India is adopting AI at enormous scale, but this study argues that the hardest problem is not simply building better systems. It is identifying whose interests those systems serve, and who may be left out. The impact of artificial intelligence can be seen as inevitable, and it is already being felt in numerous ways by citizens globally.

In India, fifty-seven percent of organisations reported having deployed AI, while twenty-seven percent reported exploring AI. AI is often deployed with the intention of improving access, quality, and efficiency, but risks and challenges have also emerged across different areas.

At the same time, AI continues to advance rapidly, while the discourse on AI ethics and governance evolves. Globally, an increasing number of sets of AI ethics principles have been put forward by organisations, private sectors, and nation states. The study asks what that conversation means in the Indian context.

Much of the current discourse on AI in India is government led and predominantly economically focused, considering mainly the fiscal benefits that this technology offers India. There is less discussion of the social and ethical issues generated by the development, deployment, and adoption of AI in India presently.

Those issues include privacy, data protection, job displacement, historical bias, and discrimination. Even well-intentioned algorithmic systems can have disproportionate consequences for vulnerable and marginalised communities, and that potential is left unconsidered.

The research seeks to drive forward debate on socially beneficial and ethically sound AI in India by considering the current social and ethical risks and challenges identified by AI experts in India. It uses a responsible research and innovation framework to explore possibilities for tackling those challenges.

The aim is therefore not only to identify risks, but to examine how they might be addressed. The paper focuses on the AREA framework. AREA is an acronym for Anticipate, Reflect, Engage, and Act. Anticipate gives an opportunity to address the possible implications of the proposed research, while Reflect is an opportunity to think critically about current methodology and research practice.

Engage enables the development and embedding of a communication strategy for the research, while Act looks to develop a strategy for acting on information arising from engaging with stakeholders. Together, these four processes or steps help put research in the context of responsible innovation.

The emphasis and origin of responsible research and innovation have largely been in the Global North. Reference to what responsible research and innovation, or responsible-research-and-innovation-like practices, mean in the context of the Global South is limited.

RRI has largely originated in and focused on the Global North, while discussion of what RRI or similar practices mean in the Global South remains limited. The research focuses on the challenges of AI at the societal level. To investigate these challenges clearly from the ground up, a focus group methodology was adopted.

Focus groups are an informal discussion among selected individuals about specific topics, designed to explore people’s attitudes, opinions, knowledge, or beliefs. They also enable the development of collective understandings of shared problems and, often, solutions to those problems.

Four focus groups were conducted between November twenty-twenty and February twenty-twenty-one. A total of thirteen participants took part, with between three and four participants in each focus group. Participants were able to select which focus group they wished to participate in, allowing the maximum number of participants to take part.

This allowed interactions to develop between a heterogeneous group of participants. The facilitators adopted a semi-structured approach to the focus groups, with six predetermined questions asked of participants. Follow-up questions were asked when the facilitator felt more information could be pertinent and to encourage group discussion.

All participants were encouraged to engage with each question, although a response to every question was not required from each participant. The focus group manuscript was professionally transcribed, and the transcripts were analysed using NVivo Pro twelve.

A combined approach to coding was adopted because the global nature of the focus groups was expected to require a more flexible approach. A preliminary round of data analysis was conducted by a core group of researchers on a sample of transcripts to determine an overarching framework for coding based on key, identifiable themes.

The broad framework of thematic nodes and subnodes was designed to be added to and adapted as each additional focus group transcript was coded. The responses were considered within the context of an RRI framework: whether an RRI approach was potentially lacking in relation to identified challenges, and whether adopting an RRI approach could address those challenges.

Participants reflected on how AI is or will be used in India given its current social, cultural, and political condition. The political landscape was a primary source of discussion, including political misinformation campaigns and ongoing bureaucracy as factors affecting the development and use of AI in India.

Participants raised concerns about the lack of existing legislation in India to protect individual privacy in the context of AI. They argued that a lack of data protection law leaves individuals without a say in how their data is used. Western big tech companies were framed as exploiting legislative loopholes to gain access to large personal datasets irrespective of people’s choice.

This was characterised as a colonisation of technology and as a systemic, unilateral, and top-down approach that fails to centre on Indian citizens’ needs. Many responses about globalisation broadly considered aspects of technological colonialism.

Participants highlighted language as a source of potential inequalities. The default language for AI systems is English, and most AI applications adopt an English-first approach. Given India’s diversity of languages and dialects, from twenty-two to seven hundred, this status quo is considered to exclude groups of potential users based on a lack of English language skills.

The influence of big Western tech companies was also seen as shaping technological and regulatory standards through adherence to the companies’ existing regulations. Privacy and data protection, as well as associated data breaches, came up in the focus groups a number of times.

The Indian COVID-nineteen app was used by over a third of participants as an example of the impact of these issues. Similar issues were identified in applications used in climate, water, and education. Discussions also focused on the current lack of legislation in this area.

Some participants suggested that an Indian version of GDPR could legislate for the protection of people’s personal data and help build trust around data collection and sharing in AI systems developed and deployed in India. One of the most commonly identified ethical issues around AI was a lack of transparency.

This included opacity in how the system operates or makes decisions, commonly referred to as a black-box system, and opacity about the impact the system will have for people in India. The issue was considered across two vectors. First, systems could be deployed with no assessment of their impact on citizens, discussed primarily in relation to automation and job displacement.

The focus groups produced key themes and narratives highlighting the need for responsible-research-and-innovation practices across several areas to support India in promoting AI’s benefits and mitigating its risks. The areas in which RRI could offer scope to improve this outlook include education, policy and governance, legislation and regulation, and innovation and industry practices.

RRI offers scope to identify and consider these challenges, while the AREA framework can support the development of ways to address them. The paper adopted the Anticipation, Reflection, Engagement, Action framework to explore how RRI could address the social and ethical issues and challenges raised by focus group participants.

Figure one maps the key concepts emerging from the India focus groups into opportunities and concerns around artificial intelligence. It highlights responsible or ethical governance, India’s varied AI landscape, major initiatives, and demand for “Made in India” technologies, alongside limited awareness, insufficient research and funding, education and skills needs, and ethical issues involving privacy, bias, and social inequality.

This matters because the discussion links AI’s societal challenges in India with possible areas for responsible research and innovation. Table one lists the thirteen respondents and the standardized codes used to identify them. Each code records the focus group and participant number, followed by descriptors for organization type, disciplinary background where applicable, and gender; for example, R1 is coded as FG1 P2_[PRO-H&SS-f].

The authors explain that these pseudonymized labels replaced names and generic speaker labels in the transcripts, supporting participant privacy while preserving relevant demographic context. The mini focus group approach has an inherent issue of small sample size, so the views expressed do not necessarily represent the whole Indian population.

Future research could expand this by using a larger sample. The sample focused on people with existing knowledge and experience of AI and its associated issues. That approach allowed a broad overview, but gave primacy to academic and industry schools of thought.

Future studies could consider a wider range of stakeholders to ensure that a plurality of experiences and views are represented. Because sampling was voluntary, the sample was skewed towards people already knowledgeable about and interested in social and ethical issues related to AI.

A more robust sampling method could ensure a broad spectrum of views in follow-up research. The study’s central message is that responsible AI in India requires more than principles on paper: it needs education, governance, regulation, industry practice, and wider stakeholder engagement grounded in local conditions.