Artificial Intelligence (AI) now underpins tools for learning, governance, and trade, and it is central to India’s aim of inclusive development that respects dignity, rights, and security for over 1.4 billion people. These systems can uplift when designed well, but they can also exclude or misjudge if trained on narrow datasets or deployed without safeguards, especially across India’s many languages and identities, where context is essential for fairness and accuracy.
When a speech model misreads a dialect or a facial system underperforms on under‑represented communities, the harm is social as well as technical, which is why fairness, explainability, and human oversight are practical necessities rather than optional ideals. This piece blends global scholarship, Indian constitutional values, and concrete steps so that AI serves everyone, and invites citizens, innovators, and policymakers to consider how their choices will shape India’s trusted digital future.
Foreword: The Human Face of AI in India
AI should widen opportunity and protect people, not entrench bias or reduce lives to data points, so design and deployment must reflect India’s diversity in language, caste, region, and culture from the very start.
The same tool that helps a farmer decide when to sow or a clinician to flag early disease can harm a borrower or a student if it ignores dialects, skin tones, or local realities, underscoring why responsibility for outcomes must remain clearly and accountably human.
This article translates values such as dharma (duty) and nyāya (justice) into specific choices on data, models, and governance, what to measure, how to explain, and where to set boundaries, so innovation lifts the many without harming the few.
Why Ethics Matters in AI–Now More Than Ever
AI now sits in phones, hospitals and classrooms, shaping access to services and life chances at scale, which means trust depends on systems that are fair, explainable, and lawful in practice as well as on paper.
A single flawed model in welfare, hiring or lending can affect crores in minutes, so accountability, human oversight, and routes to challenge matter as much as accuracy and speed, particularly for public functions or essential services.
Global rulemaking, from bans on clearly harmful uses to transparency for powerful models and risk management for high-impact systems, offers a shared grammar that India can align with while tailoring to its multilingual and federal context.
Ethical guardrails give effect to constitutional principles of equality and non-arbitrariness, ensuring that new tools serve public interest without undermining due process or dignity, especially for those most at risk of exclusion.
Principles from Scholarship: The Ethical Spine of AI
Human accountability is non-negotiable because only people and organisations can be answerable for outcomes, so every consequential AI‑assisted decision must trace to a responsible decision‑maker who can provide reasons and remedies.
Fairness requires context because datasets mirror society’s inequalities, so diverse data, ethical audits, and independent checks are essential in a multicultural setting to avoid embedding or amplifying bias in language, vision, or decision systems.
Legitimacy flows from explainability and auditability, not from full transparency alone, so robust logs, records, and post‑hoc explanations enable redress and learning without exposing sensitive data or proprietary information.
These ideas resonate with Indian values, dharma (duty), nyāya (justice), and sarvodaya (welfare of all), and translate into practical steps such as stress‑testing models on rural and urban data, auditing for regional bias, and documenting decisions to enable appeals and corrections.
The Global Rulebook: Lessons for India’s AI Ambition
India can help shape global norms by aligning with frameworks that prohibit clearly harmful practices, require transparency from general-purpose models, and mandate risk controls for high-impact systems while preserving room for innovation and local nuance. The EU’s approach mixes prohibitions, duties, codes of practice, and sandboxes[i]; it offers templates India can adapt to language diversity and federal coordination across states and sectors without stifling experimentation. Risk-based governance complements India’s data‑protection and consumer‑protection laws, while shared tools like model documentation, watermarking, and incident reporting foster interoperability with partners and markets. International cooperation, through safety summits, standards bodies and research exchanges, can strengthen domestic institutions and help ensure models serve people equitably across languages and regions.
India’s AI Governance: Building a Responsible Ecosystem
India’s national mission couples compute capacity, open datasets and innovation hubs with guardrails such as consent, security, explainability and redress, so scale is matched by safety and accountability in practice. Programmes like IndiaAI‑Compute[ii], the AIKosh datasets platform[iii] and national challenges aim to extend participation beyond metros, while curated Indian‑language datasets help reduce bias and improve relevance for public services and markets.
Data protection rules, deepfake advisories, and cybersecurity guidance are converging on expectations for labelling, takedown, provenance and detection, tying technical practice to lawful use and rapid response across platforms and agencies.
Standards for AI management and risk provide agencies and vendors with practical frameworks for assurance, documentation, and audits throughout the lifecycle, including in procurement and oversight.
