• UGI USER
  • Dec 19, 2023
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REGULATING ARTIFICIAL INTELLIGENCE: BUILDING REGULATORY CAPABILITIES

Why in the News?

  • US, EU, and UK have taken significant steps in regulating AI.
  • Agreements, declarations, and global summits highlight the need for AI trust and safety.
Source: PixelPlex

What is Artificial Intelligence?

Artificial Intelligence (AI) refers to the development of computer systems capable of performing tasks that typically require human intelligence. This includes learning, problem-solving, language understanding, and adapting to new information, enhancing machines’ ability to emulate and automate intelligent behaviour.

Current AI Applications:

  • Banks and credit card companies using AI for fraud detection and risk assessment.
  • E-commerce employing AI for credit risk prediction and personalized services.
  • Indian insurance industry adopting AI for risk management.

Scope and Impact of AI:

  • Artificial Intelligence’s transformative potential in sectors like banking, telecommunications, and insurance necessitates a revaluation of regulatory capabilities.
  • Generative AI products show vast applicability and a rapid enhancement in service quality.
  • AI adoption may alter professional practices, including bookkeeping, accounting, and legal contracts.
  • Professional bodies maintaining norms and practices may see a shift.

Challenges :

  • Rapid evolution of AI requires regulatory skill-building.
  • Governments, especially regulatory agencies, must match the pace of new risks from AI technology.
  • Transitioning from an analog to a digital state demands a new set of capabilities.
  • The challenge lies in developing the capability to build regulatory capabilities in AI.
  • Sole reliance on private sector incentives for regulation in critical sectors like banking and insurance is inadequate.

Regulatory Agencies’ Response:

  • RBI and SEBI developing AI tools for regulatory supervision.
  • Need for regulators to prepare for potential transformative changes caused by AI.

Way Forward:

  • Regulatory agencies need nimble skills to implement and evaluate AI regulations.
  • Exploration of algorithmic auditing for AI models’ lifecycle understanding.
  • Regulators need the capability to understand and evaluate algorithmic auditing and disclosure.
  • Deep thinking required for systemic capability building.
  • External collaborations with firms like McKinsey and Accenture for advanced analytics.
  • Effective regulation facilitates market acceptance of AI products and services.