Artificial Intelligence & Ethics under Indian Law

The rapid integration of Artificial Intelligence (AI) into everyday life presents both significant opportunities and profound ethical challenges. This paper examines four interconnected ethical dimensions of AI—data privacy, algorithmic bias, transparency deficits, and accountability gaps—within the framework of Indian law, drawing exclusively on secondary sources including published research, government policy documents, and institutional reports. AI systems routinely process personal data without meaningful informed consent, perpetuate historical biases against marginalised communities in high-stakes domains such as credit assessment and law enforcement, and produce opaque decisions that obstruct individual redress and institutional accountability. India’s existing legal framework offers a partial but insufficient response: the constitutional right to privacy (Justice K.S. Puttaswamy v. Union of India, 2017), equality guarantees under Articles 14 and 15, and the Digital Personal Data Protection Act, 2023, provide normative foundations yet fall short of a comprehensive AI-specific regime, notably lacking mandatory bias audits, explainability requirements, and a clear liability framework. Drawing measured lessons from the European Union’s AI Act while recognising India’s unique demographic scale and developmental imperatives, the paper concludes that India urgently requires a dedicated AI governance statute, an independent multi-disciplinary regulatory authority, and sustained investment in digital literacy to ensure that AI deployment remains ethical, transparent, and equitable.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-27
DOI
https://doi.org/10.5281/zenodo.20406978
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence & Ethics under Indian Law

Mohammad Jaweed, Prakash Bhatt, Prof. Gunjan Jain, Nida Naseer L et al.
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

Artificial Intelligence & Ethics under Indian Law

Mohammad Jaweed, Prakash Bhatt, Prof. Gunjan Jain, Nida Naseer L, Noor Fathima
article en

Abstract

The rapid integration of Artificial Intelligence (AI) into everyday life presents both significant opportunities and profound ethical challenges. This paper examines four interconnected ethical dimensions of AI—data privacy, algorithmic bias, transparency deficits, and accountability gaps—within the framework of Indian law, drawing exclusively on secondary sources including published research, government policy documents, and institutional reports. AI systems routinely process personal data without meaningful informed consent, perpetuate historical biases against marginalised communities in high-stakes domains such as credit assessment and law enforcement, and produce opaque decisions that obstruct individual redress and institutional accountability. India’s existing legal framework offers a partial but insufficient response: the constitutional right to privacy (Justice K.S. Puttaswamy v. Union of India, 2017), equality guarantees under Articles 14 and 15, and the Digital Personal Data Protection Act, 2023, provide normative foundations yet fall short of a comprehensive AI-specific regime, notably lacking mandatory bias audits, explainability requirements, and a clear liability framework. Drawing measured lessons from the European Union’s AI Act while recognising India’s unique demographic scale and developmental imperatives, the paper concludes that India urgently requires a dedicated AI governance statute, an independent multi-disciplinary regulatory authority, and sustained investment in digital literacy to ensure that AI deployment remains ethical, transparent, and equitable.

Zenodo (CERN European Organization for Nuclear Research)
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Ethics and Social Impacts of AI
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Artificial Intelligence & Ethics under Indian Law — Mohammad Jaweed, Prakash Bhatt, et al. · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS