Artificial Intelligence and the Future of Automotive Law
As artificial intelligence (AI) continues to reshape the automotive industry, legal frameworks are struggling to keep pace with technological advancements. From self-driving vehicles to AI-generated content in marketing and design, the integration of intelligent systems into automotive ecosystems has introduced complex legal questions that challenge traditional doctrines of liability, ownership, and personhood. While innovation accelerates, lawmakers and legal scholars are grappling with how to regulate AI without stifling progress. A recent comprehensive study by Wang Hongxia and Zhang Anyi from the School of Civil and Commercial Law at Henan University of Economics and Law offers a clear, principled approach to addressing these challenges, particularly in the context of autonomous vehicles and AI-driven product development.
The emergence of AI in the automotive sector is no longer speculative—it is operational. Companies like Tesla, Waymo, and NIO have deployed fleets of semi-autonomous and fully autonomous vehicles on public roads, relying on deep learning algorithms, sensor fusion, and real-time decision-making systems. These vehicles collect vast amounts of data, adapt to driving conditions, and make split-second judgments that were once the exclusive domain of human drivers. However, when an AI-controlled vehicle causes an accident, determining legal responsibility becomes a thorny issue. Who is liable—the manufacturer, the software developer, the owner, or the AI system itself?
This question lies at the heart of a growing debate in legal academia and policy circles. In their article published in the Journal of Yangtze Normal University, Wang Hongxia and Zhang Anyi argue that attributing legal personhood to AI is neither necessary nor feasible. Despite the sophistication of modern AI, they emphasize that these systems lack independent will, purpose, and property—core attributes required for legal subjectivity. Unlike human beings or corporate entities, AI does not act out of intention or self-interest; it executes pre-programmed instructions and learned behaviors derived from human-designed algorithms. Therefore, treating AI as a legal person would not only be philosophically unsound but also practically ineffective.
The authors reject the notion of granting AI an “electronic legal personality,” a concept that has gained traction among some legal theorists. Proponents of this idea suggest that AI systems, especially those capable of autonomous decision-making, should be recognized as electronic persons or digital entities with rights and obligations. Such a status, they argue, would streamline liability allocation and enable AI to enter contracts, own assets, or be held accountable for harm. However, Wang and Zhang counter that this approach misunderstands the nature of AI. An AI system cannot possess assets independently, nor can it exercise control over its own actions in a way that reflects genuine autonomy. Any property attributed to AI would ultimately be controlled by its human owners or operators. Moreover, holding AI accountable would serve no deterrent function, as machines do not experience consequences in the way humans do. There is no moral or behavioral incentive for an AI to avoid future harm simply because it was “punished” for past actions.
Instead, the authors advocate for a return to product liability principles as the most appropriate legal framework for regulating AI-related harm in the automotive context. Under this model, AI systems—including autonomous driving platforms—are treated not as agents but as products. When a self-driving car causes injury due to a software flaw, sensor malfunction, or algorithmic error, the responsibility should fall on the producers and designers of the system. This aligns with established tort law doctrines, particularly the principle of strict liability for defective products. By placing the burden on manufacturers and developers, the law creates a powerful incentive for them to ensure the safety and reliability of their AI systems before deployment.
Wang and Zhang stress that this approach must be adapted to the unique characteristics of AI technology. Traditional product liability law typically focuses on manufacturing defects, design flaws, and inadequate warnings. However, AI introduces new complexities, especially due to its capacity for machine learning and adaptive behavior. Unlike a static mechanical component, an AI system can evolve over time, modifying its decision-making processes based on new data. This raises concerns about whether a system that was safe at launch could later become hazardous through unintended learning or environmental feedback.
To address this, the authors propose extending liability to AI designers, not just manufacturers. In many cases, the design phase—where algorithms are developed, training datasets are selected, and safety protocols are embedded—is more critical than the physical production of hardware. A flaw in the underlying code or a bias in the training data can lead to catastrophic failures, even if the vehicle itself is mechanically sound. Therefore, holding designers accountable under a no-fault liability regime ensures that they exercise the highest degree of care during development. This includes rigorous testing, transparent documentation, and ongoing monitoring of system performance.
The authors also highlight the importance of risk mitigation mechanisms such as mandatory liability insurance. Given the unpredictable nature of AI behavior, especially in edge cases or rare scenarios, manufacturers and designers may face unforeseeable legal exposure. Requiring them to purchase insurance would help spread the financial risk and ensure that victims of AI-related accidents receive timely compensation. Furthermore, insurers could play a proactive role in promoting safety standards by offering lower premiums to companies that adopt best practices in AI development and deployment.
Beyond liability, the paper also examines the intellectual property implications of AI in the automotive industry. Modern vehicles are increasingly reliant on AI-generated content—from user interface designs and voice assistants to marketing materials and predictive maintenance reports. Who owns the rights to these creations? Can an AI system be considered an author under copyright law?
