Stories
India Needs Own AI Safety Framework, Not US Copy: Experts
- •Industry experts advocate for India to develop its own AI safety framework tailored to its languages, scale, and application-led AI ecosystem, rather than replicating the US voluntary pact.
- •The call comes after the White House Accord on Super Intelligence, a voluntary AI safety pact signed by Donald Trump and top tech executives, focusing on internal controls and oversight.
- •Nasscom Chairman Srikanth Velamakanni emphasizes that India should not simply copy the US model but build a domestic commitment as a foundation for global AI safety understanding.
- •Experts suggest a hybrid approach combining mandatory rules for critical sectors like defense and healthcare with voluntary standards for rapidly evolving technical areas.
- •Key concerns for India include deepfakes, cyberattacks targeting financial institutions, misinformation, and political manipulation, with calls for independent audits and accountability for AI-related risks.
Key Questions Answered
Experts argue that India needs an artificial intelligence safety framework uniquely tailored to its scale, diverse vernacular languages, cultural contexts, and application-led ecosystem rather than replicating a voluntary pact designed for the US. Building a domestic framework allows India to address specific regional priorities while advancing a trusted AI environment.
Signed on September 29, the voluntary US accord commits top technology executives and companies to implementing internal controls, dedicated oversight teams, independent external audits, and regular meetings to establish safety standards. However, the agreement relies largely on self-policing and carries no legal force.
Experts highlight deepfakes, large-scale cyberattacks, financial fraud, misinformation, and political manipulation as critical and immediate threats to India's social fabric and financial institutions. Other prioritized concerns include preventing systemic harms, safeguarding data privacy, addressing job impacts, and ensuring clear accountability when AI systems fail.
