India’s fragmented landscape of AI regulation and governance and the work and employment scenario
Suggested citation: Roy, C., Das Sarma, K. (2025). ‘India’s fragmented landscape of AI regulation and governance and the work and employment scenario’, Artificial Intelligence Policy Observatory for the World of Work (AIPOWW) Symposium, Global Political Economy, Vol. 4 Issue 2. (September 2025).
Introduction
As the value of AI (Artificial Intelligence) is being unlocked, its use is increasing globally across all sectors of economies, resulting in a diverse range of applications affecting not only businesses and industries but also all areas of social and economic life. While the scope of AI technologies is expanding, it is giving rise to pressing questions on the risks associated with AI and how that will be managed, particularly in terms of privacy, data protection, anti-discrimination, protection of worker rights and voice, liability, product and data safety, to name a few. There is a growing body of scholarly research now drawing attention to extreme AI risks in wake of the rapid growth of autonomous AI systems. From predicting “large-scale social harms, malicious uses, and an irreversible loss of human control” (Bengio et al., 2024, p. 842), to possible threats to human health and human existence (Federspiel et al., 2023) and psychological impacts on human psyche (Sebestyen, 2025) the risks predicted are varied and wide ranging. Cyber security threats and attacks (Jada and Mayayise, 2024), labour market disruption, and economic power inequalities (International AI Safety Report, 2025), and potential harm to fundamental rights (Kusche, 2024) are also discussed widely. As a result, regulation and governance of AI has gained significant global attention in the recent years. The need for effective regulation and advancing effective governance approaches to inform the development of regulatory practices is becoming more crucial than ever and is a key consideration among policymakers worldwide.
The current regulatory landscape is evolving. Governments and industries worldwide are responding to AI risks through a combination of AI-related standards activities, regulatory initiatives, cross-jurisdictional collaboration, AI safety research and ethical guidelines (AI Action Summit, Paris, 2025). However, approaches to AI regulation and governance have differed across jurisdictions – for example, while EU is pursuing a centralized approach to AI regulation, the United States is pursuing a decentralized regulatory framework and the UK following a pro-innovation approach. Approaches to AI regulation in much of Asia is diverse and light touch with not many enforcing laws and regulations except China that introduced mandatory technology-specific regulations and measures to address AI associated risks.
As part of the Artificial Intelligence Observatory for the World of Work (AIPOWW) Symposium for Global Political Economy, this article contributes to global debates on AI governance and regulation by analysing the current scenario in India. Among South Asian countries, India is considered as one of the fastest growing economies and a hub for technology and innovation that has taken a lead in promoting and regulating AI (Chakrabarti and Sanyal, 2020). India is slotted to host the next global AI Summit and this year’s summit held in Paris, saw Indian Prime Minister, Narendra Modi co-chairing the summit. In his opening address, the Prime Minister noted that AI is reshaping polity, economy, security and society, and acknowledged the need to address associated risks and build trust while also emphasising the need to promote innovation for harnessing AI for both economic prosperity and social inclusion (Ministry of External Affairs, 2025). India’s Electronics and IT Minister Ashwini Vaishnaw recently (dated 21 May 2025) stated that India aims to pursue a “techno-legal approach to regulate AI” (News On AIR, 2025) meaning embedding legal compliance within technological systems. However, the legal frameworks around AI are viewed as nascent and developing. At the time of writing this commentary piece, India does not have a dedicated law exclusively for AI governance. Currently, there are various policies, guidelines and frameworks that are all aimed to develop and deploy AI technologies responsibly but there is lack of a cohesive and collective vision to effectively achieve this.
It is important to note that India has a large population and employment is predominantly in the informal sector characterised mostly by self-employment and casual employment. Harnessing AI for inclusive development focusing on youth employment, skills development and the labour market more broadly (Hammer and Karmakar, 2021; ILO India Employment Report, 2024) will be of key significance in the development of India’s AI eco system and this commentary piece aims to underscore this.