Wang and Zhang argue that AI-generated works can meet the originality requirement for copyright protection, provided they exhibit sufficient creativity and are not mere mechanical reproductions. However, they firmly reject the idea that AI itself can hold copyright. Since AI lacks consciousness and creative intent, it cannot be the legal author of a work. Instead, the authors propose that copyright in AI-generated content should generally belong to the owner of the AI system. This assignment serves both practical and policy objectives. It incentivizes investment in AI technology by ensuring that those who fund and deploy these systems can benefit from their outputs. It also maintains consistency with existing legal frameworks, such as the treatment of works made for hire or corporate authorship.
In cases where users interact with AI systems to produce content—such as customizing a vehicle’s infotainment interface or generating personalized driving reports—the ownership may be shared between the AI owner and the user, depending on the level of human input. If the user contributes original expression or creative direction, they may qualify as a co-author. Alternatively, if the AI is used as a tool under a service agreement, the rights could be governed by contract, allowing parties to define ownership in advance.
The authors acknowledge that as AI-generated content becomes more prevalent, distinguishing between human and machine-created works will grow increasingly difficult. To prevent confusion and protect intellectual property rights, they recommend establishing a formal registration system for AI-generated works. Such a registry would allow creators to document the origin of content, facilitating enforcement and reducing disputes over authorship and infringement.
Another critical issue addressed in the paper is the so-called “black box” problem in AI decision-making. In deep learning models, the internal logic of decisions is often opaque, even to the developers who built the system. This lack of transparency poses significant challenges for accountability, especially in high-stakes domains like autonomous driving. If a self-driving car makes a fatal error, investigators may struggle to determine why the AI made a particular choice. Was it due to faulty sensors, corrupted data, or an unforeseen interaction between variables?
To enhance accountability, the authors call for greater algorithmic transparency and explainability in AI systems. While full disclosure of proprietary code may not always be feasible, manufacturers should be required to provide detailed logs, decision trees, and impact assessments that enable regulators and courts to reconstruct the AI’s reasoning process. This would support fair adjudication of liability claims and promote public trust in AI technologies.
The paper also touches on the broader societal implications of AI in transportation. As autonomous vehicles become more common, they are likely to reduce traffic accidents caused by human error, improve mobility for the elderly and disabled, and increase fuel efficiency through optimized routing. However, they may also disrupt labor markets, particularly for professional drivers, and raise privacy concerns due to extensive data collection. The authors suggest that legal reforms should anticipate these secondary effects, incorporating safeguards for workers, consumers, and civil liberties.
One of the most compelling aspects of Wang and Zhang’s analysis is its grounding in legal tradition while remaining open to innovation. Rather than calling for a complete overhaul of existing laws, they advocate for targeted adjustments that preserve the integrity of legal principles while accommodating new realities. They emphasize that law should serve human interests, not abstract technological entities. This human-centered approach ensures that legal rules remain relevant, enforceable, and just.
Their work also underscores the importance of interdisciplinary collaboration. Regulating AI in the automotive sector requires input not only from lawyers but also from engineers, ethicists, policymakers, and social scientists. Legal frameworks must be technically informed, ethically sound, and socially responsible. By fostering dialogue across disciplines, lawmakers can develop regulations that are both effective and adaptable.
In conclusion, the integration of AI into the automotive industry presents both opportunities and challenges. While the technology promises to revolutionize transportation, it also demands a rethinking of legal concepts that have long been taken for granted. Wang Hongxia and Zhang Anyi provide a thoughtful and balanced framework for addressing these issues, advocating for a pragmatic, human-centered approach grounded in product liability, intellectual property, and ethical responsibility. Their analysis offers valuable guidance for legislators, regulators, and industry leaders as they navigate the complex legal landscape of intelligent vehicles.
As the world moves toward a future dominated by smart machines, it is essential to remember that laws are made for people, not for algorithms. The goal should not be to grant AI the rights of persons, but to ensure that the humans behind AI—designers, manufacturers, owners, and users—are held accountable for its actions. Only through such accountability can society harness the benefits of AI while minimizing its risks.
The insights presented in this study are particularly timely, given the rapid pace of AI adoption in the automotive sector. With major automakers investing billions in autonomous technology and governments drafting new regulations, the need for clear, coherent legal standards has never been greater. Wang and Zhang’s contribution fills a critical gap in the literature, offering a principled foundation for future legislation.
Their work also serves as a reminder that technological progress must be accompanied by legal and ethical reflection. Innovation should not outpace responsibility. As AI becomes more embedded in everyday life, the legal system must evolve to protect individual rights, promote fairness, and uphold the rule of law. The path forward lies not in anthropomorphizing machines, but in strengthening the institutions and norms that govern human behavior in an age of artificial intelligence.
Wang Hongxia, Zhang Anyi, Journal of Yangtze Normal University, DOI: 10.19933/j.cnki.ISSN1674-3652.2021.04.011