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Building accountability through continuous and transparent Responsible AI assurance.
In brief
- Responsible AI 2.0 (RAI 2.0) moves from “trust us” to “show us,” demanding proof of ethical AI through monitoring and validation.
- Regulators are shifting from guidelines to mandatory requirements, including risk assessments and transparency measures.
- The RBI’s FREE-AI framework mandates board-approved AI policies to strengthen governance and consumer protection.
Responsible AI (RAI) has entered a new phase. The journey from RAI 1.0 to RAI 2.0 marks a move from high-level ethical intentions to measurable, verifiable and auditable systems. Responsible AI 2.0 emphasizes a “show us” approach where organizations must demonstrate ethical compliance through data logs, bias testing and ongoing performance audits. This evolution enhances accountability as GenAI and Agentic AI gain autonomy to write, decide and act independently.
Drivers of Responsible AI 2.0
The shift toward RAI 2.0 is shaped by three main forces:
Technological
velocity:
As autonomous systems
gain independence, safety
and accountability risks
increase.
Regulatory
shift:
Global frameworks like
the EU AI Act now
require audits,
documentation, and
governance for high-risk
systems.
Public
trust:
Ethical claims must now
be backed by measurable
evidence and transparent
assurance.
Responsible AI in India and governance initiatives
India is taking a leadership role through frameworks such as the Reserve Bank of India’s FREE-AI guidelines. This framework outlines seven core Responsible AI principles, including trust, fairness, accountability and human oversight. It mandates board-approved AI governance structures across financial institutions to enable transparency and risk management.
Additionally, the Responsible AI at the IndiaAI Mission is being operationalized through initiatives like “Fairness Passports,” which record data lineage, consent logs, bias testing and system performance over time. These evidence packs provide regulators and partners with verifiable assurance of ethical compliance.
Continuous assurance and the way forward
To meet the demands of Responsible AI 2.0, enterprises must adopt continuous assurance practices maintaining auditable controls, monitoring for model drift, documenting bias tests and preparing for third-party certification. Synthetic data is also emerging as a safe innovation tool, enabling testing without violating privacy.
The article is also contributed by Jatin Patni, Director, Risk Consulting, EY India.
Summary
The next stage of Responsible AI in India is about moving from policy to proof. Continuous validation, transparent governance and measurable accountability will define how organizations build public trust and AI serves both innovation and integrity in a regulated, data-driven world.
How EY can help
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