1 Introduction

In March 2024, during a meeting with Microsoft co-founder Bill Gates, India’s prime minister joked that AI appears to be one of the first words uttered by newborns and stated that he had used AI for language interpretation during the 2024 G20 summit (Soni 2024). While the comments were made in light humor, they highlighted the significance of artificial intelligence (AI) in India’s digital transformation journey. As the National Strategy for Artificial Intelligence (NSAI) 2018 states, India is poised to define its brand of AI leadership given the nascent stage of adoption of AI worldwide (NSAI 2018). The NSAI set out a strategic objective to leverage AI for inclusive economic and social growth while proposing the #AIforAll brand for India’s leadership in AI. To guide research and development in emerging technologies, the National Institution for Transforming India (NITI) Aayog emphasizes undertaking exploratory proofs-of-concept, collaborating with industry and academic experts, and building a vibrant AI ecosystem in India.

The available literature attempts to assess the credibility of India’s AI strategy using the lens of India’s current technical capabilities (Mohanty and Sahu 2024, Sarwgi et al. 2023). Some have sought to understand specific areas of development, such as security or economic growth (Garg et al. 2024). However, to fully grasp the implications and direction of India’s AI strategy, it is insufficient to analyze specific elements in siloes or consider only technical capabilities or the economic potential of AI. To comprehensively understand India’s AI strategy, it is essential to conduct a comprehensive and critical analysis of the underlying driving forces, including the country’s political economy, cultural context, and ongoing policy debates.

This article aims to undertake that detailed examination by contending that while India’s AI strategy is still in its nascent stages, there is an evident and urgent need to establish normative guidelines for the ethical use of AI. This article endeavors to provide a thorough and critical examination of the underlying motivations and the current direction of India’s AI strategy. It highlights the Indian government’s awareness of AI’s potential benefits, associated risks, and ethical challenges. The trajectory of India’s AI strategy will be significantly influenced by the interplay of social, economic, cultural, legal, and political factors, and by the extent to which governmental priorities may override ethical considerations.

2 AI legislation in India

Before 2018, India lacked a comprehensive and targeted policy approach for regulating AI. Various laws existent before 2018 aimed at affecting the development or use of AI, instead of directly regulating it. The Information Technology Act 2000 and the Information Technology (Reasonable security practices and procedures and sensitive personal data or information) Rules 2011 (White & Case 2024) offered guidelines for the protection of data used by AI applications, as AI was treated as a part of Information Technology (IT) systems. In 2018, the Finance Minister of India mandated NITI Aayog in his annual budget speech to establish a National Program on AI to guide research and development in emerging technologies (NSAI 2018). The directive demonstrated the government’s recognition of AI’s potential to transform economies and the need for India to strategize its approach. However, despite the growing cognizance of the impact of AI in different sectors of the domestic economy, India has not passed specific laws or regulations with a territorial scope or a sectoral scope, although there exist guidelines in financial services and healthcare. For example, in January 2019, the Securities and Exchange Board of India (SEBI) released a circular mandating reporting requirements for the use of AI and machine learning applications within the finance sector (White & Case 2024). This initiative aims to enhance transparency and oversight of AI and machine learning systems in financial services. In the healthcare sector, the strategy outlined by the National Digital Health Mission underscores the necessity of establishing guidelines and standards to ensure the reliability and safety of AI systems used in healthcare (Ibid). In 2018, the Indian Union government significantly increased its financial allocation for research, training, and skill development in emerging technologies, particularly AI, marking a 100% rise from previous investments (Press Information Bureau 2014). This move aligns with the broader objectives of the Digital India initiative, which aims to transform India into a digitally empowered society and a knowledge-based economy (Marda 2018). Furthermore, the Union Ministry of Commerce and Industry established an Artificial Intelligence Task Force in August 2017. This task force was created to integrate AI into India’s economic, political, and legal frameworks, thereby equipping the nation with the systemic capability to achieve its ambition of becoming a global leader in AI-driven economies. In 2018, India launched the National Strategy for AI, followed by the Principles for Responsible AI and Operationalizing Principles for Responsible AI in 2021, representing a marked shift from the general laws that were not tailored to AI.

2.1 National Strategy on Artificial Intelligence (NSAI)

Released in June 2018, the NSAI aims to establish a strong foundation for future regulation of AI in India. The strategy document is premised on the proposition that India’s strengths and characteristics can potentially position the country among the leaders on the global AI map. With a unique brand of #AIforAll, the NSAI emphasizes the application of AI to foster social and inclusive growth in India, besides replication of the solutions in similarly placed developing countries (NSAI 2018:5). The overarching motivation while designing the strategy, as stated in NSAI, has been to learn from the best policies of the other countries and to democratize access to AI. The NSAI also emphasizes the importance of applying AI to sectors having the greatest externalities during the adoption of AI solutions, which would inform the development of the AI implementation roadmap. The national-level strategy also acknowledges the limitation of the standalone efforts of the private sector is inefficient and financially sub-optimal, and therefore, envisages the role of sustained government intervention guided by optimization of social goods, rather than maximization of topline growth (Ibid:5). From a technological standpoint, the approach to AI implementation envisions maximization of late movers advantage, acknowledging India’s distance from consistently delivering home grown solutions in AI and accepting that innovation for India’s unique needs will help the country to leap frog technologically. Altogether, the strategy paper offers a comprehensive AI strategy and lays down the principles and objectives around which the AI implementation approach is centered.

Unlike its Asian counterparts, such as China, India has not defined clear and measurable short-term or long-term goals to attain leadership in AI in Asia or globally. However, India’s approach posits that the country’s scale and opportunity landscape affords the ideal test-bed to build, test, and validate scalable and sustainable AI solutions that can be extended to the rest of the emerging and developing economies, thereby positioning India as an AI leader in the Global South. The National Strategy for Artificial Intelligence (NSAI) made comprehensive recommendations to foster and support the AI ecosystem in India, structured around four key areas:

1. promoting research;
2. skilling and reskilling the workforce;
3. facilitating the adoption of AI solutions; and
4. developing guidelines for ‘responsible AI.’

