the choices of policymakers, industry leaders, and society as a whole. Taking on this
responsibility is crucial for building an inclusive and sustainable future, where AI contributes to
broad economic growth, empowers people, and promotes ethical progress.

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