Abstract

The pervasive influence of artificial intelligence in our surroundings is undeniable, as it has significantly streamlined various facets of our daily lives. As we increasingly engage with artificial intelligence (AI) in our daily lives, our dependence on this transformative technology becomes more pronounced. AI influences a wide array of areas, such as the intricate algorithms that analyze our online behavior to curate personalized social media feeds, ensuring that we see content tailored to our interests. In the realm of transportation, autonomous vehicles are revolutionizing how we commute, promising safer and more efficient travel by utilizing advanced sensors and machine learning. In healthcare, AI-powered diagnostics are streamlining patient evaluations, providing doctors with precise insights that enhance treatment plans and improve patient outcomes. Moreover, in the legal system, AI-assisted tools are beginning to play a role in decision-making processes, potentially influencing judicial outcomes with speed and data-driven analysis .

Despite these advancements, the rise of AI brings forth critical questions regarding legal and policy frameworks. How can we ensure accountability for AI-driven decisions? What measures need to be in place to guarantee fairness and protect individual rights as we navigate this rapidly evolving landscape? These pressing concerns require thoughtful consideration and innovative solutions as we move forward into an AI-driven future. This growing reliance on artificial intelligence may be understandable in a world shaped by automation, yet it pushes us toward a difficult question: when an autonomous system commits an act that amounts to criminal wrongdoing, who should bear the legal burden? The dilemma is no longer about whether AI can assist humans but about how the law should respond when its actions produce real-world harm. This paper examines that tension by exploring the attribution of responsibility within the emerging debate on whether sophisticated AI systems should be granted a form of legal personhood. The inquiry goes beyond technology and enters the deeper legal question of whether an entity that operates independently of human control can be treated as a subject of law, capable of duties and liability rather than merely a tool in the hands of its creators. It is within this intersection of autonomy, accountability, and personhood that the core issues of modern AI regulation unfold, guiding the discussion on how future legal frameworks should evolve.

Keywords- Legal Personhood, Artificial Intelligence, Criminal Liability, Real- World Harm, Ai Driven Decisions.

Introduction

Artificial Intelligence (AI) is no longer just a concept found in science fiction movies; it is transforming our world in many ways. From how we interact with technology to the operations of businesses, the functioning of healthcare systems, and the development of smarter cities, AI is becoming increasingly integrated into our daily lives. However, many people still do not fully understand what AI is or how it works. At its core, artificial intelligence refers to machines or computer systems designed to perform tasks that traditionally required human intelligence. These tasks can include recognizing speech, making decisions, solving complex problems, and understanding emotions. AI aims to mimic human thinking and decision-making, but it accomplishes this through algorithms, data, and statistical models rather than biological processes.

The term “artificial intelligence” was coined by John McCarthy in 1955, marking the beginning of serious research in the field. The initial goal was to create machines capable of performing tasks like humans. Over the years, AI has evolved significantly, from self-driving cars to chatbots, and its applications now span nearly every industry imaginable. Its capabilities continue to grow. Despite these advances, one thing that needs to be peeked on is the liability for the criminal acts.

When an autonomous system commits a wrongful act that rises to the level of a crime, the law faces a profound challenge: who should be held liable, and how can the victim secure remedy? Traditional liability doctrines strain under the weight of AI’s unpredictability: the programmer, manufacturer, or deployer may not fully control or foresee the system’s decisions, and yet letting the AI act entirely unaccountably undermines justice. This is precisely where the doctrine of legal personhood becomes vitally relevant.

Legal personhood, in its basic sense, refers to the capacity of an entity to hold rights and duties under the law as it was well stated in the landmark case of Salomon v. A. Salomon & Co Ltd [1896] UKHL 1; [1897] A.C. 22. According to legal theory, a “legal person” need not be a biological human — corporations, for instance, are classic examples of non-human legal persons, capable of suing, being sued, owning property, and bearing obligations. In the context of AI, scholars like Mireille Hildebrandt have argued that highly autonomous systems might be granted a limited form of legal subjectivity so they can themselves bear responsibility for harm they cause when traditional actors (manufacturers, users) are not sufficiently accountable.

