Technical Dimension
The standardisation process involves technical benchmarking that tracks the evolution of AI technologies and their various use cases, ensuring systems meet specific performance and quality criteria.

 

Legal-Ethical Framework
Standards incorporate legal and ethical considerations, including privacy, fairness, and data quality principles, while avoiding rigid or absolutist implementation that could hinder market innovation.

While data and machine learning algorithms are governed with different culturally amenable policy contexts, India's AI landscape is still emerging, and nascent. 

​

India has the potential to contribute to the Indo-Pacific, and the Global South, not as an 'AI Garage' but as an AI Forge.

​

Regulatory capture, subterfuge and arbitrage haunt current and future AI builders, at every stage, maybe because of incumbent market hype cycles, technology bubbles, lack of technical clarity, unstable use cases, and anticompetitive practices. 

​

This is why AI standardisation is needed, to achieve pre-regulatory standards, which could be legal, technical, or domain / sector-specific.