The Reserve Bank of India’s report on the Framework for Responsible and Ethical Enablement of AI (FREE‑AI) sets out seven “Sutras”—trust, people‑first, innovation over restraint, fairness, accountability, understandability by design, and safety, supported by six pillars and 26 recommendations to balance innovation with risk in the financial sector.[iv]
Released on 13 August 2025, it proposes practical measures such as an AI innovation sandbox, indigenous financial AI models, risk‑based audits, incident reporting, and board‑approved AI policies for regulated entities to ensure trustworthy, explainable, and inclusive deployment of AI in finance.[v]
State-Level AI Policies: Complementing National Efforts
States are translating national aims into local programmes for health, agriculture, education, and skills, ensuring AI improves services where people live and work rather than staying confined to pilots or proof‑of‑concepts.
- Tamil Nadu’s Safe & Ethical AI policy (G.O. Ms. No.25, 2024) operationalises evaluation through TAM‑DEF/DEEP‑MAX‑style scoring, procurement guidance and a monitoring committee to keep systems fair, transparent and accountable across departments.[iv]
- Karnataka is launching a state‑level AI Mission within its IT Policy 2025–2030 to promote sandboxes, incubators and workforce studies, pairing innovation with responsible governance and sector pilots across the state.[vii]
- Telangana’s TGDeX (2025) is a state‑run “AI‑ready” exchange that integrates high‑quality datasets, subsidised GPUs and open models to accelerate responsible AI development and scale public‑interest use cases.[viii]
- Maharashtra’s MahaAgri‑AI Policy 2025–29 funds AI, drones and robotics for farming, focusing on Marathi‑language advisories, traceability and weather‑linked decision support to lift productivity and resilience.[ix]
- Odisha’s AI Policy‑2025 establishes the Odisha AI Mission to drive adoption across healthcare, agriculture, education, disaster management and governance with an emphasis on infrastructure, skills and ethical deployment.[x]
- Uttar Pradesh’s AI Pragya and Lucknow “AI City” [xi] initiatives align compute, models, skills, and urban services to build a robust state-level ecosystem for AI innovation and employment.[xii]
- Andhra Pradesh is embedding AI and quantum into higher education via APSCHE’s new curriculum and faculty development, linking talent pipelines to research, industry, and public‑service innovation.[xiii]
Ethical Challenges for India’s Plural Society
AI must work across more than 1,600 languages [xiv]and multiple identities, which demands representative data, inclusive design, and evaluation that reflects real users across regions, dialects, and contexts. Explainability and redress are vital where AI influences access to services or rights, so documentation, model cards, and appeal routes[xv,xvi] should be built in from day one and proportionate to risk and impact. Generative systems raise deepfake risks, from fraud and reputational harm to gender‑based abuse, so provenance, watermarking, reporting channels and rapid takedown must be paired with victim-centred support and due process.[xvii,xviii]
Workforce impacts need planning so AI complements people and creates good jobs, with reskilling pathways that reach smaller towns and groups at risk of exclusion or displacement.
a) Fairness in a Diverse Nation
Fairness means AI should “see” every Indian, so datasets and tests must deliberately include dialects, regions, skin tones and contexts that mirror society, not only early adopters or urban users.
Platforms like AIKosh can reduce bias by curating high-quality, diverse datasets that developers and agencies can reuse, improving accuracy and inclusion for underserved groups in public and private services.[xix]
Skills and inclusion programmes broaden participation and yield feedback from users who are often left out of design decisions, which in turn improves performance, safety and trust. Public benchmarks that report performance across languages and communities make fairness measurable and actionable rather than a vague aspiration.[xx]
b) Explainability and Redress
People deserve timely, useful explanations for decisions that affect them, tailored to context-credit, benefits, healthcare, so they can challenge outcomes and obtain remedies if the system errs. Logs, records and audit trails enable oversight, help teams fix models and processes, and ensure that providers can identify and correct recurring errors or drift. Explainability can be risk-based, emphasising high‑impact uses such as welfare, health or credit where consequences are severe and errors harder to reverse, with proportionate documentation. Clear points of contact, service levels and escalation routes turn principles into practical protections for individuals and communities, including in regional languages.
c) Security and Deepfakes
Deepfakes exploit trust in familiar voices and images, so provenance tools, durable labels and rapid takedowns are essential to limit harm, prevent re‑uploads and preserve evidence for redress and enforcement.
Coordinated guidance from digital authorities and cybersecurity agencies helps organisations deploy detection tools, verify sources and protect users from scams and misinformation across platforms.[xxi,xxii] Victim‑centred processes, evidence capture, injunctions, de‑indexing and psychosocial support, should be standard, with fast lanes for gendered harms and election‑time abuse when risks to dignity and public order are highest.
