Data Integrity: The Cornerstone of AI Success in India

The effectiveness of Artificial Intelligence in India hinges on the quality and organization of data rather than technology itself, according to MoSPI Secretary Saurabh Garg. At RICON, he highlighted the essential role of data readiness for AI, emphasizing the need for reliable, machine-readable, and well-structured data for successful AI implementation.

The fate of Artificial Intelligence in India is more closely tied to data quality than to technological advancements, according to Saurabh Garg, Secretary of the Ministry of Statistics and Programme Implementation. Speaking at the Responsible Intelligence Confluence, Garg highlighted that AI models could falter if they rely on poor or inconsistent data.

Garg pointed out that the crux of AI readiness lies in the quality of data and metadata rather than in the sophistication of the models themselves. Poorly formatted, low-quality data can undermine AI systems, leading potentially to critical errors in applications like government welfare programs. The importance of reliable and accessible data is therefore paramount.

To address this, MoSPI has implemented updated frameworks, national metadata structures, and statistical quality assessments to ensure data consistency and reliability. Garg emphasized that AI systems must rely on machine-readable and semantically clear data for trustworthiness and effectiveness.

(With inputs from agencies.)