Visa A.J. Kurki, in his book A Theory of Legal Personhood, further elaborates on this: he distinguishes between “active legal personhood” (where the AI acts with a measure of independence) and “dependent personhood” (where the system’s actions are still tightly bound to its human creators). By granting AI a tailored legal personality, the law can create a structured mechanism: AI can bear certain liabilities, victims can sue the AI (or its insurer), and yet humans (designers, deployers) remain responsible for oversight or residual risk.

Of course, the idea of AI personhood is not without controversy. Critics worry that naming an AI as a legal person might serve as a “liability shield” for corporations or human actors, letting them off the hook while the artificial entity takes the fall. Others argue from a moral standpoint: AI lacks consciousness, moral agency, or genuine understanding, so giving them a status akin to legal persons may be philosophically incoherent or ethically problematic.

India’s legal framework does not currently recognize artificial intelligence (AI) as a legal person, which creates a significant gap in addressing situations where autonomous systems cause harm. Existing laws operate on the assumption that every wrongful act can be traced back to a human or a legal entity, but AI introduces complexity because it can act independently without direct human command. This is where the concept of legal personhood becomes important. Legal personhood allows the law to recognize an entity as capable of having rights and liabilities, similar to how it treats companies and trusts. The possibility of extending a limited form of personhood to AI is now under discussion as a potential solution for determining liability and ensuring appropriate remedies.

This research paper seeks to examine whether AI should receive a limited form of legal personhood, how liability for autonomous actions can be assigned, and whether such recognition can create a clearer, fairer structure for accountability and remedies as India moves toward increasingly intelligent technological systems.

Research Methodology

This paper follows a doctrinal research methodology, relying primarily on legal texts to analyse the question of whether Artificial Intelligence can or should be granted legal personhood. Primary sources include statutes such as the Information Technology Act, 2000, relevant provisions of the Indian Penal Code, and judicial decisions addressing personhood, liability, and technological harms. Secondary sources such as scholarly articles, committee reports, and policy papers are used to understand the evolving legal discourse on autonomy and accountability in AI systems. A comparative approach is also adopted, examining how jurisdictions like the European Union, the United States, and the United Kingdom approach AI liability frameworks. This methodology enables a structured evaluation of existing legal gaps and helps assess whether extending a limited form of legal personhood to AI can offer a viable model for assigning responsibility and ensuring remedies.

Review of Literature

Scholarship on Artificial Intelligence and legal accountability has expanded rapidly in the past decade, with researchers debating whether traditional doctrines of liability are sufficient to regulate increasingly autonomous systems. Early discussions focused on AI as a technological tool, placing responsibility entirely on developers and users. Balkin argues that modern AI systems possess a degree of autonomy that challenges the assumption that liability can always be traced back to a natural person, suggesting that law must evolve to avoid accountability gaps. Similarly, Calo highlights the unpredictability of machine-learning models and the difficulty of attributing intent—an element essential for criminal liability.

The idea of granting AI a limited form of legal personhood has been explored in several jurisdictions. The European Parliament’s 2017 report proposed an “electronic personhood” model to manage liability for autonomous robots, triggering significant academic debate. Abbott and Sarch argue that legal personhood could function as a regulatory tool, similar to corporate personhood, allowing AI to hold rights and duties without equating it to human status. Indian scholarship, however, generally remains cautious. Rajat Sharma notes that India’s existing legal framework is ill-equipped to recognise AI as a person in law but acknowledges that future regulation may require new categories of personhood.

Comparative studies further deepen the conversation. European scholars lean toward structured liability regimes, while American commentators prioritise human accountability frameworks. Across the literature, a consistent theme emerges: autonomous decision-making by AI exposes gaps in criminal and civil liability, leaving victims uncertain about remedies. These scholarly discussions collectively underline the central question of this research—whether limited legal personhood for AI can create a coherent and fair system of accountability in India.