A publication (Mohanty and Sahu, 2024) produced under Carnegie India’s Technology and Society Program notes that the body of literature available in connection to AI regulation in India is “disconnected, narrow, or superficial”. In the subsequent segments of this article, we first present factual information about India’s AI regulation approaches, development programs, and governance mechanisms, followed by a brief discussion of the current sentiment prevailing around AI adoption in India. We then delve deeper to discuss impact of AI adoption on labour market status, and work and employment situation in India. In doing so, we bring to light the disconnected storyline that currently exists characterised by the evolving but fragmented AI regulation and governance landscape, the euphoric sentiment about AI adoption and a vulnerable work and employment scenario exacerbated by the impact of AI.
Regulatory landscape of AI in India
India’s foundational cyber law, the Information Technology Act of 2000, regulates electronic transactions and digital security, establishing a “safe harbour” for intermediaries alongside data protection standards (Government of India, 2009). Yet, its failure to tackle AI governance has led to proposals for new legislation (Times of India [TOI], 2023). In 2022, Ministry of Electronics and IT (MeitY) proposed the India Data Accessibility and Use Policy to monetise anonymised public sector data, but it was shelved due to privacy concerns (MeitY, 2022a). It was replaced by the Draft National Data Governance Framework Policy (NDGFP), focusing on voluntary data sharing through a new India Data Management Office (MeitY, 2022b). Enacted in 2023, the Digital Personal Data Protection Act (DPDP Act) is India’s first comprehensive privacy law. It establishes a framework for personal data processing emphasising consent, transparency, and data minimisation. It requires data fiduciaries to secure personal data, with stricter rules for “Significant Data Fiduciaries.” The Data Protection Board of India enforces compliance, with penalties up to ₹250 crore ( 30 million) (Burman, 2023). To modernise tech governance and replace the aging IT Act, India unveiled the Digital India Act (DIA), an “omnibus” law addressing user rights, online safety, competition, and technologies like artificial intelligence (Tata Consultancy Services, 2023). It covers AI regulation, online safety, and re-evaluates intermediary liability in the age of deepfakes and generative AI (TOI, 2023). As of 2025, the Digital India Act is still in public consultation.
The National Programme on AI (IndiaAI Mission), approved in 2024 with a ₹10,300 crore (≈$1.3 billion) budget, aims to build AI infrastructure and capacity via public–private partnerships (MeitY, 2024b). As a public–private partnership under MeitY, it unites government, organisations, industry bodies like National Association of Software and Service Companies (NASSCOM ), and academia to advance AI research and deployment. The IndiaAI Mission prioritises “Safe and Trusted AI”, underscoring the government’s commitment to ethical and secure AI development (MeitY, 2025a). This is aligned with ethical principles laid out by in the 2021 “Responsible AI for All” framework (NITI Aayog, 2021a,b).
AI governance in India
India’s approach to AI governance is multi-stakeholder, engaging government, industry, academia, and civil society in dialogue and implementation. This collaboration is essential due to the fast-moving nature of AI technology (Kaushik et al., 2025).
The Ministry of Electronics and IT (MeitY) governs AI in India, overseeing policy and national programmes (Mohanty and Sahu, 2024). In 2023, the Office of the Principal Scientific Advisor (PSA) started coordinating cross-ministry AI regulation (Mohanty and Sahu, 2024). The Prime Minister’s Office (PMO) and National Security Council Secretariat (NSCS) shape AI policy due to security concerns, including misinformation (Mohanty and Sahu, 2024). Sectoral regulators are expected to issue AI guidelines (Mohanty and Sahu, 2024). Parliament’s IT committee reviews AI impacts and oversight mechanisms (Mohanty and Sahu, 2024).
India’s federal system allows state governments to develop AI governance. Tamil Nadu launched a “Safe and Ethical AI” policy in 2020 to promote fairness and transparency in public services. States like Telangana and Karnataka have established AI hubs, such as the WEF Centre for the Fourth Industrial Revolution in Telangana.
AI for India 2030's multi-stakeholder approach is crucial for governance that reflects broad consensus (Kaushik et al., 2025). This partnership has produced AI playbooks and held policy hackathons, demonstrating India's preference for participatory governance that blends industry self-regulation with state involvement. Organisations like NASSCOM serve as key governance intermediaries and established an AI & Big Data Council with MeitY for policy advocacy. Another example is RAISE 2020, a global AI summit in October 2020, where government, domestic and foreign tech firms, and civil society exchanged insights on AI ethics and innovation (MeitY, 2024c).
