Generative Artificial Intelligence and Copyright Law Case: A Legal Examination of Tender Cancellation

Keywords

Artificial Intelligence liability
AI copyright law
Criminal liability of AI
AI in India legal framework
Generative AI law
Copyright challenges
AI case laws
AI legal reforms

Introduction

Artificial Intelligence (AI) has emerged as the most disruptive force of the 21st century. From self-driving cars and automated trading systems to generative tools like ChatGPT and DALL·E, AI is reshaping industries, governance, and daily human interactions. With its rapid evolution, AI has raised profound legal questions, particularly in the domains of criminal liability and copyright law. Unlike traditional tools, AI demonstrates autonomy, unpredictability, and decision-making abilities, which blur the boundaries of responsibility. This forces the legal system to grapple with a fundamental issue: who is accountable when AI causes harm or infringes upon existing rights?

The criminal law perspective highlights the dilemma of applying principles such as actus reus (guilty act) and mens rea (guilty mind) to non-human entities. Can AI, which lacks consciousness and intention, truly be criminally liable? Or should liability shift to developers, corporations, or users who create and control these systems? At the same time, copyright law is being tested by generative AI, which can produce artistic, literary, and musical works at scale. These outputs often resemble human creativity, yet the law traditionally ties authorship to human originality. Courts, regulators, and scholars are debating whether AI-generated works deserve copyright protection, and if so, who should be recognized as the author-the user, the developer, or the corporation behind the technology.

The Indian legal system, rooted in a human-centric Constitution and statutes like the Indian Penal Code 1860 and the Copyright Act 1957, is at a crossroads. While the Information Technology Act 2000 provides some liability provisions, it does not address AI’s autonomous conduct. Globally, jurisdictions like the European Union, United States, and Japan are experimenting with frameworks, but there is no uniform consensus. This lack of clarity opens the door for ethical and practical challenges, including the risk of over-criminalization, corporate impunity, or misuse of copyright doctrines.

This blog explores these debates in depth by analyzing the meaning, scope, and characteristics of AI in legal contexts, highlighting the merits and demerits of imposing liability, examining leading case laws, and suggesting reforms. It combines the themes of criminal liability and copyright law, weaving them into a holistic study of AI and law. By offering an India-focused but globally informed analysis, it aims to guide students, researchers, and practitioners in understanding the evolving jurisprudence of AI.

Meaning

Artificial Intelligence, in its simplest form, refers to machines or systems designed to perform tasks that typically require human intelligence. These tasks include learning from data, recognizing patterns, solving problems, and making decisions. However, AI today is not a singular technology but an umbrella term encompassing machine learning, deep learning, natural language processing, robotics, and generative algorithms. While earlier AI systems were narrow and deterministic, modern AI exhibits adaptability, unpredictability, and autonomy. This legal analysis focuses on AI’s capacity to cause harm, infringe rights, or generate creative outputs, which makes its regulation urgent and complex.

The meaning of criminal liability in the AI context stems from traditional doctrines of law. Criminal liability is premised on two essential components: actus reus (the wrongful act) and mens rea (the guilty mind). Humans can be held liable because they possess intention, awareness, and free will. AI, on the other hand, operates through algorithms and data patterns. It can simulate decision-making but lacks subjective consciousness. Thus, applying criminal liability directly to AI raises philosophical and practical problems. For example, when a self-driving car causes a fatal accident, the car itself does not “intend” to commit homicide. The law must decide whether liability rests with the human driver, the developer, or the company deploying the AI.

In contrast, the meaning of copyright in relation to AI revolves around originality and authorship. Copyright law protects “original works of authorship” fixed in a tangible medium, granting exclusive rights to creators. Traditionally, authorship assumes human creativity. However, AI challenges this assumption by generating works-poems, artworks, software code-that are indistinguishable from human creations. The issue is whether AI can be considered an author and whether AI-generated works qualify for protection. If AI cannot be an author, the question becomes whether the rights should go to the human user who prompted the AI, the developer who built it, or the entity that owns the AI.

This dual inquiry shows that AI is not just a tool like a hammer or camera. It is a system that interacts with data, evolves with time, and produces results that even its creators may not fully predict. Therefore, meaning in this legal discourse is not confined to definitions in dictionaries but extends to legal interpretations, philosophical debates, and regulatory needs. The scope of AI liability and copyright protection ultimately reflects how societies balance innovation with accountability.

