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The Reserve Bank of India (RBI) has released the Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) Committee Report, marking a major step in shaping ethical, transparent, and sustainable AI adoption in India’s financial sector. The framework seeks to balance innovation with risk mitigation, ensuring that the transformative power of AI is harnessed without compromising trust, fairness, or safety.

RBI’s 7 Sutras for Responsible AI in Finance

The FREE-AI framework is built on seven guiding principles (Sutras):

1. Trust is the Foundation – AI must be reliable, transparent, and inspire public confidence.
2. People First – AI should empower human decision-making while safeguarding dignity, inclusion, and citizen interest.
3. Innovation over Restraint – Encourage responsible innovation without excessive restrictions.
4. Fairness and Equity – AI outcomes must be unbiased and equitable.
5. Accountability – Responsibility for AI decisions rests with deploying entities, with clear lines of answerability.
6. Understandable by Design – Systems must be interpretable and explainable to users, auditors, and regulators.
7. Safety, Resilience, and Sustainability – AI must be secure, adaptable, and capable of delivering long-term benefits.

India’s Policy Developments

• MuleHunter AI – Developed by RBI Innovation Hub to detect mule accounts and curb digital frauds.
• Digital Lending Rules – Mandate auditable AI-driven credit assessments with human oversight and grievance redressal.
• SEBI’s 2025 Guidelines – Propose responsible AI use in Indian securities markets.
• IndiaAI Mission – Aims to boost AI innovation, research, and computational infrastructure.

RBI’s Recommendations under FREE-AI

The Committee laid down 26 recommendations across six pillars:

1. Infrastructure – Establish high-quality financial data infrastructure, integrated with AI Kosh.
2. Innovation Enablement – Create an AI Innovation Sandbox for testing models with anonymised data, ensuring compliance with AML, KYC, and consumer protection norms.
3. Consumer Protection & Security – Periodic AI red-teaming, incident reporting frameworks, and good-faith disclosures.
4. Capacity Building – Structured AI governance training at all institutional levels; knowledge sharing across REs (regulated entities).
5. Governance – Oversight frameworks ensuring accountability and transparency in AI deployments.
6. Assurance Mechanisms – Standards and audit processes for AI-based systems.