Exploring the Concepts of Legal and Moral Personhood: Implications for AI and Beyond

Legal personhood is a distinct legal status that empowers an entity to possess rights, fulfill obligations, own property, and engage as a participant in court proceedings. This unique designation has been bestowed upon a variety of entities, including corporations, associations, and, in India, even certain revered deities and institutions. This status plays a crucial role in the legal landscape, allowing the law to treat these entities “as persons” to effectively manage responsibilities and transactions. Importantly, this does not imply that these entities are imbued with human characteristics or possess moral awareness; rather, it is a functional approach to organizing societal interactions. Conversely, moral personhood delves into the realm of ethics, evaluating an entity's status based on qualities such as consciousness, the capacity to experience suffering, and the ability to engage in rational thought. It is entirely possible for an entity to be granted legal personhood while lacking moral personhood, and vice versa—highlighting a complex interplay between legal and ethical considerations. In recent discussions, innovative proposals have surfaced to extend the concept of personhood to sophisticated artificial intelligence systems and robots. Advocates for “electronic” or “AI” personhood suggest that legal recognition could allow these advanced technologies to navigate issues of liability and asset ownership, marking a significant step in the evolving relationship between humanity and technology.

Autonomous AI system : liability and accountability

Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern society, with the potential to transform various domains such as healthcare, transport, and consumer products. These advanced technological systems present new approaches to problem-solving, enhancing our ability to make decisions and improving overall societal well-being. However, the integration of AI into our daily lives is not without its complications and possible downsides. One of the most compelling benefits of AI systems is their ability to process vast amounts of data efficiently. In healthcare, for example, AI can analyze medical records, research data, and even genetic information to offer personalized treatment plans. Algorithms can quickly identify patterns that might take humans years to discern, potentially leading to earlier diagnoses and more effective interventions. This capability could revolutionize the way we approach diseases, improve patient outcomes, and significantly decrease healthcare costs.

Furthermore, in transport, autonomous vehicles powered by AI have the potential to decrease accidents caused by human error, reduce traffic congestion, and lower carbon emissions through optimal driving strategies. In the realm of consumer products, AI has already made significant strides. From personalized shopping experiences to smart home devices responding to our preferences, AI enhances user engagement by providing tailored recommendations. These improvements can lead to more efficient use of resources and greater consumer satisfaction. Moreover, businesses that adopt AI technologies can streamline operations, reducing costs and increasing productivity. However, with these advancements come notable challenges.

One primary concern is the unpredictability of AI systems. Unlike traditional software, which follows defined rules and procedures, AI algorithms can develop their own decision-making processes based on the data they receive. While this flexibility is a strength, it also poses risks. For instance, an AI system might arrive at a solution that is optimal in the dataset context but fails to consider ethical implications or real-world scenarios. This unpredictability can manifest in various forms, whether in healthcare decisions resulting from biased data or autonomous vehicles misinterpreting signals on the road. An even more significant issue is the semi-autonomy of AI systems. As AI continues to evolve, we are increasingly seeing applications that operate independently or make critical decisions without human intervention. In such cases, the question of accountability arises. Who is responsible when an AI system makes a mistake? The traditional liability frameworks often struggle to accommodate these modern challenges, leading to uncertainties about legal and ethical responsibilities. For instance, consider an autonomous vehicle involved in an accident. In a conventional scenario, liability would typically fall on the driver. However, if the vehicle is self-driving, is the responsibility with the manufacturer, the software developers, or the owner of the vehicle? Current laws may not adequately address these nuances, creating potential legal grey areas that could hinder the adoption of autonomous technologies. This complexity extends to other sectors as well.

In healthcare, if an AI system misdiagnoses a patient, can the treating physician be held liable? And if so, to what extent? The implications further complicate existing medical malpractice laws and practices, necessitating a reevaluation of how we assign liability in the era of AI. To address these challenges, there is a growing call for a reformed approach to liability and regulation concerning AI technologies. Policymakers, industry leaders, and legal experts must come together to establish comprehensive frameworks that account for the unique characteristics of AI, such as unpredictability and autonomy.