Indian civil society, including non-profits, researchers, and the media, actively engages in AI governance, advocating for accountability. The government’s AI ethics guidelines reflect independent research, and the UNESCO AI Ethics Readiness Assessment for India includes academic experts (CAIDP, 2025). Concerns over bias in facial recognition and demands for algorithmic transparency led the government to adopt rights-based approaches (Joshi, 2024). As a result of advocacy, the 2022 Data Governance draft emphasises privacy safeguards more than the previous data accessibility policy (Joshi, 2024).
India’s multi-stakeholder approach aims to bring transparency in policymaking. Key AI policies, like the AI Governance Guidelines report (2025) and the AI Safety Institute draft, are open for public comment (MeitY, 2025b,c). While civil society may not always be central to decision-making, its inclusion boosts legitimacy.
Development
India’s AI ecosystem is rapidly advancing through government programmes, private sector leadership, and international collaborations. The flagship IndiaAI Mission, launched by MeitY, focuses on seven pillars: high-end computing infrastructure, indigenous foundational models, the national dataset platform (IndiaAI Kosha), skill-building via IndiaAI FutureSkills, and the Safe and Trusted AI vertical (IndiaAISafe). Key initiatives include deploying 18,000+ GPUs, developing Indian language LLMs, and establishing regulatory sandboxes and the IndiaAI Safety Institute to address AI risks through a techno-legal lens (MeitY, 2024a; 2025b). Platforms like “AI for India 2030” guide ethical AI use, supported by the DPDP Act (2023), the draft Digital India Act, and NITI Aayog’s Responsible AI principles (Burman, 2023; NITI Aayog, 2021a, b).
India’s private sector drives AI adoption, led by tech giants and startups. Major companies like TCS, Wipro, Reliance, and Adani use proprietary AI tools (NASSCOM, 2024a). Research centres from global firms such as Google leverage India’s top tech talent (Maslej et al., 2025). The startup ecosystem boasts over 6,200 AI startups and rapid growth in generative AI (NASSCOM, 2024b).
Public–private partnerships, including FutureSkills Prime and IndiaAI Centres of Excellence, focus on promoting AI innovation and enhancing workforce skills (FutureSkills Prime, n.d.; NASSCOM Centre of Excellence for IoT & AI, n.d.). Various ministries are incorporating AI into public services across sectors such as agriculture and health (Microsoft Stories India, 2017; Ministry of Agriculture and Farmers Welfare, 2019).
Internationally, India is a founding member of the Global Partnership on AI (GPAI, 2023) and collaborates on various governmental initiatives with the USA, the UK, and the EU (Chaudhuri and Mohanty, 2025; European Commission; UK Government, 2024; White House, 2024).
Current sentiment about AI adoption
India shows strong enthusiasm for AI adoption and confidence in its potential to solve “systemic challenges and achieve inclusive growth” (Kaushik et al., 2025). The government’s focus on public digital infrastructure has led to widespread AI tool adoption (Chakrabarti et al., 2024). Electronics and IT Minister Ashwini Vaishnaw recently stated that India aims to be among the top five AI nations (News On AIR, 2025). Discussions about innovation, socio-economic upliftment, and AI applications to improve healthcare delivery, address climate issues, enhance agricultural yield, and transform smart cities are viewed as "modernising efforts" for the future (Kaushik et al., 2025; Sambasivan et al. 2021). India’s robust software development and IT industry provide fertile ground for AI adoption, driving innovation and enthusiasm. AI tools are enhancing traditional software development tasks like coding and data analysis, and India’s technical talent is embracing AI and upskilling to remain relevant (Sarah, 2025; Chakrabarti et al., 2024). According to Times of India (TOI, 2024), India could become the “largest exporter of AI expertise” as companies like Google and Microsoft target its large pool of skilled programmers and technologists.