2.2 Principles for Responsible AI

Launched in February 2021, the approach paper served as the first part of the strategy titled “Towards Responsible AI for All” aimed to establish ethical principles for the design, development, and deployment of AI within India’s unique legal and regulatory framework, drawing from global initiatives while ensuring contextual relevance. This strategy was intended to serve as a critical roadmap for the responsible adoption of AI in India, thereby building public trust in the technology (“Towards Responsible AI for All: Part 1” 2021). It also defined technological, legal, and regulatory approaches for managing AI systems effectively. The paper categorized AI solutions into two broad categories: ‘Systems considerations,’ which arose from system design choices and deployment processes that could impact stakeholders directly interacting with a specific AI system, and ‘Societal considerations,’ which encompassed broader ethical challenges arising from the usage of AI solutions, potentially affecting society beyond the immediate stakeholders. The paper examined several system considerations, including the lack of understanding of AI system functioning, challenges in explaining AI decisions, inherent biases leading to prejudiced decisions, potential exclusion of citizens in AI-driven services, difficulties in assigning accountability, privacy risks, and security risks. Additionally, it addressed societal considerations such as the impact of AI on jobs and the risks of malicious psychological profiling. The paper also acknowledged the Supreme Court of India’s emphasis on constitutional morality over social morality, highlighting that constitutional morality extends beyond the text of the Constitution to include the values of a diverse and inclusive society while remaining faithful to other constitutional principles. Based on the identified systems and societal considerations, the paper proposed broad principles for the responsible management of AI. These principles included safety and reliability, equality, inclusivity and non-discrimination, privacy and security, transparency, accountability, and the protection and reinforcement of positive human values.

The second part of the paper, “Towards Responsible AI for All: Part 2 – Operationalizing Principles for Responsible AI”, was launched in August 2021 to aid the implementation of AI principles. The paper identified a series of actions that both the government and the private sector needed to adopt to promote responsible AI. It recommended that regulation be proportional to the potential harm posed by an AI system (“Towards Responsible AI for All: Part 1” 2021). To assess the risks associated with AI, the paper proposed adopting policy interventions such as sandboxing and controlled deployments. In cases where the perceived risk of harm was low, it advised that governments might prefer regulatory forbearance, allowing market players to lead with self-regulation, while sectoral regulators would continue monitoring AI developments to avoid future conflicts in guidelines. Additionally, the paper suggested establishing an independent, multidisciplinary advisory body at the apex level, named the Council for Ethics and Technology (CET). This council would assist sectoral regulators in formulating appropriate AI policies and serve as a think tank to generate high-quality research on AI-related issues (Ibid). A marked feature of the paper was the outlining of specific roles for the government and the private sector, including the research and academia to build an institutional capacity for evaluating and addressing risks. Both the papers on responsible AI principles were a culmination of working papers placed for public consultation in 2020, to make the AI policymaking process participative and democratic for Indian citizens. The expansive consultations comprised experts across research, non-profit, law, civil society, government, and the private sector with the intent to understand the impact of AI on various stakeholders and the broader impact on society. It emphasized that promoting risk-minimized AI relied on two central concepts: calibration and continuous assessment. Calibration required that regulatory and policy interventions be precisely tailored to the specific uses and risk profiles of AI systems. Continuous assessment involved embedding these principles throughout the AI system’s lifecycle. The paper also recommended establishing guidelines for public-sector procurement of AI systems and adopting high-risk AI. Furthermore, it encouraged the private sector to develop innovative, cost-effective strategies for complying with AI standards.

2.3 Implementing the NSAI

The NSAI stated that despite the significant stake of the private sector in the development of AI in India, driving the adoption of AI in diverse sectors is the responsibility of the government (NSAI 2018). The adoption of AI is primarily aimed at overcoming access barriers, efficient access to government schemes, and enabling high-quality skill-based services at different levels of the government, leading to inclusive growth. Recognizing the sheer scale of public programs, the NSAI does not recommend a centrally enacted policy initiative but recommends a multi-stakeholder approach comprising private sector entities, academia, and research institutions. Pursuing a delicate balance between fostering innovation and mitigating risk, India’s AI strategy recommends an institutional mechanism for the procurement of AI systems with incorporation of responsible AI principles to foster trust in and improve acceptance of AI systems by the public (“Towards Responsible AI for All: Part 2” 2021:24). While various legislations and regulations influence the development and use of AI systems, India does not have any overarching legislation specific to AI (“Towards Responsible AI for All: Part 1” 2021:42). Therefore, at the central level, specific rules and regulations need augmentation to include AI-specific risks. Consequently, a risk-based regulatory mechanism is recommended in NSAI, where the government is responsible for calibrating the stringency of regulatory interventions proportional to the potential for harm carried out by the AI system. Instead of centralizing the authority over AI regulation, the NSAI establishes the basis for coordination between the government and the sectoral regulators. For example, SEBI’s circular on reporting requirements for AI applications in financial services, and the National Digital Health Mission’s (NDHM) strategy on laying down standards to check the reliability of AI systems have elaborate mechanisms to govern AI-based innovations in their respective domains. This avoids the risk of conflicting guidelines, leading to reduced compliance overhead (“Towards Responsible AI for All: Part 1” 2021:26).

Concerning the research ecosystem, the discussion papers on responsible AI recommend against reliance on private institutions for responsible AI research and advocate in favor of the national government forging alliances with international institutions to proactively commence, fund, and support research projects on AI. It emphasizes enabling social research to understand the outcomes of the interaction of AI systems with the local and marginalized communities. Moreover, increased focus on policy and empirical research is proposed to adapt policies towards technology-driven economies (Ibid: 28). The proposed National Strategy for Artificial Intelligence (NSAI) introduces a two-tiered integrated approach aimed at enhancing both core and applied research in AI within India. This approach is designed to establish a robust AI ecosystem that not only fosters innovation but also ensures the practical application of AI technologies. The first tier involves the establishment of Centres of Research Excellence in Artificial Intelligence (COREs), which are dedicated to advancing foundational AI research (NSAI 2018). These COREs are envisioned to spearhead the creation of new knowledge by focusing on fundamental research and sourcing essential technologies that will equip India for future technological advancements. The COREs are also tasked with developing infrastructure tools that facilitate the direct application of basic research, including AI architecture and platform development. The selection of institutions to be designated as COREs follows an application-based model, wherein the applicant must demonstrate viability in terms of faculty and institutional capabilities. COREs may specialize in one or multiple focus areas, and collaborative projects across different COREs are encouraged to promote interdisciplinary linkages and the development of cross-functional technologies. The financial framework for COREs includes significant funding allocations, ranging from INR 50 crore to INR 100 crore per CORE, to support large-scale projects and research initiatives.

