Artificial Intelligence: Impact and Governance

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Published on March 11, 2024

Executive Note

The Takshashila Institution organised a conference on 14th February 2024 to understand Artificial Intelligence's impact and governance. Takshashila's in-house scholars, Satya S Sahu, Shrikrishna Upadhyaya, Sridhar Krishna, Bharat Sharma, Saurabh Todi and  Bharath Reddy, presented papers covering diverse themes related to Artificial Intelligence. Shambhavi Naik chaired the conference. This document is a compendium of the four working papers presented at the conference. 

In the first paper, Satya S Sahu and Shrikrishna Upadhyaya comprehensively analyse India's AI regulatory framework, emphasising the diverse policies, frameworks, and guidelines that contribute to its structure. They also analyse AI regulations in various jurisdictions, such as the US, UK, EU, and China, and identify convergent and divergent trends. The analysis reveals how varying priorities, strategies, and stages of legislative development shape the landscape.

In his paper, Sridhar Krishna assesses the legitimacy of concerns about Artificial Intelligence displacing jobs on a large scale. After reviewing relevant literature on the subject, he advocates for proactive measures, suggesting that preparing for the future involves staying ahead of the AI wave. He emphasises the importance of focusing on tasks that AI cannot perform and adapting to new endeavours when AI achieves significant breakthroughs.

Bharat Sharma and Saurabh Todi analyse the current global governance landscape concerning key technologies and identify valuable lessons applicable to the governance of artificial intelligence. Their analysis indicates the need for a nimble and recommendatory approach towards AI, allowing room for regulations to evolve into more direct, targeted, and binding measures as the technology matures.

In the concluding paper, Bharath Reddy explores how Artificial Intelligence can augment state capacity. The paper breaks down government processes and assesses them based on transaction volume and discretion. The paper identifies specific areas where AI systems can effectively augment state capacity through this analysis.

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