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30 June 2026

Data integrity in AI

The topic covers significant institutional reforms by MoSPI and national policy directions on AI governance, which are directly relevant to governance, transparency, and data-driven policy-making in the UPSC syllabus.

1 min read 2 questions 1 prelims

Notes

  • Principal Secretary to the PM emphasized that AI-ready datasets require robust standards of quality, privacy, and transparency to be effective national assets.
  • Governance frameworks for AI adoption must address critical challenges including algorithmic bias, accountability, and explainability.
  • The Ministry of Statistics and Programme Implementation (MoSPI) has initiated comprehensive reforms to address outdated datasets, dissemination delays, and fragmented statistical architecture.
  • MoSPI has accepted 216 recommendations for time-bound implementation under an institutional oversight mechanism.
  • Recent statistical reforms include the introduction of the Producer Price Index (PPI) and the Index of Services Production.
  • Updated series for national accounts, Index of Industrial Production (IIP), Consumer Price Index (CPI), and Wholesale Price Index (WPI) have been released since February.

Questions

  1. Discuss the significance of robust governance frameworks in ensuring the integrity and reliability of AI-ready datasets for effective policymaking in India. 150 words
    Attempt this — 150 words in 8 min
    0 / 150 words 8:00
  2. The integration of AI into the national statistical system necessitates a shift towards modern data architecture. Elaborate on the challenges faced by traditional statistical systems and the role of institutional reforms in enhancing data quality and dissemination. 250 words
    Attempt this — 250 words in 11 min
    0 / 250 words 11:00

Prelims

  1. Which of the following indices has been recently introduced as part of the statistical reforms by the Ministry of Statistics and Programme Implementation (MoSPI)?

Syllabus

Topics

Sources

250words.in · https://250words.in/daily/2026-06-30/data-integrity-in-ai