The second tier involves the creation of International Centres for Transformational Artificial Intelligence (ICTAIs). These centers are envisioned as industry-led initiatives focused on the development and deployment of application-based AI technologies (NSAI 2018). ICTAIs are expected to address high-level challenges, particularly those identified through interministerial projects, and to deliver commercial AI solutions. These centers aim to transform ideas and prototypes into marketable products by facilitating proactive coordination, communication, and technology transfer to the industry. The operational model of ICTAIs is structured as a public-private partnership, with initial seed funding ranging from INR 200 crore to INR 500 crore per ICTAI, covering major operational expenses, infrastructure, and technology requirements. The governance of ICTAIs involves a management team with significant representation from the private sector, while also including government participation. Corporate entities are incentivized to participate in ICTAIs through access to high-quality training data, infrastructure, and opportunities to contribute to a national mission, with expenditures potentially counted towards corporate social responsibility (CSR) obligations.

In addition to these two tiers, the NSAI proposes the establishment of an overarching body known as the Centre for Studies on Technological Sustainability (CSTS). This entity is tasked with addressing issues related to finance, social sustainability, and global competitiveness of AI technologies. CSTS is responsible for monitoring the social impact of AI technologies, studying their financial viability, and recommending pricing models to enhance market penetration. It also plays a crucial role in fostering international collaborations, studying global AI landscapes, and promoting knowledge exchange through workshops and conferences (NSAI 2018).

A stark difference in the NSAI compared to the policy approach adopted by its East Asian counterpart, China, is the absence of private sector champions that are tasked with the development of AI systems in specific sectors of AI. Therefore, a greater part of the responsibility of legislating and regulating AI rests with the government. Though the NSAI is not to be read alone in isolation, it does offer an objective and comprehensive viewpoint of the driving forces behind India’s AI strategy. Given the strategic and policy significance of the NSAI, this article will use it as the core framework to analyze the drivers and ethical boundaries shaping India’s AI approach, ensuring alignment with national interests and global ethical standards.

3 India’s AI strategic focus

The NSAI offers a longitudinal outlook on India’s policy and strategic situation regarding AI, comprising its unique capabilities, opportunities for exploitation, and latent risks. Although a technology-first approach may offer a perspective on the different types of sector-specific technologies India is investing in, it will provide only a myopic viewpoint of India’s readiness towards the adoption of AI and the inclusivity of its AI policies. Therefore, it will be of greater interest to analyze India’s preparedness for AI from a policy-first approach by studying the areas where the NSAI considers AI presents significant opportunities. This section also focuses on the sectors outlined by the NITI Aayog where India currently faces considerable challenges, however, stands to gain from developing AI systems in each of them. Additionally, the section explores the international cooperation method recommended in the NSAI to foster the adoption of AI systems in the country across corporations, academia, and other enterprises. Moreover, India’s attention to principles for responsible AI also warrants an analysis to understand how India plans to spearhead thought leadership in technology ethics and inclusivity. The NSAI, together with the two discussion papers on responsible AI, emphasizes four areas where AI can make a significant impact within the country, viz. global cooperation, economic development, social governance, and moral governance. Though interrelations exist, the article shall study each independently by discussing the relevant literature centered on contemporary policy discourses, political backdrop, and socioeconomic context.

3.1 Global cooperation

India’s AI strategy, as outlined in the AI Taskforce Report by the Ministry of Commerce and Industry and NITI Aayog’s National Strategy for AI, centers on the concept of “AI for All.” The strategy emphasizes India’s ambition to become the primary provider of AI solutions for 40% of the world, specifically targeting emerging and developing economies. India aims to collaborate with international partners and its private sector to advance AI research and development (Saran et al. 2018). NITI Aayog identifies Canada, Germany, Israel, Japan, Russia, Singapore, the UK, and the US as key potential partners to jointly develop AI solutions that cater to the needs of the global lower-income population. To enable a level playing field for all players in the value chain, address information asymmetry on price points, and simplify collaboration, India is building a multi-stakeholder marketplace of AI solutions (NSAI 2018). Ensuring the availability of raw components at various stages of AI solution development will enable firms to concentrate on specific issues rather than building end-to-end capabilities. This will facilitate efficient data utilization, optimize computational and human resources, and ensure that data custodians obtain proper permissions from data owners before sharing data.

Unlike the United States and China which are engaged in an arms race to become the ultimate AI superpower, India’s consciously chosen AIforAll approach shifts the focus to inclusivity and utility of AI in solving age-old problems of the country (Elias n.d.). With the intent to become the “AI Garage” of the developing world (NSAI 2018), India has entered into strategic partnerships with several countries. For example, India signed a GBP 1 billion trade deal with the United Kingdom for the development of drones and AI technologies (Elias n.d.). Similarly, India and the US cooperate on AI research through the Indo-US Science and Technology Forum (IUSSTF) that launched the US-India Artificial Intelligence (USIAI) Partnership for bilateral research and development collaboration on AI (Ibid). Moreover, as founding members of the Global Partnership on Artificial Intelligence (GPAI), India and the US are poised to develop a shared understanding of the safety risks that can help build scientific consensus on a set of common standards to mitigate risks related to bias, privacy, trust and security in AI systems (Mohanty and Singh 2024). The U.S.-India Global Digital Development Partnership promotes the adoption of Digital Public Infrastructure (DPI) in developing countries. India can share its expertise in building DPI for low-resource settings to support AI deployment in regions with limited connectivity. Germany and India formalized an agreement to establish a joint research program in Artificial Intelligence and extended the Indo-German Partnership in Higher Education for an additional four years, with each nation contributing 3.5 million euros (PIB Delhi 2023). Additionally, the UAE and India signed a Memorandum of Understanding (MoU) to foster growth by assessing the technical and investment potential for deploying an eight-exaflop supercomputer cluster in India, aimed at benefiting the government, public and private sectors, as well as academia (Esmail 2024).

The European Union (EU) and India actively engage in various bilateral and international consultative forums, aimed at addressing the multifaceted challenges and risks associated with the development of artificial intelligence (AI). These forums facilitate collaborative efforts to establish a comprehensive stance on AI-related issues (Šime 2024). The sustained partnership between the EU and India, as signatories to the Bletchley Declaration (2023), holds significant strategic importance, not only for the two entities but also for the broader international community. The EU’s renowned regulatory framework, often termed the ‘Brussels Effect,’ when combined with India’s expertise in the rapidly evolving socio-economic landscape of Asia, can create a robust partnership. This collaboration is expected to yield substantial advancements in AI governance, contributing to global regulatory improvements. The joint statement from the inaugural meeting of the EU-India Trade and Technology Council (2023) emphasizes the commitment of both parties to coordinate within the GPAI and to explore bilateral cooperation in fostering trustworthy and responsible AI, particularly in the domains of research and innovation (Šime 2024). This partnership is envisioned as a pivotal source of expertise, with the potential to influence AI governance frameworks on a global scale.

