The Regulator’s Challenge in the Age of AI

Why in News?

• There have been a lot of discussions and regulations surrounding artificial intelligence (AI) which require a critical focus on upcoming challenges, particularly the urgency for regulatory skill-building in the digital era.
• Recent global initiatives, such as the US executive order and the EU legislation on AI, underscore the need for effective regulation in managing the risks associated with evolving technology.

Recent Global Efforts to Regulate AI

• The world’s first ever AI Safety Summit (at Bletchley Park, UK)
  • 28 major countries including the US, China, Japan, the UK, France and India, and the EU agreed to sign on a declaration saying global action is needed to tackle the potential risks of AI.
  • The declaration incorporates an acknowledgment of the substantial risks from potential intentional misuse or unintended issues of control of frontier AI - especially cybersecurity, biotechnology, and disinformation risks.
• US President’s Executive Order: It aims to safeguard against threats posed by AI, and exert oversight over safety benchmarks used by companies to evaluate generative AI bots such as ChatGPT and Google Bard.
• G20 Leaders’ Summit in New Delhi
  • The Indian PM had called for a global framework on the expansion of “ethical” AI tools.
  • This shows a shift in New Delhi’s position from not considering any legal intervention on regulating AI in the country to a move in the direction of actively formulating regulations based on a “risk-based, user-harm” approach.
• GPAI (Global Partnership on Artificial Intelligence) Summit, Delhi
  • It is a multi-stakeholder initiative which aims to bridge the gap between theory and practice on AI by supporting cutting-edge research and applied activities on AI-related priorities.
  • It brings together engaged minds and expertise from science, industry, civil society, governments, international organisations, and academia to foster international cooperation.
  • 29 countries of the GPAI have unanimously adopted the GPAI New Delhi Declaration.
  • New Delhi Declaration promises to position GPAI at the front and centre of shaping the future of AI in terms of both innovation and creating collaborative AI between the partner nations.

An Analysis of Expanding Scope and Quality Improvements in AI

• Scope of Use
  • AI's deployment, exemplified by products like ChatGPT, indicates a broadening scope of applications, transcending sectors like banking, telecommunications, and insurance.
  • Current uses include fraud detection, risk assessment, digital marketing in banks, credit card companies, and e-commerce, with potential widespread adoption.
• Quality Improvement
  • AI's rapid improvement, as seen in the enhanced quality of services, signifies a growing potential for economic integration.
  • Examples include AI aiding risk management in the Indian insurance industry and its potential to reshape professional practices in accounting, law, and other fields.
  • While these are examples of limited use, AI usage may become much more prevalent soon as the technology improves and people start understanding how to use the technology better.

Regulatory Challenges of Growing Use of AI

• Deployment of Regulatory Tools
  • Regulatory bodies, such as the Reserve Bank of India and the Securities and Exchange Board of India, are initiating AI tools for regulatory supervision.
  • Since 2019, SEBI has required mutual funds to disclose the use of AI in their product offerings and product managements.
  • However, they and other regulators will need to do much more to prepare for potentially transformative changes.
  • Moreover, the transformation in professional practices, like AI-driven book-keeping and legal contracts, will necessitate adjustments in regulatory frameworks.
• Capability Building Challenges
  • Governments globally have laid down initial AI regulatory frameworks, but regulatory agencies must focus on building the capabilities to implement these frameworks effectively.
  • While these frameworks focus on the substance of AI regulation, regulatory agencies that have to implement these frameworks have to build the capabilities to implement them as these capabilities are not easy to build in-house. 

Potential Solutions for Regulatory Skill-Building Challenges

• Proactive and Quick Actions are Required
  • Regulatory agencies will have to be quick and proactive in order to acquire the necessary skills.
  • The RBI for example, reportedly, has signed a contract with McKinsey and Accenture to provide advanced analytics to help it discharge its supervisory functions.
  • Even if regulators can hire external firms and experts with the necessary skills, they will have to develop the capabilities to evaluate the inputs from these external resources.
• Development of the Capability to Understand Algorithmic Auditing
  • Regulators globally explore algorithmic auditing to understand AI models' lifecycles and potential problematic outcomes, requiring the development of auditing capabilities.
  • Algorithmic auditing is the audit of each part of a model’s lifecycle to gain a better understanding of how the model works, and whether its use leads to potentially problematic outcomes.
  • However, to make use of this practice, regulators will have to develop the capability to understand algorithmic auditing.
• Disclosure Requirements: Disclosure-related requirements are effective only if regulators possess the capabilities to evaluate the reported information accurately.
• Regulatory Intervention on Government Level
  • While market dynamics and consumer preferences may influence AI adoption, effective regulation remains crucial.
  • Effective regulation can facilitate market acceptance of AI products and services. 
  • Relying solely on private sector incentives may be inadequate, emphasising the need for regulatory intervention, particularly in critical sectors like banking and insurance.

Way Forward

• Coordinated Approach
  • The development of regulatory capabilities on a systemic scale necessitates deep thinking and a coordinated approach.
  • The central government must take on the responsibility of understanding and replicating the successful transition to a significantly digital state.
• Build Capabilities Systematically: The absence of a comprehensive body of knowledge on transitioning to a digital state requires a focused effort to build capabilities systematically.
• Sustained Government Involvement: The central challenge lies in developing the capacity to build capabilities, emphasising the need for sustained government involvement.

Conclusion

• In India's context, it is critical how rapidly AI capabilities can be produced at a mass scale; otherwise, development of such capacities will be unequal and ad hoc.
• Therefore, the imperative for regulatory skill-building emphasises the need for proactive government intervention, systemic development of capabilities, and strategic coordination.
• These efforts will effectively navigate the evolving landscape of technological advancements.

Q1) What is GPAI?

The Global Partnership on Artificial Intelligence (GPAI) is a multi-stakeholder initiative which aims to bridge the gap between theory and practice on AI by supporting cutting-edge research and applied activities on AI-related priorities. Built around a shared commitment to the OECD Recommendation on Artificial Intelligence, GPAI brings together engaged minds and expertise from science, industry, civil society, governments, international organisations and academia to foster international cooperation.

Q2) What is Generative AI?

Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data. The recent debate around generative AI has been driven by the simplicity of new user interfaces for creating high-quality text, graphics and videos in a matter of seconds.

Source: The Indian Express