An MIT Technology Review article indicates that businesses are mandated to adopt AI in their processes and products (Aggarwal, 2018). A Global Workplace Skills Study 2025 reports that 96% of Indian professionals use AI and generative AI (Srivastav, 2025). Another study (Chakrabarti et al., 2024) reveals that many Indian white-collar workers believe AI enhances their performance and is vital for career advancement. Employees are reported to be preparing to “ride the AI wave” through self-driven or organisation-facilitated AI training. Additionally, there's growth in AI-enabled online platforms providing income-generating opportunities, especially in the informal sector (Katta, n.d). Research (Sambasivan et al., 2021; Chakrabarti et al., 2024; Joshi, 2024) suggests an “AI Euphoria” in India, fuelled by expanded information infrastructure and public discourse. Sambasivan et al. (2021) noted that a 2019 international AI perceptions survey ranked Indians highest in viewing AI as ‘exciting’, ‘futuristic’, and ‘mostly good for society’ (p. 321). AI is regarded as a revolutionary technology by various stakeholders in India (Joshi, 2024), shaping citizens' views with narratives of modernity and progress. This embrace is linked to “the aspirational role played by technology in India, signifying symbolic meanings of modernity and progress via technocracy” (Sambasivan et al., 2021, p. 321).
The AI landscape in India is continuously evolving and India is riding the AI wave unquestioning the power of AI. For example, in an influential study on analysing AI power in India, Sambasivan et al. (2021) note that much of the public discourse in India is dominated by what tech journalism is encouraged to cover which is mostly about app launches and tech investments and rarely about issues of “algorithmic bias” or need for “algorithmic fairness” because journalists fear facing “disapproval for questioning certain narratives” (p. 321). As AI’s application and integration is becoming increasingly pronounced across India’s socio-economic fabric, it is critical to understand and examine India’s approach to AI regulation and governance for responsible, ethical and safe AI implementation. The next segment discusses work and employment in India’s current AI context.
AI and the work and employment scenario
Any transformational effects of emerging new technologies such as AI on the world of work have to be situated within specific contexts of developing and emerging economies – in this case, an important emerging economy characterised by labour surplus, largest youth population in the world and a large informal economy (Hammer and Karmakar, 2021, ILO India Employment Report, 2024), where labour laws often fail to meet ILO’s comprehensive standards (Narayanan and Ramasubramanian, 2025; ITUC, n.d.).
Labour market status and impact of technology
The Indian Employment Report 2024 published by International Labour Organisation (ILO) highlights recent trends in the Indian labour market of “paradoxical improvements” (p. xx) with decent employment generation remaining a challenge. While there is evidence of economic growth post Covid 19 with modest improvements in employment conditions over the years, employment is predominantly in the informal sector and is characterised by poor quality employment and lack of social provisioning. Real wages of regular workers either remained stagnant or declined and women largely accounted for increase in self-employment and unpaid family work. With the world’s largest youth population residing in India, the report notes Indian youth experiencing difficulties in accessing decent employment. There is “persistently high and increasing unemployment rate observed among highly educated young individuals” (p. 208). Economic growth and productivity gains are attributed mostly to technology adoption and advancements rather than to rise in good quality employment.
Historically, technology adoption and advancements have had a profound impact on the nature of work and labour markets in India. From the emergence of the Information Technology services sector in the mid-20th century to large platform-based Gig economy and now AI, the history is long and chequered in terms of technologies impact on work, employment and labour market. While technologies helped generate new employment opportunities and increased economic prosperity, the gains across different social groups are reported to be unequal or limited (Nair et al., 2024; Hammer and Karmakar, 2021). Contrary to the labour market needs of the country, increasing use of technology in the workplace has given rise to creating production processes that are capital and skill intensive requiring fewer workers to be employed (ILO Indian Employment Report 2024). On the other hand, rapid increase in digitally mediated gig and platform work is providing employment to under and unemployed urban and rural workforces. A large surplus of labour comprised of young workforce in the informal sector have created a “fertile ground for the proliferation of AI -enabled platform work” which is reported to be an extension of informal work (Katta, n.d.; ILO Indian Employment Report 2024).
Nature of work and implications for workers in India’s AI-ecosystem
There is a growing body of research now drawing attention to the unprecedented pace at which AI is transforming the world of work globally with potentially disruptive consequences for the nature of work, workers and employment in general. India’s baseline conditions are different from developed countries as discussed in the beginning of the segment ‘AI and Work and employment in India’. With the advent of AI in India, optimistic narratives have focused on job creation and reshaping of the labour market (NASSCOM, 2022) – 12 million job creation by 2025 was projected by NAASCOM.