To bring a multilateral approach to AI governance, India garnered support from the US, France, the UK, Canada, Japan, Korea, Brazil, and Argentina to make GPAI as the apex authority for all matters related to AI regulations, including formulating a common international framework for AI (Agarwal 2024). India also collaborated with international organizations, such as UNESCO, to organize the National Stakeholder Workshop on the Ethics of Artificial Intelligence to launch the Readiness Assessment Methodology. The methodology will evaluate India’s AI capacities across legal, social, cultural, scientific, educational, technical, and infrastructural domains, enabling experts and policymakers to identify necessary institutional and regulatory changes to harness AI technologies while mitigating potential risks (“Catalyzing AI Readiness in India” 2024).

3.2 Economic development

The NSAI highlights economic development as a key strategic opportunity, emphasizing the transformative potential of AI to drive the country’s economic growth. AI is seen as a tool to surpass traditional capital and labor constraints, unlocking new avenues for value creation and growth (NSAI 2018). The strategy targets economic restructuring across multiple sectors, including healthcare, agriculture, education, smart mobility, transportation, and the development of smart cities and infrastructure.

Economic growth in India has been one of the fastest in the recent decade (“Is Generative AI beginning to deliver on its promise in India?” 2024) and has bypassed any other country of a similar economic condition, except China (Erumban and Das 2024). Liberalization of the economy in India during the early 1990s coincided with the rapid information and communication technology (ICT) revolution, and India leveraged its large English-speaking demographic, low unit labor cost, and availability of qualified engineers to benefit from the macroeconomic trend (Ibid). The inclusion of ICT in different industries led to increased labor productivity and greater efficiency in the organization of production. Over the years, ICT and the digital economy have grown to become the major economic drivers for India, contributing to 13% of India’s Gross Domestic Product (GDP). Moreover, India aims to grow the ICT sector to USD 1 trillion by 2025, accounting for 20% of the GDP (“Information and Communication Technology” 2024). ICT holds potential for India’s rural development, currently characterized by poverty and low socioeconomic development, thereby helping India transition from a low-productivity agrarian economy to a high-productivity service sector-led growth economy (Bajpai n.d.).

Against this backdrop, the National Association of Software and Service Companies (NASSCOM) suggested in 2020 that on average, a unit increase in AI intensity by AI-using firms can return USD 67.2 billion (accounting for 2.5% of GDP) to the Indian economy in the short-term (Kathuria et al. 2020). The Ministry of Finance had approved an investment of INR 7 billion for NITI Aayog’s AI program, which could increase AI investments at rates higher than the business-as-usual rates. The investment was expected to increase AI intensity by 1.3 times leading to spillover effects of USD 85.8 billion for the Indian economy (Ibid: 25). Availability of skilled workforce and diversity of use cases and data sources add up to India’s strength. Amidst the increasing potential of AI in boosting the Indian economy, Generative AI has garnered attention to significantly enhance India’s GDP, with projections estimating an increase of $359-438 billion by 2030, equating to a 5.9-7.2% boost. Over seven years, the cumulative impact could total $1.2-1.5 trillion, contributing 0.9-1.1% to the annual growth rate. This growth is anticipated to be sector-specific, with substantial benefits expected in business services, finance, transportation, education, retail, and healthcare, aligning with the strategic objectives outlined in the National Strategy for Artificial Intelligence (NSAI) (“Is Generative AI beginning to deliver on its promise in India?” 2024). Despite this potential, a study by Ernst & Young revealed that while 60% of organizations recognize the significant impact of Generative AI on their business models, approximately 75% report only low to moderate readiness to fully leverage its benefits (ET Bureau 2023). Additionally, Accenture forecasts that AI could raise India’s annual growth rate by 1.3 percentage points by 2035, under a scenario where intelligent machines collaborate with humans to address complex challenges. This increase could add up to $957 billion, or 15% of the current gross value added, to the economy (Menon et al. 2017). Notably, 34% of executives surveyed by Accenture believe that AI will transform their organizations within three years, with 53% planning investments in AI-related technologies.

AI presents a paradoxical challenge, as its potential to drive significant benefits and advancements is accompanied by the risk of adverse effects, notably the disruption of labor markets. This concern is clearly stated in the NSAI as it acknowledges that AI “disrupts the nature of jobs and shifts the benchmarks of technological aptitude”, and therefore “skilling and reskilling of workforce forms an integral part of” India’s approach to adopting AI (NSAI 2018). AI significantly impacts employment in three key ways: by complementing human labor in certain tasks, fully replacing it in others, and generating new job types (Kumar 2021). Jujjavarapu et al. (2018) estimated that AI technologies would increase employment opportunities in India’s organized manufacturing and services sectors from 38 million to 46-48 million by 2022. Ghosh et al. (2018) analyzed over 1,000 responses from CXOs and decision-makers and found that Indian executives expressed fewer budgetary concerns (32%) compared to their global counterparts (40%), highlighting a greater willingness to invest in AI. Efficiency gains were cited as the main driver for AI adoption in India (80%), followed by revenue enhancement and innovation. According to a study conducted by the Broadband India Forum in collaboration with the Electronics Skill Council of India, Agriculture Skill Council, and Healthcare Sector Skill Council, the implementation of Internet of Things (IoT) and AI applications is projected to generate over 2.8 million jobs in rural India over the next 8 to 10 years, with an estimated annual economic value of approximately USD 9 billion (Devendranath et al. 2020). Despite these benefits, AI poses challenges, particularly in workforce displacement. The surplus of STEM graduates and the automation of certain jobs could hinder employment for a significant portion of the population (“National Strategy for Artificial Intelligence” 2018). Although India’s advanced IT sector and favorable demographics suggest the country might be well-prepared for the workforce disruptions AI will bring, these demographic advantages could become liabilities if the necessary structures and policies are not established (Ibid). The Economic Survey of India (2024) warned that AI could displace high-skilled, managerial roles and non-routine intellectual tasks, potentially affecting long-term economic growth (TOI Tech Desk 2024). The report highlighted that the increasing demand for AI skills in businesses negatively affects non-AI roles and top-tier wages, leading to the displacement of high-skilled managerial positions and non-routine intellectual tasks.

