AI Governance in the U.S. vs India: What Enterprises Need to Know

Introduction

The rise of data centers and AI computing networks creates the need for a set of frameworks and ethical principles to guide the use of artificial intelligence systems. These principles and control measures serve as the foundation of AI governance in the U.S. vs India, and other leading nations.

As AI applications continue to advance through advanced human-machine interactions, governments will need to find a balance between control and innovation. Examples of this decade’s AI developments are conversational chatbots such as Haptik AI and even smart assistants like Alexa. There are also AI-driven automated systems and predictive technologies that can influence how we live and work. This article compares AI governance in the U.S. vs India, exploring how each nation’s approach affects enterprise operations and future growth.

Why Enterprises in the United States and India Need AI Governance

The United States alone boasts of 50% of the global computing AI power, with about 187 AI data center clusters. Compared to the U.S., India is relatively lower in the rate of AI adoption and exploration, but is projected by Statista to grow at a 43.76% annual rate up to $45 billion between 2025-2031.

From the largest investor in artificial intelligence to another country with an over 55.3% internet penetration rate, AI governance in the United States and India is important due to the following:

1. Regulatory Compliance and Market Access

Compliance with updated AI governance in the U.S. and India is a must for enterprises to avoid penalties. For enterprises in the United States, sector-based regulatory laws exist instead of a single, standalone framework. That means every business leader must follow AI usage laws within their industry.

For enterprises in India, the Digital Personal Data Protection (DPDP) Act is a significant regulation, but compliance is limited to businesses that handle the data of citizens.

2. Risk and Liability Management

Beyond the terms of compliance, enterprises must understand AI governance in the U.S. vs India to minimize risks in real-world applications. Unregulated artificial general intelligence systems can lead to misinformation, biased decision-making, or privacy breaches that cause reputational and financial harm.

For instance, an AI-powered recruitment tool without proper oversight might favor one gender over another for technical roles. Such bias could result in civil lawsuits from aggrieved persons and damage the company’s credibility. AI governance frameworks in enterprises help detect and avoid these risks, especially in sensitive sectors such as finance, healthcare, and public services.

3. Controlled Innovation within Ethical Bounds

While some critics believe AI regulations pose innovation risks, AI governance in the U.S. vs India offers safe and ethical developments for enterprises. The presence of these frameworks, whether in the United States or India, promotes explainability, ethics, and transparency to ensure users and investors feel safe to use any AI product. Otherwise, a lack of control in innovative limits would mean users’ rights may sometimes be violated.

AI Governance in the United States

AI governance in the U.S. is a multi-layered regulatory framework that involves executive orders, non-binding instructions, sector-specific laws, and state-level policies. While Europe already has a major, legally binding EU AI Act, the United States still uses a fragmented approach through the following:

A. Executive Orders

The Federal government of the United States continually offers revised strategies on how AI should be deployed. For example, a recent Executive Order from the White House under President Trump revoked certain existing AI policies that were considered restrictive to American AI innovation. The aim was to help the United States claim global leadership in artificial intelligence.

B. Non-binding Instructions

These refer to legal guidance for enterprises regarding AI development and concerns such as ethical responsibility and security. Major ones are:

i) The U.S. AI Bill of Rights

This non-binding guide, as part of AI governance in the U.S., was issued by the Office of Science and Technology Policy in the White House. The AI Bill of Rights in the United States promotes ethical AI usage through five core principles. They are safe and effective systems, algorithmic discrimination protections, data privacy, notice and explanation, and human alternatives and oversight.

ii) NIST AI RMF

This represents the National Institute of Standards and Technology AI Risk Management Framework. It is best described as a structured, voluntary guide for enterprises to manage the different risks associated with the design, development, and deployment of AI and machine learning technologies. These four components: Govern, Map, Measure, and Manage, are used by the NIST’s AI RMF to oversee the entire AI lifecycle.

C. Advisory Bodies and Institutes

Due to the fragmented regulatory approach, AI governance in the United States also relies on these advisory bodies:

i) National Artificial Intelligence Advisory Committee (NAIAC)

The NAIAC is a leading advisor to the executive on the AI governance framework in the U.S. and is tasked with making recommendations on matters involving AI issues such as ethics, technology transfer, algorithmic accountability, education, security, legal rights, and commercial applications. NAIAC was established under the National AI Initiative Act of 2020.

ii) The U.S. AI Safety Institute (AISI):

The AISI is responsible for developing standards to test the safe implementation of innovative AI applications. Their other responsibilities towards AI governance in the U.S. include conducting risk evaluations and collaborating with both the government and private sector on best practices for research.

D. State-Level Regulations:

The absence of a comprehensive AI governance framework in the United States has also led to some state regulations regarding artificial intelligence, such as:

• California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA)
• California Landmark AI Safety Bill SB 53
• Colorado Artificial Intelligence Act
• Illinois Artificial Intelligence Video Interview Act

AI Governance in India

There is also no comprehensive AI governance framework in India. However, the country is working to create a unified ecosystem of policies that govern the use of artificial intelligence across sectors.

A. National Strategy for Artificial Intelligence

This AI strategy, published in 2018, is commonly referred to as AI for All in India and serves as a foundational roadmap for leveraging AI in India’s economic and social development. Five major sectors identified under this AI governance framework are healthcare, agriculture, education, smart cities, and smart mobility. Like other examples of AI governance in India, it favors ethical, transparent, and accountable AI systems.

B. Digital Personal Data Protection (DPDP) Act 2023

This is the comprehensive data protection law for India on how personal data should be collected, processed, and protected. Compliance requirements are for both domestic and foreign enterprises that handle data of Indian citizens. DPDP functions as a legal foundation for accountable AI usage with defined penalties for acts such as misuse of personal information.

C. IndiaAI Mission

The IndiaAI mission is a national objective to strengthen the overall AI governance in India. This includes support for AI research and indigenous models in Indian languages, and the establishment of computer infrastructure hubs such as the Quantum Valley Tech Park project. Other roles of the IndiaAI mission include encouraging startup innovation and global partnership under a well-organized public-private framework.

Comparing AI Governance in the U.S. vs India

The national AI strategy comparison between the United States and India highlights clear differences in philosophy and structure. The U.S. follows a decentralized AI governance model, while India is developing a more centralized framework guided by unified policies. The table below summarizes key distinctions between the U.S. AI Act vs India’s AI policy.

Conclusion

AI governance frameworks in the United States vs India highlight the common goal of balancing innovation with safety, transparency, and accountability. The national AI strategy comparison between the countries also reveals certain differences that call for standardizing current policies to achieve global leadership in artificial intelligence. For example, the growth of supporting technologies, such as AI audit and certification systems for independently validating algorithmic fairness and security, will be key to advancing responsible AI governance.