Characteristics of AI in Legal Context

Autonomy and Decision-Making

One of the most defining characteristics of artificial intelligence is its autonomy in decision-making, which distinguishes it from traditional machines and tools. Unlike conventional systems that operate strictly on fixed instructions provided by human beings, AI has the ability to make decisions independently based on algorithms, data patterns, and predictive models. For instance, a self-driving car can assess road conditions, detect obstacles, choose routes, and even respond to emergencies without human intervention. Similarly, algorithmic trading bots in financial markets execute high-frequency trades in milliseconds based on dynamic market changes, while generative AI systems can produce music, art, or literature without being explicitly programmed to generate each output. This autonomy offers significant advantages in terms of efficiency, scalability, and speed, but at the same time, it creates unique legal challenges. Liability in law has traditionally flowed from human conduct because it is assumed that human beings exercise control over their actions. However, when AI acts beyond its programming or in ways that even its creators cannot foresee, assigning blame becomes complicated. Courts are confronted with the dilemma of whether to attribute responsibility to the programmer, the corporation deploying the AI, or the user who initiated its functioning. The issue deepens because AI autonomy makes it resemble a quasi-agent that appears to act independently. This has led some scholars to suggest that AI could, in the future, be treated similarly to corporations, which are recognized as legal persons for certain purposes despite being non-human entities. However, unlike corporations, AI lacks governance structures or shareholders that can be held responsible. This unique nature of AI autonomy forces lawmakers to rethink established liability doctrines, since failure to address this could lead to accountability gaps where victims of AI-related harm find themselves without legal remedies. Thus, while autonomy is a strength of AI, it is simultaneously the most complex challenge for legal regulation.

Unpredictability and Learning

Another key characteristic of AI is its unpredictability, arising from its capacity to learn, evolve, and adapt beyond its initial programming. Traditional machines operate with predictability—pressing a button on a machine produces the same output each time. In contrast, AI systems, especially those based on machine learning and deep learning, process massive amounts of data and modify their behavior over time. For example, a medical AI trained to detect tumors in radiology scans may initially operate as intended, but as it processes new data, it may develop its own methods of classification that deviate from the developer’s original design. This unpredictability is both a strength, because it allows AI to continuously improve, and a weakness, because it makes outcomes harder to foresee. In the legal sphere, unpredictability challenges traditional principles of criminal liability, particularly the element of foreseeability. Developers and corporations may argue that they cannot be held responsible for AI outcomes that were impossible to predict at the time of development. On the other hand, victims of harm caused by AI—whether in cases of accidents, medical errors, or financial losses—demand accountability. Courts then face the difficult question of whether liability should be based on strict responsibility (regardless of foreseeability) or on negligence standards (depending on what could reasonably have been anticipated). In copyright law, unpredictability also raises concerns. When AI systems are trained on large datasets that may include copyrighted works, their outputs can unintentionally replicate or transform elements of existing works. This creates legal uncertainty about whether such outputs are infringing derivative works or entirely new creations. Since unpredictability is an inherent feature of AI learning, assigning liability becomes contentious. It pushes legislators and regulators to consider whether laws should require transparency in AI training datasets, mandatory audits, or clear disclosure obligations. Overall, the unpredictability and learning capacity of AI highlight that the technology cannot be easily analogized to traditional machines, making the need for adaptive legal frameworks urgent.

Lack of Consciousness and Moral Agency

AI systems fundamentally differ from human beings because they lack consciousness, emotions, intentions, and moral reasoning. In criminal law, liability is tied not only to the commission of a wrongful act (actus reus) but also to the presence of a guilty mind (mens rea). Punishments like imprisonment, fines, or even the death penalty are based on the assumption that offenders can experience deterrence, remorse, or rehabilitation. Since AI lacks subjective awareness, it cannot comprehend punishment or deterrence in any meaningful way. For example, imprisoning an AI system for causing harm would be absurd because it does not experience freedom, pain, or moral guilt. This absence of moral agency is the strongest argument against treating AI as a subject of criminal liability. Instead, liability must shift to the humans and corporations responsible for its design, deployment, or misuse. Similarly, in copyright law, creativity has historically been associated with human expression, originality, and intentionality. When a poet writes a poem, it reflects not just a combination of words but the poet’s subjective experiences and emotions. AI, in contrast, generates outputs based on data inputs and algorithmic probabilities without intentional expression. While the output may mimic human creativity, it lacks the consciousness and originality required by copyright doctrines. This creates a conflict: should AI-generated works be treated on par with human-created works, or should they remain outside the realm of copyright protection? Courts in various jurisdictions, including the United States, have consistently rejected copyright claims for purely AI-generated works, affirming that authorship requires human creativity. The absence of moral agency in AI thus affects not only criminal liability but also intellectual property rights. Legal systems must balance this by ensuring accountability rests with humans while acknowledging that AI outputs are becoming increasingly indistinguishable from human work.