To reskill the workforce on AI skills, the NSAI proposes incentivizing the creation of jobs within the AI solution development value chain, focusing on roles requiring minimal expertise to generate large-scale employment opportunities. Tasks such as data annotation, image classification, and speech transcription, which demand lower skill levels, offer a chance to leverage labor cost advantages for global companies. Additionally, the NSAI advocates for the recognition and standardization of informal training institutions, the establishment of open learning platforms, and the creation of financial incentives for employee reskilling (“National Strategy for Artificial Intelligence” 2018).

3.3 Social governance

Social needs have been placed at the center of India’s approach to AI, as explicitly highlighted in the NSAI. NITI Aayog has emphasized several sectors that are anticipated to benefit the most from AI, including healthcare, agriculture, and education.

India’s healthcare system exhibits a dichotomy where world-class hospitals coexist with an acute shortage of qualified medical professionals. The doctor-to-population ratio, with an assumed availability rate of 80%, stands at 1:1,596, reflecting a significant gap in healthcare accessibility (Central Bureau of Health Intelligence 2018). Government expenditure on healthcare remains minimal, with only 1.4% of GDP allocated in 2016-17, one of the lowest globally (Rao, 2018). Consequently, the majority of Indians, 79% in urban areas and 72% in rural areas rely on private healthcare services, which are largely fragmented and unregulated (National Sample Survey Office 2014), with only 1% of private hospitals formally accredited (Jyoti 2017). The application of machine learning (ML) and AI technologies offers potential solutions to these challenges, particularly in rural and low-income settings. AI can enhance access to quality healthcare by addressing the disparity between skilled doctors and patients, improving the training and efficiency of medical professionals, and enabling personalized healthcare delivery at scale (Parry and Aneja 2023). The NSAI envisions AI as a tool to lower barriers to healthcare access, particularly in underserved rural areas. AI-driven diagnostics, personalized treatments, early pandemic detection, and imaging diagnostics are highlighted as key applications. Additionally, the integration of AI with robotics and the Internet of Medical Things could revolutionize healthcare by creating a new, responsive system. For instance, NITI Aayog, in collaboration with Microsoft and Forus Health, is piloting an AI-powered technology for the early detection of diabetic retinopathy using the 3Nethra device, which provides AI-driven insights in remote areas with limited connectivity (NSAI 2018).

Agriculture still accounts for approximately 49% of the Indian workforce, while contributing only 16% of the GDP (Ibid). Agriculture remains a critical sector in India, supporting the livelihoods of approximately 58% of the population. In 2022, the sector was valued at USD 370 billion, contributing 19.9% to the national GDP for FY 2021, and representing 11.9% of global agriculture’s Gross Value Added (GVA) (Shukla et al. 2022). To sustain India’s economic growth at an annual rate of 8-10%, agriculture must achieve a growth rate of 4% or higher. Despite substantial progress and government focus, the sector continues to grapple with challenges such as reliance on unpredictable variables, inefficient supply chains, and low productivity (“National Strategy for Artificial Intelligence” 2018). India’s agriculture is hindered by its dependence on resource-intensive practices, leading to land degradation, reduced soil fertility, excessive use of inorganic fertilizers, depleting water tables, and emerging pest resistance. Water use remains inefficient, with agriculture consuming 89% of extracted groundwater despite only one-third of the gross cropped area being irrigated. The sector’s vulnerability to climate change, particularly in rainfed regions, further exacerbates these issues. AI offers promising solutions to these challenges by predicting advisories for sowing, pest control, and input management, thereby enhancing income and stability for farmers. The National Informatics Centre (NIC) has developed the Agriculture Information Management System (AIMS), which has evolved to deliver smart farming solutions (Shukla et al. 2022). The NSAI envisions integrating AI with remote sensing, high-resolution weather data, and local image capture to enable real-time monitoring of crops, providing actionable insights to farmers and extension workers as needed. Private sector initiatives have also made significant contributions. Microsoft, in collaboration with Andhra Pradesh’s provincial government, provided an AI-powered sowing app to 3,000 farmers, resulting in yield increases of 10-30% (Elbehri & Chestnov 2021). Post-harvest challenges such as crop wastage and market access remain critical, with up to 40% of produce lost. The World Economic Forum’s Artificial Intelligence for Agriculture Innovation (AI4AI) initiative, in partnership with the Telangana state government, launched the ‘Saagu Baagu’ pilot in Khammam district. Supported by the Bill and Melinda Gates Foundation and implemented by Digital Green, this project improved the chili value chain for over 7,000 farmers, yielding a 21% increase in chili production per acre and significant reductions in pesticide and fertilizer use (Elbehri & Chestnov 2021). Additionally, the German startup Plantix developed an AI-based app to assist farmers in detecting plant damage during crop production, available in more than 17 local languages. Wadhwani AI also developed an object detection model to combat pest infestation, a major issue for India’s 10 million cotton farmers. This model identifies and counts pests in traps using images taken by farmers, generating real-time alerts based on pest density and action thresholds defined by agricultural scientists (Elbehri & Chestnov 2021).

India’s large youth population underscores the critical importance of a well-developed education sector. Despite notable improvements in enrollment, with Gross Enrolment Ratios (GER) at 97% at the elementary level and 80% at the secondary level in 2018 (“National Strategy for Artificial Intelligence” 2018), the sector faces significant challenges, including low retention rates and poor learning outcomes. The retention rate at the elementary level stood at 70.7%, indicating that approximately one-third of enrolled students dropped out before completing eighth grade, while the secondary level retention rate was 57.4% (Ibid). A key issue is the lack of differentiated instruction, particularly in rural and remote schools where multi-grade classrooms are common. Teachers often face the challenge of managing classrooms with students of varying ages, abilities, and learning levels, which contributes to poor learning outcomes. Additionally, teaching methods in many classrooms remain rote-based and non-interactive, and remedial instruction is often not tailored to individual students’ needs. Furthermore, the adoption of technology in education is limited, primarily due to insufficient teacher training despite the availability of ICT infrastructure. Many children are at risk of dropping out due to inadequate school infrastructure, untrained teachers, language barriers, significant learning gaps, and challenging family circumstances. Despite the availability of ICT infrastructure, the adoption of technology in schools remains low, largely due to insufficient teacher training. However, ICT and AI can bridge these gaps. The growing availability of digital content through initiatives like the National Repository of Open Educational Resources (NROER) and Diksha highlights the potential of AI in enhancing education (Kasinathan 2020). The National Education Policy (NEP) 2020 also emphasizes the role of disruptive technologies in transforming the education system (Majid and Lakshmi 2022). The NSAI emphasizes the potential of AI to address these challenges by assisting teachers in managing multi-level classrooms, developing customized educational content, and predicting student dropout risks. AI-driven tools, such as Intelligent Tutoring Systems, can deliver personalized learning experiences, adapting to students’ proficiency levels, learning styles, and paces. For instance, Education Initiatives (EI) has utilized AI algorithms through its Mindspark product to analyze student responses and provide tailored learning activities (Kasinathan 2020). Moreover, AI-powered language platforms and initiatives like Bhashini are being developed to support multilingual education (Garg et al. 2024). Additionally, AI applications like DeepGrade offer solutions to teacher shortages by automating the grading of handwritten responses, thereby allowing educators to focus on more interactive teaching. These advancements represent a significant opportunity to enhance the quality of education in India, particularly in underserved areas (Garg et al. 2024).