One approach could involve the concept of “shared accountability,” where multiple parties contribute to the oversight and responsibility of AI systems. Additionally, implementing robust AI governance mechanisms is essential. This includes developing standards for data integrity to ensure that AI systems are trained on diverse, unbiased datasets. Regulatory bodies may need to create guidelines for the ethical use of AI, emphasizing transparency in algorithms and decision-making processes. By enforcing such standards, we can foster trust in AI technologies, ensuring they serve society’s best interests while minimizing potential harms. Moreover, promoting interdisciplinary collaboration between AI developers, ethicists, and legal professionals can lead to more holistic solutions. By considering the implications of AI advancements from multiple perspectives, the industry can create systems that are not only efficient but also ethical and accountable. As society continues to embrace AI technologies, public awareness and education will also play a crucial role. Providing individuals with knowledge about how these systems work and their limitations can mitigate fears and misconceptions. Moreover, fostering an understanding of the ethical and legal challenges associated with AI can empower consumers and stakeholders to engage more meaningfully in discussions about regulation and oversight.

In conclusion, while Artificial Intelligence presents considerable promise for enhancing efficiency and societal well-being across various sectors, significant challenges must be addressed as we move forward. The unpredictable nature of AI and its increasing autonomy complicate existing liability frameworks, raising pressing questions about accountability. By developing comprehensive guidelines and fostering interdisciplinary collaboration, we can better navigate these uncertainties and integrate AI technologies into our lives more responsibly. Through these efforts, we can harness the full potential of artificial intelligence while safeguarding human interests and ethical standards.

Arguments For and Against Conferring Legal Personhood on Artificial Intelligence

The debate on granting artificial intelligence legal personhood has intensified as AI systems mimic increasingly complex human-like outputs. Yet the question is neither simple nor unprecedented. In Mohd. Salim v. State of Uttarakhand, the Uttarakhand High Court attempted an unusual experiment by declaring the Ganga and Yamuna rivers as “living persons” with rights, duties, and liabilities. The Court relied heavily on cultural, ecological, and spiritual arguments, emphasizing that the rivers—seen as “Ganga Mata” in Hindu tradition—support nearly half the Indian population and were in immediate danger due to decades of rising pollution. Scientific data from the Central Pollution Control Board showed that by 2016 more than 350 million liters of untreated sewage entered the Ganga daily, industrial discharge had rendered stretches “biologically dead,” and heavy metals regularly exceeded permissible limits. Faced with this crisis, the Court believed that granting personhood could create stronger legal protection mechanisms.

However, the Supreme Court swiftly overturned the ruling, reasoning that personhood for natural objects would impose unmanageable liabilities on the state, including responsibility for floods, accidents, and ecological failures. The Court stressed that only the legislature—not the judiciary—could create such a radical legal category. This judicial caution is crucial because it reveals the deeper structural issue: legal personhood is not merely symbolic; it carries enforceable rights and duties that require a clear basis in law.

This dilemma echoes powerfully in present debates about AI. Brandeis Marshall argues that granting AI legal personhood is premature and legally incoherent. AI lacks the essential qualities that the law presumes in any rights-bearing subject: independent agency, the ability to form intentions, moral reasoning, and accountability. AI’s functioning is rooted in pattern recognition and statistical prediction rather than understanding or judgment. It consumes content—accurate, misleading, or malicious—and produces outputs without distinguishing among them. This has already produced harmful hallucinations, such as fabricated legal citations and false allegations.An entity that cannot comprehend context or exercise judgment cannot meaningfully bear legal responsibility.

Marshall also warns that the push toward AI personhood ignores the unresolved inequalities in human civil rights. She recounts how marginalized groups in the United States—Black people, women, LGBTQ+ communities—have historically received civil rights protections late, unevenly, or not at all. From the Three-Fifths Compromise to literacy tests disenfranchising Black women until 1965, legal personhood for humans has never been uniform. Even today, Supreme Court decisions on reproductive rights and affirmative action show that basic human rights remain unsettled. Extending personhood to AI before resolving these human disparities risks deepening injustice.

Indian jurisprudence offers similar caution. In Animal Welfare Board v. A. Nagaraja, the Supreme Court recognized limited rights for animals but grounded them entirely in human ethical obligations, not in any independent agency of the animals themselves. This pattern confirms Marshall’s point: non-human personhood in law has always been instrumental—created to protect human interests, not to elevate non-human actors to the status of independent subjects.