Public awareness can teach people to spot and report synthetic media, reducing spread and improving response times by platforms and public agencies.[xxiii, xxiv]
d) The Future of Work
AI will create and reshape jobs, so policy should pair innovation with safety nets, training and pathways into new roles across cities and smaller towns to keep growth inclusive and broad‑based.[xxv] Skills councils, industry bodies and state missions can align curricula to practical roles, from model evaluation and prompt engineering to AI assurance and safety functions.
Workplace transitions should include workers’ voices to keep changes fair and sustainable, with targeted support for women and marginalised groups at risk of displacement. Measuring real outcomes, not only training counts, ensures programmes lead to employment, entrepreneurship, and better public services in practice.
Sector Spotlights: Opportunities with Ethical Guardrails
Health, agriculture, education, cities, and finance illustrate how AI can deliver real gains when privacy, fairness, explainability, and safety are designed in from the outset. Each deployment needs context-specific controls, clear data practices, and robust evaluation so users can trust systems, challenge errors, and obtain timely remedies.
Public-private partnerships can scale proven tools while protecting rights through standards, audits, and transparent procurement, especially where systems affect entitlements and livelihoods. Open, well-governed datasets in Indian languages boost accuracy and inclusion across sectors, amplifying local innovation and participation in smaller cities and rural areas.
a) Healthcare: Saving Lives with Care
AI supports screening, diagnosis, and adherence when consent, safety and explainability are built into clinical workflows and documentation rather than added later as an afterthought.[xxvi] Projects in women’s health, eye care and TB show how Indian datasets can improve performance if models are routinely audited and updated to avoid drift and bias over time.[xxvii]
State programmes that integrate AI should publish data‑use protocols, model limits and recourse for patients to maintain trust, including in local languages and accessible formats.
[xxviii]Clinician‑in‑the‑loop designs keep decisions human-centred and reviewable, balancing speed with accountability and patient dignity.[xxix]
b) Agriculture: Empowering Farmers
AI can improve yields, pricing, and risk management for over 100 million farmers, but tools must be affordable, language‑aware, and reliable in the field to be trusted and useful at scale[xxx]. Drones, satellites, and soil sensors enrich advisories when paired with clear consent, data rights, and dispute resolution rather than treating farmers as passive data sources.
[xxxi,xxxii]Common data platforms reduce duplication and improve quality, making services more consistent across districts and seasons in a changing climate.[xxxiii]
Farmer feedback loops align models with ground realities, not just lab assumptions, improving relevance, safety, and uptake over time.[xxxiv]
c) Education: Personalizing Learning
Adaptive tools can support teachers and learners if they respect privacy, avoid intrusive monitoring, and explain recommendations in ways students and families can understand.[xxxv] Restrictions on harmful practices like emotion inference in classrooms protect children’s rights and preserve trust in learning technologies at home and in school.[xxxvi] States embedding AI in curricula should teach ethics, safety, and critical thinking alongside coding and data, preparing learners for responsible AI use in daily life. [xxxvii]Accessible design, including language and disability inclusion, keeps learning tools fair and effective for all students, not only the most connected.[xxxviii]
d) Smart Cities: Balancing Efficiency and Privacy
AI can ease congestion and improve services, but blanket surveillance risks chilling effects, so deployments must be necessary, proportionate and subject to independent oversight.[xxxix] Procurement should require privacy‑by‑design, retention limits and audits to prevent function creep and protect rights in public spaces and transport systems.[xl]
Residents need transparency about what data is collected and why, plus channels to challenge misuse or error with timely responses and remedies.
[xli]Interoperable standards help cities learn from each other safely without exporting mistakes or creating vendor lock‑in that undermines accountability.[xlii]
e) Finance: Protecting Consumers
AI can detect fraud and widen inclusion, but deepfake scams show why provenance, strong authentication and rapid response matter as much as detection accuracy and model performance.[xliii] Financial regulators and platforms should coordinate on incident playbooks, disclosure and user education, with clear liability and redress where automated systems cause loss.[xliv] Model governance, testing for bias, drift, and robustness, protects consumers and institutions, and sustains confidence in digital finance through cycles of change.[xlv] Clear escalation paths with timelines and evidence standards make protections practical and accessible for individuals and small firms, not only large enterprises.[xlvi]
A Playbook for Ethical AI: Choose Your Path
Policymakers can set guardrails by adopting AI management and risk standards, banning clearly harmful practices, and funding sandboxes with public compute for safe, audited testing. Innovators can use curated datasets, document models, red‑team systems, and adopt watermarking and provenance for generated content to prevent misuse and aid accountability across platforms.[xlvii] Citizens can ask for explanations, keep records, and appeal harmful decisions, while reporting deepfakes or scams through the platform and public channels with preserved evidence.[xlviii]
Public procurement can mainstream ethics by requiring standards, audits, inclusive design and documented evaluation in every contract that deploys AI in public services.[xlix]
Law and Remedies: Ensuring Accountability Across Levels
The DPDP Act anchors consent and security, while sector rules and advisories set expectations for model risk, transparency and takedown of harmful content in line with due process. High‑impact systems should undergo impact assessments, and people must have routes to challenge, correct or contest decisions that significantly affect rights or entitlements. Courts have acted swiftly against deepfakes, issuing injunctions and directing platforms to remove synthetic abuse, with fast lanes for gendered harms and reputational violations.[l] States reinforce transparency and accountability through policies, standards and oversight aligned with national law to reduce fragmentation and raise the safety baseline.