3.4 Moral governance

India’s experiment with AI in moral governance has primarily been to track population health and to monitor conditions for public safety. Unlike its East Asian counterpart, China, India lacks a social credit system that regulates the financial and social behavior of individuals. Though India has dabbled with AI for counterterrorism measures, most of the applications have been limited to the international border regions where infiltration by malicious actors is possible. However, given the democratic setup of the nation, surveillance of citizens is largely looked down upon by the populace. India’s approach to using AI for moral governance has been a cautious introduction of incremental use cases, instead of a massive policy introduced through legislation.

For example, the city of Ahmedabad implemented an AI-integrated surveillance system, incorporating live drone footage and camera feeds from traffic signals and buses, providing a comprehensive six-camera view of the city. This system tracks traffic violations, unidentified activities, and missing persons while also identifying suspicious activities (Dharamraj 2024). Similarly, in April 2021, AI-enabled cameras in the city of Haridwar monitored festival crowds for individuals without masks and those violating physical distancing rules (Mahapatra 2021). Private sector collaborations with the Indian government have also advanced facial recognition and citizen surveillance. Companies like FaceTagr and StaqU supply AI-driven facial recognition solutions to law enforcement agencies. Mobineer Info Systems developed E-Beat Book, a smart-policing app incorporating facial recognition to match individuals with databases for rapid information retrieval (Ibid). However, the expansion of AI-led digital surveillance in India has raised significant privacy concerns. In a rapidly digitalizing democracy, the absence of a robust data protection law has led to diverse forms of digital surveillance, despite the Supreme Court’s ruling in K.S. Puttaswamy vs. Union of India 2017 affirming privacy as a fundamental right (Mahapatra 2021). Moreover, India is experimenting with AI-driven surveillance in controlled environments, ensuring prior notification to stakeholders. For instance, the Union Public Service Commission (UPSC) plans to deploy advanced facial recognition and AI-based surveillance to enhance exam security. This includes using AI-enabled CCTV systems for comprehensive monitoring of exam venues, detecting suspicious activities, and safeguarding against fraud and impersonation (Sharma 2024). Despite the incremental progress in the application of AI for moral governance in context-specific micro-environments, India’s approach is yet to integrate informed consent in the use of facial recognition technology and biometrics on a statutory basis, to dispel risks of compelled consent (Mahapatra 2021).

4 The discourse on digital ethics and AI in India

Alongside establishing economic, social, and governance goals, the NSAI specifies the obligation to ensure digital ethics in the use of AI in diverse sectors. This appreciation of the need for ethics in AI comes in the wake of the absence of laws established specifically for AI and intelligent systems. The approach papers titled “Towards Responsible AI for All” extensively discuss the safety and ethical considerations surrounding AI technologies. These papers outline seven key principles that set the ethical requirements for AI in India. Both the country’s approach to responsible AI emphasizes broad principles encompassing safety and reliability, equality, inclusivity, privacy, security, transparency, accountability, and the reinforcement of human values (“Towards Responsible AI for All – Part 1,” 2021). These principles are intended to guide the design, development, and deployment of AI technologies to ensure they are ethically sound and aligned with the broader societal goals.

While no specific anti-discrimination legislation in India directly addresses AI-driven decision-making, existing anti-discrimination laws do not explicitly exclude decisions made through AI. Consequently, it falls within the purview of such laws to regulate AI-based decisions, particularly when AI is employed by entities with constitutional or legal duties to ensure impartiality (“Towards Responsible AI for All – Part 2” 2021). Although overarching AI ethics principles are intended to guide the design, development, and deployment of AI technologies, a sector-specific, graded risk-based approach should be adopted to address the unique challenges presented by different use cases across various industries. In this context, the Supreme Court of India, in landmark cases such as Naz Foundation and Navtej Johar, has emphasized that the prevailing morality in India must be grounded in constitutional morality rather than societal morality. Constitutional morality serves as the foundation for protecting minority rights against majoritarian views, ensuring that AI decision-making adheres to the basic rights guaranteed by the Constitution of India.

Government-affiliated bodies in India are actively promoting the national standardization of AI technologies. The Department of Telecommunications (DoT) and its technical arm, the Telecommunication Engineering Center (TEC), collaborate closely with the International Telecommunication Union in AI standardization efforts (“Artificial Intelligence Policies in India – A Status Paper” 2020). Similarly, the Bureau of Indian Standards (BIS) engages with the International Organization for Standardization (ISO) through the joint committee ISO/IEC JTC 1/SC 42, which is responsible for standardization in artificial intelligence. In December 2017, BIS established a committee focused on AI and Big Data standardization, comprising experts from academia, research institutions, government bodies, and leading technology organizations. This committee addresses critical areas such as cybersecurity, legal and ethical issues in IT, technological mapping, and leveraging AI for national missions. Additionally, private sector companies are aligning their AI ethics initiatives with the government’s focus on responsible AI. Major technology firms, such as IBM and Wipro, have established internal panels and ethics boards to oversee the development of responsible AI systems (Lohchab 2024). These panels address emerging concerns related to data privacy, sovereignty, bias, and the risks associated with generative AI models. Notably, a significant proportion of businesses with mature Responsible AI practices have instituted internal AI ethics committees, with a strong commitment to ongoing investments in workforce sensitization and the development of robust RAI compliance strategies (“The State of Responsible AI in India” 2023). Additionally, the NSAI states that a consortium of Ethics Councils could be established across all COREs, ensuring that each CORE adheres to standardized ethical practices in the development of AI technologies and products (NSAI 2018). The strategy document also posits that to achieve the vision of “AI for All,” which emphasizes the need for inclusive AI for the global community, the Government of India should lead efforts to establish a global public AI research institution. This institution, referred to as the “People’s AI,” would advance the field of AI for the benefit of humanity (Ibid).

