How India’s New AI Governance Rules Will Redefine Liability, Safety & Data In The Auto Industry

The India AI Governance Guidelines (AI Governance Guidelines), released by the Ministry of Electronics & IT under the IndiaAI Mission on 5 November 2025, have profound implications for the Indian automobile sector.

It is rapidly transforming from a mechanical industry to a software-driven data-intensive ecosystem.

The policy’s core challenge is to ensure that AI systems, especially in autonomous vehicles (AVs) and Advanced Driver-Assistance Systems (ADAS), deliver on promises of safety and efficiency without compromising human safety, accountability, or data privacy.

The guidelines essentially introduce ‘Entity-Based’ and ‘Activity-Based’ regulation to the automotive value chain, focusing on three key areas: Vehicle Safety and Liability, Manufacturing Efficiency, and Ethical Data Ecosystems.

The Core Dilemma: Liability, Accountability, And ADAS

The integration of AI into driving functions (ADAS and AVs) elevates safety and accountability to the highest regulatory priority, directly challenging India’s obsolete legal framework.

Legal Liability Gap

India’s principal road transport legislation, the Motor Vehicles Act, 1988 (MVA), is based on the assumption of human error and negligence. This existing framework is inadequate for AI-driven systems (excluding ADAS) in which human control is limited or absent.

• Shifting Responsibility: The AI Governance Guidelines, aligning with global trends, imply a shift in liability away from the human driver (especially at higher automation levels, SAE Level 3 and above) toward the manufacturer, software provider, and original equipment manufacturer (OEM)
• Product Liability Focus: Accidents caused by poorly designed or buggy software are being increasingly treated as product liability issues. In the case of AI related issues, not only the manufacturer may be held liable, but other stakeholders involved in the deployment of the AI system such as developers, may also be brought under scrutiny.
• Accountability Mandate: The guidelines contemplate firms to adopt a graded liability system and provide greater transparency about how different actors (suppliers, developers, integrators) in the AI value chain operate, to ensure that the accountability is clear and enforceable.

Ethical Dilemmas In Collision Avoidance

For systems in AVs and advanced systems of ADAS being implemented in automobiles today, the AI faces unavoidable crash scenarios (the “trolley problem“), forcing it to prioritise lives based on its programming.

• Pre-Programmed Ethics: The decision-making algorithms within AVs and advanced ADAS levels must be explicitly programmed with ethical parameters (e.g., utilitarianism, which saves the maximum number of lives, or principles that prioritise vehicle occupants).
• Transparency of Choice: The policy’s ‘Understandable by Design’ principle demands that the decision-making algorithms should be capable of providing clear explanations of the choice it makes, which is essential for both public trust and post-accident investigation.
• Bias in Data: Algorithms trained on biased data may inadvertently lead to unequal performance across different demographics or road environments (e.g., misidentifying pedestrians of certain skin tones or in specific traffic environments unique to India). The ‘Fairness & Equity’ principle requires manufacturers to ensure bias testing and mitigation specific to the Indian context.

Operational Safety And Standards Compliance

The guidelines reinforce the need for robust, internationally recognised safety standards, which are essential for automotive AI adoption.

• Functional Safety: The guidelines also focus on maintaining functional safety standards and the Bureau of Indian Standards (BIS) and Telecommunication Engineering Centre (TEC) will be tasked to develop standards and certifications relating to these AV and ADAS systems.
• Role of the AI Safety Institute (AISI): The AISI will be crucial in developing and enforcing Standardised Safety Guidelines and conducting stringent testing and validation on autonomous systems before deployment. This provides the critical technical expertise needed to ensure that governance is scientifically informed.

Manufacturing, Data Ecosystems, And Talent

The guidelines support the broader digital transformation of the Indian automotive sector.

• Sustainability in Manufacturing: The principle of ‘Sustainability’ pushes manufacturers to use AI for optimising energy consumption and resource usage in manufacturing processes, aligning with broader environmental goals.
• Data Ecosystem and Privacy: The connected vehicle is a major data generator. The framework reinforces the need for strong safeguards, ensuring all vehicle data collected via sensors, GPS, and usage logs is handled in compliance with the current Information Technology Act, 2000 and under the upcoming Digital Personal Data Protection Act, 2023
• Talent Development: The industry currently faces a shortage of AI experts. The governance framework directly supports capacity building through government-backed programs to reskill the existing workforce and develop future talent in ADAS / AV safety and its ethical deployment.

The AI Governance Guidelines confirm that the automotive industry’s future in India is a collaborative effort, where technological advancement must be strictly measured against its impact on human safety, ethical integrity, and verifiable accountability.