India and the World: Leading with Values
India is shaping global AI governance through summits and standards, aiming for interoperable rules that reflect constitutional values and local realities across languages and states. Codes of practice, documentation templates, and shared benchmarks help align developers, platforms, and regulators across borders without suppressing local innovation or needs. Standards bodies offer practical tools for risk management and assurance that Indian agencies and firms can adopt at scale through procurement and regulation. International leadership strengthens India’s voice abroad while improving safety, rights, and trust at home across sectors and communities.
FAQ: Your Guide to AI Ethics
Does India regulate AI? Yes, through data‑protection law, IT rules and advisories that set duties for transparency, safety, and lawful use, especially for high-risk and generative systems.
Are there global standards? Yes, AI management and risk standards can be adopted in procurement and audits to lift the baseline for safety and fairness across sectors.
What if I see a deepfake? Report it promptly, preserve evidence, and use the platform and public channels for removal and legal action where needed, with victim support where harms recur.
How do state policies fit? They complement national law and should align with data protection, security, and fairness, using shared playbooks to avoid fragmentation and speed learning.
What remedies exist? Appeal through official portals or courts, request explanations and corrections, and seek injunctions in urgent cases with help from legal and civic organisations.
India’s 12-Point Commitment
- Human accountability: make impactful AI decisions traceable and contestable, with reasons and remedies published proportionate to risk and impact.[li]
- Fairness: stress‑test models and datasets for bias across languages, regions and identities, and publish benchmark summaries where appropriate to build trust.[lii]
- Transparency: maintain audit trails and documentation tailored to use and risk, enabling oversight, learning, and redress without exposing sensitive data.[liii]
- Privacy: embed consent, security, and data minimisation in design and operations, including governance for updates, retention, and data‑sharing.[liv]
- Safety: counter deepfakes and high-risk misuse with provenance, detection, rapid response, and victim-centred protocols and timelines.[lv]
- Boundaries: prohibit clearly harmful practices that undermine rights or dignity, and review lists periodically in light of evidence and experience.[lvi]
- Standards: adopt practical AI management and risk frameworks in procurement and audits to drive consistent assurance and accountability.[lvii]
- Datasets: invest in open, well-governed Indian‑language datasets with clear licences, quality checks, and documentation.[lviii]
- Audits: use independent evaluators, publish proportionate summaries, and fix issues found within defined timelines and responsibilities.[lix]
- Skilling: train broadly and inclusively across regions and roles with pathways into good jobs, especially for under‑represented groups.[lx]
- Innovation: run sandboxes with public compute and common data assets for safe, rapid learning with oversight and public value.[lxi]
- Dialogue: convene cross-state and cross-sector forums to keep policy responsive, evidence, and aligned to constitutional values.[lxii]
Conclusion: Shaping India’s AI Future with Integrity
India’s AI path must pair inclusive growth with ethical responsibility so that tools lift people up and protect rights in everyday life, from classrooms and clinics to farms and city streets. National missions, state policies, and standards can scale innovation while keeping consent, fairness, provenance, and redress at the core, building public trust across languages and communities. Courts, regulators, and platforms each have roles in countering deepfakes and other harms without chilling lawful speech or creativity, ensuring proportionate and reviewable action. With clear guardrails and shared values, India can lead in building AI that is trustworthy, interoperable, and aligned with dignity and justice for all.
References
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(xix) https://www.tandfonline.com/doi/full/10.1080/13600869.2024.2324540
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(xxix) https://agriwelfare.gov.in/Documents/DPR_Punjab.pdf
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(This article has been written by Tanmaya Nirmal, TAU, National e-Governance Division. For any comments or feedback, please write to tanmaya.nirmal@digitalindia.gov.in and negdcb@digitalindia.gov.in)