In this section, the paper will examine AI ethics paradigms concerning privacy, medical ethics, and deepfakes, as these domains represent some of the most developed areas where one can discern the broader contours of India’s approach to digital ethics. The focus on these areas is not intended to provide a comprehensive overview of all ethical debates surrounding AI in India. Rather, the analysis aims to shed light on the emerging contentious issues and contribute to a broader understanding of the ethical boundaries that may be delineated as India formulates its normative agenda in the realm of AI ethics.

4.1 Privacy

The NSAI acknowledges the absence of formal regulations around anonymization of data as a barrier to achieving the goal of AI for All in India (NSAI 2018). It establishes that privacy, security, and ethics are common denominators for all the recommended AI-related initiatives. Till 2023, three laws were primarily responsible for regulating the collection, use, and disclosure of personal data by the ICT systems. The Information Technology Act 2000, the Information Technology (Amendment) Act 2008, the Information Technology (Reasonable Security Practices and Procedures and Sensitive Personal Data or Information) Rules 2011, and the Personal Data Protection Act 2019 (withdrawn in 2022) governed privacy and data protection in India by prescribing general information security requirements (Chakraborty et al. 2023). However, the Acts’ primary focus stayed on information security, instead of data protection, and did not offer comprehensive regulations on the processing or transfer of personal data. The Digital Personal Data Protection Act (DPDPA) 2023, published in the Official Gazette of India in August 2023, is the country’s first comprehensive data protection legislation and statutory data protection framework, aimed at regulating the collection, use, and disclosure of personal data. The legislation is designed to protect the personal data of “data principals,” which includes individuals to whom the data pertains, as well as guardians of minors and individuals with disabilities. It applies to personal data processed by “data fiduciaries,” entities responsible for determining the purposes and means of data processing, and “data processors,” who handle data on behalf of fiduciaries (Chakraborty et al. 2023). The regulation encompasses any data that identifies an individual, except for data processing for personal or domestic purposes or data made publicly available by the data principal or under a legal obligation (Chakraborty et al. 2023). It confers several rights upon individuals, including the right to access information, request correction or erasure of data, and seek grievance redressal (“The Digital Personal Data Protection Bill, 2023” 2023). The establishment of the Data Protection Board of India is mandated to address non-compliance issues (Ibid). Consent for data processing must be “free, specific, informed, unconditional, and unambiguous,” with individuals receiving clear notices regarding their rights and the grievance redress mechanisms available (Burman 2023). Additionally, individuals are entitled to withdraw consent and obtain a summary of their collected data, including the identities of other data fiduciaries and processors with whom their data has been shared. Furthermore, individuals have the right to request the correction, completion, updating, and erasure of their data, as well as access to grievance redressal (Burman 2023).

Notably, the central government reserves the authority to exempt certain government agencies from the Bill’s provisions on grounds such as national security, public order, and crime prevention. Exemptions granted to the State for data processing under the pretext of national security could potentially lead to excessive data collection, processing, and retention, posing a threat to the fundamental right to privacy (Burman 2023). The Supreme Court of India, in the landmark case Justice K.S. Puttaswamy v. Union of India, affirmed that the right to privacy, including informational privacy, is integral to the right to life (Burman 2023). The 2019 bill contained provisions allowing the government to regulate non-personal data and mandate private entities to provide specific non-personal data as required, thus establishing a broad, cross-sectoral framework that emphasized preventive measures for businesses (data fiduciaries) and rights for individuals (data principals) (Burman 2023). The 2023 Bill lacks provisions addressing risks from personal data processing, omits rights like data portability and the right to be forgotten, and introduces exceptions for consent under special circumstances, which significantly empower the state. These discretionary powers could potentially undermine the intended data protection measures.

These contentions require consideration given that the protection of personal data is a critical concern for 82% of Indian consumers, according to PwC’s 2024 Voice of the Consumer Survey. This concern exists alongside a willingness by over 66% of Indians to share personal data for more personalized services, though 76% express anxiety about privacy and data sharing. This highlights a clear need for stringent privacy protections. In response, the NSAI outlines seven core principles of data protection and privacy: informed consent, technology agnosticism, data controller accountability, data minimization, holistic application, deterrent penalties, and structured enforcement. The strategy further recommends the establishment of sectoral regulatory frameworks and alignment of national laws with international standards, such as the GDPR. However, despite these recommendations, the DPDPA 2023 contains significant shortcomings, leaving several ethical issues unresolved.

4.2 Medical ethics

India’s use of AI in medical science has been increasing. AI and robotics are seen as solutions to address the shortage of skilled healthcare professionals and to fulfill the need for personalized healthcare for patients (Das et al. 2024). For example, Manipal Hospitals in India deployed IBM Watson to assist surgeons with data analytics and deep medical knowledge. The surgical robots market is estimated to grow at a compounded annual growth rate of 20% owing to the increasing demand for automation (Ibid). AI holds the potential to analyze vast amounts of medical data from X-rays, MRIs, and CT scans while helping with disease prediction, telemedicine, and personalized treatment of individuals with different medical needs. Moreover, India’s adoption of AI surged substantially during the COVID-19 pandemic as 73% of pharmaceutical and healthcare companies adopted AI in 2020 (Press Trust of India 2020). For instance, Tata Consultancy Services in India collaborated with India-based Prayas Health Group to develop a virtual computerized AI model to forecast the spread of COVID-19 in the urban districts of India (Bajpai and Wadhwa 2020). This necessitated the formulation of policies and frameworks for the ethical use of AI in healthcare. In this regard, the Digital Information Security in Healthcare Act (DISHA) 2018 and the Health Data Management Policy (HDMP), 2022 provided individuals with the right to ownership of digital data, privacy, confidentiality, and rectification of health data, alongside the right to nominate, confirm, access and restrict health data (Nathani 2023)). However, the DPDPA 2023 lacks a definition for sensitive personal data, permits data processing without explicit consent, does not grant data principals ownership rights or the ability to restrict, object, or seek compensation, and fails to mandate the use of health data solely in the principal’s best interest or ensure privacy by design (Ibid).