Both the Ganga judgment and Marshall’s critique demonstrate that AI personhood, without robust regulation, transparency requirements, and human-centered accountability, would create more legal confusion than clarity. Before contemplating rights for AI, the legal system must first secure the rights and protections of all humans affected by AI and establish governance frameworks to control AI’s risks. Otherwise, conferring personhood on AI becomes yet another attempt to “move fast and break things”—but this time at the level of the legal system itself.

Suggestions

The concept of legal personhood is a legal fiction designed to allow non-human entities to own property, enter into contracts, sue or be sued, and bear liabilities — irrespective of biological existence. Rather than leap toward granting full personhood to AI, policymakers should first adopt a graded responsibility model which recognizes degrees of autonomy and risk. Under this approach, “agentic” AI — systems that autonomously plan, decide, or act over time in complex environments — should be subject to higher regulatory scrutiny, while simpler automation tools remain under conventional liability frameworks.

Second, the law should require transparent audit- and log-mechanisms for AI decisions especially in high-stakes domains such as healthcare, finance, criminal justice, or social welfare. These records — showing how data, algorithmic weights, and decision criteria were used — enable accountability when things go wrong. The gap in explainability and accountability in contemporary AI has already been documented.

Third, the law ought to maintain human responsibility as foundational. Even if AI acts autonomously, developers, deployers, or owners must remain accountable unless a clear, robust framework shows that the AI’s decision was unforeseeable and beyond their control — similar to how corporate law “pierces the corporate veil” when necessary. Fourth, any future discussion of giving AI “electronic personality” should be limited to a narrow, instrumentally justified role, to allocate liability, facilitate remedies, and handle damages — rather than ascribe rights or moral status. Policymakers should avoid equating AI analogous to human or natural persons. What counts is legal order, not anthropomorphism.

In sum: rather than rush toward legal personhood, the legal system should first build institutions and norms around transparency, layered responsibility, human accountability, and tailored regulation of different AI capabilities.

Conclusion

Legal personhood, the status that allows an entity to own property, enter contracts, sue or be sued, and bear responsibilities, has historically been extended beyond humans to entities such as corporations, trusts, and other juridical persons, not because they are alive or conscious, but because law treats them as “legal persons” to organise complex social, economic, and governance structures. This “bundle of rights and duties” approach underlies modern business law worldwide.

Extending this concept to AI might appear tempting in light of the rapid evolution of “agentic AI”, systems capable of making decisions, acting autonomously, and managing complex tasks without constant human supervision. Recent scholarship points out that such systems already blur traditional boundaries of agency, autonomy, and control. But legal personhood is more than mere functional autonomy; it carries with it moral expectations, obligations, capacity for liability, and normative status.

Granting AI full or even limited legal personhood today risks destabilising the coherence of the law. AI lacks consciousness, moral awareness, intent, features that underlie human responsibility and liability. Treating code, algorithms, or statistical models as persons confuses legal fiction with moral reality. Moreover, corporate-type personhood has already shown how non-human “persons” can be misused: shielding individuals behind layers of legal structuring, dissolving liability, or evading accountability. An uncritically conferred AI personhood may generate a new kind of “digital corporate veil,” under which harms caused by AI become harder to trace and remediate.

Instead, a prudent path lies in regulation, not personification. Law should evolve to handle AI’s novel challenges through transparency mandates, traceability requirements, mandatory human oversight, and differentiated liability rules depending on AI’s role and autonomy. This would preserve human dignity and accountability even in a deeply automated world.

If in the future AI evolves beyond predictable algorithms, perhaps toward genuine agency or consciousness, the question of personhood may deserve reevaluation. But until then, the law must avoid conflating functional utility with legal or moral personhood. What we need is not to anthropomorphize machines, but to strengthen the legal frameworks that protect humans affected by them.

In this way, we ensure that technological progress does not outpace legal and moral clarity, and that our legal order remains anchored in responsibility, fairness, and human well-being.

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