Available literature and news reports (Rani et al., 2024; Chaudhuri and Chandhiramowuli, 2024; Lohchab and Roy, 2024; Natarajan et al., 2021) have drawn attention to the increasing forms of AI development tasks performed in India required for the on-going development and deployment of global AI systems. Human workers are intrinsic to the process but the work is repetitive, mundane and non-cognitive in nature and is generally performed by young men and women with graduate and post-graduate qualifications. Within an AI ecosystem, AI development tasks, also referred to as ‘microtasks’ sit at the lower end of the global value chain of AI production involving data collection, cleaning, categorization, tagging, labelling, text and image annotation, content moderation, transcription, audio and image recording and preparing data sets which is required to configure machines and models that use the data sets. While this work is identified as the backbone of the AI ecosystem it is far distanced from the high-end AI data processing modelling work mostly concentrated in Global North.
Studies on human workers behind AI development tasks, commonly known as data work have highlighted the harmful impact of this work. Rani et al. (2024) studying Indian and Kenyan workers, draw attention to the significant risk this work poses such as underutilisation of skills and “deepening existing inequalities between those who benefit from technological progress and those exploited within the AI value chain.” (p.5). The researchers further note precarious labour conditions for these workers and no adequate recognition or protection “exacerbating economic and social disparities on a global scale” (p. 5). Researchers (Gupta, 2019; Chaudhuri and Chandhiramowuli, 2024) studying data workers in formal sector noted how invisibilisation of workers is a result of “specific embedding of AI production within the political economy of start-up capitalism” (Chaudhuri and Chandhiramowuli, 2024, p. 8) leading to devaluation and deskilling of young data workers at large.
However, AI development tasks are seen as a viable employment opportunity in India for many as the work can be carried out remotely by both men and women in urban and rural India. India’s first–ever National strategy for AI by NITI Aayog (2018) recognised the potential for this kind of work to generate significant employment with India’s data annotation and labelling market maturing in the future. Growing entry of private companies including BPO companies providing data labelling services indicate emergence of a private industry around AI development tasks in India (Lohchab and Roy, 2024). It is reported that these companies recruit workers under formal contractual and full-time salaried model with investment in training and worker well-being. These companies provide employment opportunities for marginalised communities (the urban poor, rural youth, educated women) and is different from gig or platform-based model of data work (Natarajan et al., 2021; Chaudhuri and Chandhiramowuli, 2024). However, researchers note that formalisation of data work has only regularised precarious work and cannot address the structural challenges/conditions confronting these workers. Existing literature documents that invisibilisation, devaluation, large scale de-skilling and displacement are common outcomes in this kind of work irrespective of whether the work is undertaken in informal or formal sector. Chaudhuri and Chandhiramowuli (2024) predict that the massive deskilling of human labour by AI can have far reaching implications in terms of impact on skill and career trajectories of the young and educated workforce of India adversely impacting socio-economic development.
It will be a remiss to not draw attention to algorithmic management of work in India that has expanded beyond digital labour platforms to traditional sectors such as healthcare, construction, call centres, warehouses to name a few. Algorithmic management of work is based on extensive data collection, real-time decision making, metrics driven evaluations of work tasks and performance and is reported to have led to decline in job quality due to increased monitoring, surveillance and work intensity experienced by workers in different sectors (Rajora, 2025). In relation to AI-enabled platform model of work organisation in Indian cities, Katta (n.d) note that algorithmic arrangement of work has similar outcomes as traditional informal work. Workers continue to lack labour protections, have poor job security and low earnings.
Despite studies reporting uneven societal implications, invisibilisation of work, regularisation of precarious work, decline in job quality, possibility of large-scale deskilling in formal employments and instability in employments, the impact of AI on the future of work in India remains inconclusive at this time. Rani et al. (2024) argue for a collective commitment to ethical and equitable AI development and informed policies, practices, and international cooperation because compromising ethical standards and decent work will lead to growing economic and social disparities on a global scale.