To provide a framework tailored to medical ethics, the Indian Council of Medical Research (ICMR) released the Ethical Guidelines for Application of Artificial Intelligence in Biomedical Research and Healthcare (EGAAIBRH) in 2023. The guidelines advocate four ethical tenets for medical research in India, namely autonomy, beneficence, non-malfeasance, and distributive justice to ensure the protection of the rights, dignity, well-being, and safety of the community and the participants. The guidelines advocate giving complete control to humans over AI systems with patients holding autonomy and the right to refusal of AI decisions (EGAAIBRH 2023). AI technologies in healthcare must be rigorously designed, tested, and monitored to ensure safety, security, and ethical compliance. This includes continuous risk assessment, robust data protection, ethical oversight, and measures to prevent discrimination and stigmatization, especially for vulnerable populations. Moreover, ICMR recommends making the healthcare AI systems lawful, reliable from technical and social perspectives, explainable based on scientific plausibility, and transparent to stakeholders about the development and deployment. It specifies that privacy and personal data protection during AI development and deployment must be ensured by giving users control over their health data and implementing security measures to safeguard biometric data, with manufacturers responsible for preventing re-identification and leakage of identifiable information (Ibid). It outlines recommendations for AI technologies to undergo regular audits to ensure optimal performance, with data rigorously validated and free from biases. AI systems in healthcare must be inclusive, accessible, and based on datasets that accurately represent the population. Additionally, sector-specific guidelines for the development, validation, and deployment of medical AI systems in India are outlined. However, these remain guidelines and India needs a statutory framework and legislation tailored to the healthcare sector’s unique needs and challenges (Ibid).

4.3 Deepfake mitigation

The menace of deepfaked content has emerged across political and social circles in India. Using generative AI technology, malicious actors had created deepfake videos of eminent business personalities of India to offer spurious investment advice, while a deepfaked video of a famous Indian actress was morphed onto another person’s body to gain undue traction on social media (Basu and Manda 2024). In 2020, a political leader’s deepfake video was released showing him spreading misinformation on a controversial bill (Jha and Jain 2023). Such malicious content can spread misinformation, influence public opinion, and incite violence. However, India currently lacks specific laws addressing deepfaked content. The closest relevant provisions are Sections 66D and 66E of the Information Technology Act, 2000, which penalize cheating by impersonation and the unauthorized publication or transmission of private images. Additionally, Sections 67, 67A, and 67B of the IT Act prohibit and punish the dissemination of obscene or sexually explicit material (Basu and Manda 2024). However, these measures are insufficient to effectively identify and prevent the circulation of harmful deepfaked content. In November 2023, the Government of India issued an advisory to social media intermediaries to identify and act on deepfaked content. The advisory mandates that intermediaries exercise due diligence to identify and remove such content, ensuring compliance with rules and user agreements. Reported content, including deepfakes, must be removed within 36 hours, with failure to comply potentially, resulting in charges under Rule 7 of the IT Rules, 2021, and relevant sections of the Indian Penal Code (Ibid). Nonetheless, the legislations need to grow more stringent to safeguard the citizens’ right to privacy. The Supreme Court of India emphasized that the Right to Privacy implicitly includes an individual’s ability to control what personal information is disclosed in the public domain (Jha and Jain 2023). Individuals are regarded as the primary decision-makers regarding the release of their personal information.

Moreover, under Rule 3 of the Information Technology (Intermediary Guidelines) Rules, 2011, intermediaries are required to exercise due diligence and publish rules and privacy policies (Jha and Jain 2023). However, while the rule advises users against displaying content they do not have the rights to, it lacks mechanisms to effectively identify and address deepfake content, limiting its efficacy. India’s tryst with stringent legislation against the unethical use of generative AI to create deepfakes requires greater efforts by the government, technology platform companies, and civil society.

5 Conclusion

The analysis presented in the article indicates that India has significant opportunities for economic benefit in areas like education, healthcare, mobility, and agriculture and that the country is pushing forward in AI-related areas substantially. Nonetheless, efforts to cushion the disruptions in the job sector, infrastructure, and ethical paradigms that emerge from using AI in the industry are currently lacking. Thus, though AI can help foster increased productivity and high levels of growth in different sectors of the Indian economy, its use is likely to intensify the inequalities present within society, which requires government intervention to contain any possible damage. The NSAI also promotes AI as a way to help deal with some of the major social problems, ranging from poor healthcare access and educational inequities to poor standards of living and low productivity in the economy. Experimentation with use cases of AI seems to come with increased monitoring of individuals’ behavior, with governance extending into the realm of health monitoring, crowd surveillance, and possible erosion of privacy.

The analysis presented also highlights that India holds substantial economic opportunities in sectors such as education, healthcare, mobility, and agriculture, where AI advancements are being vigorously pursued. However, the efforts to mitigate disruptions in employment, infrastructure, and ethical considerations resulting from AI adoption remain inadequate. While AI promises enhanced productivity and economic growth across various sectors, its deployment risks exacerbating existing social inequalities, necessitating governmental intervention to prevent potential harm. The NSAI advocates using AI to address significant social challenges, including healthcare access, educational disparities, and economic inefficiencies. However, AI’s integration into these areas raises concerns about increased surveillance, behavioral monitoring, and potential privacy erosion. These developments underscore the need for a balanced approach to AI implementation that considers both the benefits and the ethical implications. India’s approach documents on responsible AI also outline the principles that can guide the development of inclusive, responsible, and ethical AI systems.

Ethics is a pivotal component of India’s policy initiatives concerning AI. The NSAI articulates a clear intention to establish ethical norms and standards; however, these efforts are currently nascent and primarily focused on broad, high-level principles. An examination of existing Indian approaches and ongoing debates in areas such as privacy, medical ethics, and deepfakes reveals the need for more comprehensive frameworks to effectively regulate AI in the country. Although recent guidelines and legislation on privacy appear robust on the surface, they fall short of the standards set by the GDPR. Closer scrutiny exposes significant loopholes and exceptions that allow the government to circumvent privacy protections, resulting in potential infringements on individuals’ fundamental rights due to the government’s unchecked authority over mass surveillance.

India occupies a pivotal position in the global discourse surrounding the development and governance of AI. A thorough understanding of India’s domestic needs, cultural complexities, socioeconomic disparities, international aspirations, and ethical considerations is crucial in shaping its AI policies. Analyzing India’s approach to AI through the lens of the Global South, rather than from a Western-centric perspective, offers a more contextual and relevant understanding of its strategies. A nuanced exploration of the structural, cultural, and political factors that influence India’s stance on AI, alongside its potential trajectory for AI governance, is essential. Such an analysis could position India as a model for policy development and governance, setting a precedent for other countries in the Global South to follow. This approach highlights India’s potential to serve as a testing ground for AI policies that are both contextually grounded and globally impactful.

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