THE FIVE PILLARS OF AI GOVERNANCE IN INDIA

THE US experiences with unregulated growth in social media and finance illustrate the dangers of delayed regulation.

- ADITYA SINHA

Social media platforms initially expanded without oversight, embedding practices that later heightened misinformation and privacy issues, while unchecked financial innovation led to the 2008 crisis, necessitating reactive measures like the Dodd-Frank Act.

These examples underline the value of ex-ante regulations—proactive rules to mitigate risks before they mature. Policymakers must rely on theoretical frameworks to anticipate risks and close regulatory gaps in regulating a complex field like AI.

R. Baldwin contended that regulation must incorporate adaptive mechanisms in rapidly evolving sectors, enabling iterative updates as knowledge and technology develop. This aligns with K.L. Hood's concept of 'regulatory intelligence,' which encourages regulators to monitor the sector continuously. Finally, David Collingridge's dilemma—the notion that early intervention is difficult due to insufficient information, while later intervention is difficult due to entrenchment—underlines the importance of scalable, flexible regulatory structures that evolve alongside the sector.

There should be five pillars of AI regulations in India. The first would be the precautionary principle and ethical guardrails. India could take cues from the tiered risk framework in the EU AI Act. But it should be tweaked. Risk in AI is inherently dynamic. Therefore, a robust regulatory framework should incorporate adaptive risk categorization, where AI applications are periodically reassessed as new information and technologies emerge.