Some recent developments in the context of AI and work are encouraging - provisions regarding the use of "automated decision-making systems (ADMS)" usage in the platform work context. These have been included in the recently debated platform workers bills across different states. On May 27, 2025, Karnataka issued a landmark ordinance to protect platform-based Gig Workers and is considered as a significant regulatory milestone and covers broad spectrum of digital platform services, including but not limited to ride-sharing, food and grocery delivery, logistics, e-marketplaces, professional service platforms, travel and hospitality, healthcare, and content and media services (Vyas and Chacko, 2025). Telangana also has proposed a gig worker Bill - Telangana Gig and Platform Workers (Registration, Social Security, and Welfare) Bill, 2025 and contains provisions pertaining to ADMS and aims to provide social security and welfare measures for gig and platform workers within the state. It is to be noted that except the Rajasthan Platform based Gig Workers (Registration and Welfare) Act, 2023, which has been passed but does not have any ADMS-related provisions, none of the other state-level legislations have been enacted yet and are caught in bureaucratic delays (Dutta, 2025). The central government legislation concerning platform work, i.e., the Code on Social Security, does not have any provisions regarding this. Aapti Institute, a consulting organisation from India and a public research institution has focused on examining lived experiences of workers at the intersection of technology and society. The insights generated through their research so far have wider implication for policy-making and technology. Their website claims to work on creating “equitable and just digital world where people are empowered to negotiate with technology”.
Closing thoughts
Regarding AI technologies, there has been a range of policy responses in India, but the landscape of governance and regulation appear fractured and is still emerging. Indian government is seen more as a facilitator of "Informational capitalism" in this regard (Joshi, 2024). The argument is that a market-led prerogative is driving the development of AI technologies in India which is actually pushing the oversight and regulation of these technologies to the private sector and the state is acting more as a facilitator providing an infrastructural base for the production of these technologies.
In the absence of a clear, cohesive and comprehensive picture of India’s overall advance on AI regulation and governance, it is difficult to ascertain the extent to which India is dedicated to guiding AI in a direction that supports its large workforce and democratic values. The existing laws and policies – from the IT Act to the draft Digital India Bill, DPDP Act, and NITI’s ethical principles – reflect ongoing evolution. However, the enforcement capacity of these policies will be a critical test. There is no specific regulation on AI in employment, such as on AI-based recruitment or worker surveillance. Issues like gig workers’ algorithmic management and AI-led productivity monitoring are not directly supervised by labour or IT laws. This area is ripe for future policy development by the Ministry of Labour in dialogue with MeitY.
The regulatory frameworks have mixed implications for the world of work as the responsible AI guidelines remain voluntary and policy initiatives exist without statutory force, for example, IndiaAI Mission, NITI Aayog’s Principles for Responsible AI (RAI). The advocacy nature of Niti Ayog’s core principles to enhance human decision-making, ensuring it respects workers’ rights and well-being only promote voluntary adoption and lack legal enforcement, relying on goodwill for compliance. There are no penalties for AI violations unless any specific law is breached (like discrimination under constitutional or labour law, not yet under any specific AI law in India). India, despite being an ILO founding member has grappled with a complex labour rights landscape and economic growth has often been prioritised over worker welfare (Narayanan and Ramasubramanian, 2025).
While India’s AI trajectory is characterized by aspirations to pair AI advancement with ethics, safety and inclusion, and ensuring its use is responsible and equitable, the key policy frameworks governing AI in India have demonstrated limited engagement with how to regulate AI-enabled technologies in work and employment contexts to address issues related to worker rights, voice, and access to formal and decent employment. Policy discussions related to AI and work in India needs to gather momentum in the course of development of the overall AI governance and regulation frameworks considering India’s international labour standards obligations. This could serve as a blueprint for understanding regulation of AI-enabled technologies in contexts characterised by informality.
In closing, it will be remiss not to draw from one of the insightful publications in this journal by Mansouri and Bailey (2025), who affirm that “AI remains a technology committed to intensifying exploitation, amplifying inequality and poverty” (p. 7), which is already being evidenced. Their debate piece has helped raise pertinent questions - in the process of adopting AI are we lending ourselves to be “governed by abstract statistical techniques designed to achieve capitalist solutions”? (p.7). In terms of AI regulation and governance, are the evolving frameworks and mechanisms merely to make AI more acceptable as part of ‘ethics washing’ and facilitate further AI “roll out across all corners of human society” (p.7)?
