AI Governance Guidelines: Revolutionising Liability and Safety in the Auto Sector

The AI Governance Guidelines, introduced by the Ministry of Electronics & IT under the IndiaAI Mission on 5 November 2025, significantly influence the Indian automobile industry. This sector is swiftly evolving from a traditional mechanical framework to a sophisticated, software-centric data ecosystem.

Core Challenges of the AI Governance Guidelines

The paramount challenge presented by the policy is to ensure that AI systems, particularly within autonomous vehicles (AVs) and Advanced Driver-Assistance Systems (ADAS), fulfil their commitments to safety and efficiency, while maintaining human safety, accountability, and data privacy.

Entity-Based and Activity-Based Regulation

These guidelines establish both ‘Entity-Based’ and ‘Activity-Based’ regulation across the automotive supply chain, concentrating on three primary facets: Vehicle Safety and Liability, Manufacturing Efficiency, and Ethical Data Ecosystems.

The Core Dilemma: Liability, Accountability, And ADAS

The incorporation of AI into driving functionalities (ADAS and AVs) elevates the importance of safety and accountability, presenting a direct challenge to India’s outdated legal framework.

Legal Liability Gap

India’s foundational road transport law, the Motor Vehicles Act of 1988 (MVA), is predicated on the assumption of human errors and negligence, rendering it inadequate for AI-driven technologies (excluding ADAS) where human control may be significantly reduced or entirely absent.

Shifting Responsibility

The AI Governance Guidelines, in alignment with global trends, suggest a transition of liability from the human driver (particularly at higher automation levels, SAE Level 3 and beyond) to the manufacturer, software provider, and original equipment manufacturer (OEM).

Product Liability Focus

Accidents stemming from flawed software design are increasingly viewed as product liability concerns. In scenarios involving AI, not only could the manufacturer be held liable, but other participants in the deployment of the AI system, such as developers, might also come under scrutiny.

Accountability Mandate

The guidelines require companies to implement a tiered liability approach and enhance transparency regarding the operations of various entities (suppliers, developers, integrators) within the AI value chain, aiming for clearly enforceable accountability.

Ethical Dilemmas In Collision Avoidance

For AI systems in AVs and advanced ADAS, unavoidable crash scenarios (the ‘trolley problem’) compel these technologies to prioritise lives based on predetermined programming.

Pre-Programmed Ethics

The algorithms driving AVs and advanced ADAS must be specifically programmed with ethical considerations (for instance, utilitarian principles that prioritise saving the greatest number of lives or rules favouring vehicle occupants).

Transparency of Choice

The policy’s ‘Understandable by Design’ principle mandates that decision-making algorithms provide clear rationales for their choices, essential for public trust and post-incident evaluations.

Bias in Data

Algorithms trained on biased data can inadvertently result in unequal outcomes across various demographics or traffic situations (for example, incorrectly identifying pedestrians based on skin tone or in particular Indian traffic scenarios). The ‘Fairness & Equity’ principle obligates manufacturers to engage in bias evaluation and rectification pertinent to the Indian milieu.

Operational Safety And Standards Compliance

The guidelines accentuate the necessity for rigorous, internationally recognised safety standards crucial for the adoption of automotive AI.

Functional Safety

Maintaining functional safety standards is emphasized, with the Bureau of Indian Standards (BIS) and the Telecommunication Engineering Centre (TEC) assigned the responsibility of formulating standards and certifications for AV and ADAS technologies.

Role of the AI Safety Institute (AISI)

The AISI will play a vital role in developing and enforcing standardised safety protocols and conducting strict testing and validation on autonomous systems prior to their deployment. This institution will provide essential technical expertise to guarantee that governance is scientifically grounded.

Manufacturing, Data Ecosystems, And Talent

The guidelines advocate for the overarching digital transformation of the Indian automotive sector.

Sustainability in Manufacturing

The ‘Sustainability’ principle urges manufacturers to utilize AI to optimize energy and resource efficiency in manufacturing processes, aligning with broader environmental initiatives.

Data Ecosystem and Privacy

Connected vehicles serve as significant data sources. The framework reinforces the necessity for robust safeguards to ensure that all data gathered through sensors, GPS, and usage records complies with the current Information Technology Act of 2000 and the forthcoming Digital Personal Data Protection Act of 2023.

Talent Development

The industry faces a pressing shortfall of AI specialists. The governance framework directly promotes capacity building through government-supported initiatives designed to reskill the current workforce and cultivate future talent in AV and ADAS safety along with its ethical implementation.

The AI Governance Guidelines assert that the future of the automotive industry in India hinges on a collaborative approach, where technological advancements are meticulously assessed for their impacts on human safety, ethical considerations, and verifiable accountability.

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