Human-AI Interdependence

Perhaps the most practical characteristic of AI in the legal context is its interdependence with human beings and institutions. AI does not exist or function in isolation; it operates within complex ecosystems that include developers who design algorithms, corporations that commercialize them, regulators who set legal boundaries, and users who interact with the systems. This interconnectedness makes it impossible to analyze liability in purely binary terms of AI versus human responsibility. The Uber self-driving car accident in Arizona illustrates this well. The tragic death of a pedestrian involved multiple actors—the AI system that failed to detect the pedestrian, the company that deployed the car in real-world conditions, and the human safety driver who was supposed to intervene. In copyright disputes, the same interdependence is visible. For example, when generative AI platforms like MidJourney or ChatGPT produce works that resemble existing copyrighted materials, it raises the question of whether the liability lies with the user providing prompts, the developers who trained the AI on copyrighted datasets, or the corporation that profited from the platform. This interconnectedness suggests that liability should not be concentrated on a single actor but distributed across the chain of responsibility. Some scholars argue for a shared liability model where responsibility is proportionately assigned to developers, corporations, and users depending on their role in the causal chain. This mirrors existing doctrines of product liability, where manufacturers, distributors, and retailers all share responsibility for defective products. By recognizing AI’s human interdependence, the legal system can avoid accountability gaps and ensure that victims of AI-related harm receive justice. At the same time, distributed liability models must be carefully designed to avoid over-burdening smaller actors like individual users while ensuring that powerful corporations cannot escape responsibility.

Merits of Assigning Liability to AI Ecosystems

1. Encourages Responsible Innovation

Assigning liability to developers, corporations, and other stakeholders in AI ecosystems incentivizes responsible innovation. When creators know they can be held accountable for harmful outcomes, they are more likely to design systems with safety, ethics, and fairness in mind. This principle mirrors the regulatory approach in industries such as automobile manufacturing or pharmaceuticals, where liability fosters rigorous testing and quality assurance. In the context of AI, developers must ensure algorithmic transparency, implement robust safeguards against bias, and conduct extensive simulations to anticipate potential misuse. Legal accountability also compels companies to integrate mechanisms such as explainable AI, monitoring frameworks, and fail-safe protocols. From a broader perspective, this merit strengthens public trust in AI technologies, which is essential for adoption. Moreover, by promoting adherence to intellectual property norms and ethical dataset curation, companies avoid copyright infringements while supporting creative industries. Encouraging responsible innovation through liability ensures that AI growth is not merely profit-driven but aligned with societal interests and legal compliance.

2. Protects Victims and Provides Remedies

One of the strongest merits of assigning liability within AI ecosystems is its protective function for victims. AI-caused harm can range from physical injuries, as in self-driving car accidents, to financial and reputational damages, such as fraudulent trading algorithms or deepfake exploitation. In the absence of clear liability, victims may struggle to obtain remedies, leaving harm unaddressed and justice unserved. By placing accountability on developers, corporations, and users, the law ensures mechanisms for compensation, insurance coverage, or restitution. For instance, if a pedestrian is injured by an autonomous vehicle, liability assigned to the manufacturer or operator allows families to receive damages without having to navigate complex technical arguments against the AI system itself. Similarly, in copyright disputes arising from AI-generated content, creators can seek remedies against corporations responsible for training or deploying the AI, streamlining enforcement and protecting original authors. This merit ensures a societal safety net that aligns technological progress with ethical responsibility, providing reassurance to both consumers and creators.

3. Enhances Legal Clarity and Predictability

Another key advantage of assigning liability in AI ecosystems is the creation of clear and predictable legal frameworks. Uncertainty in liability can hinder adoption, investment, and innovation. Businesses may hesitate to develop advanced AI solutions if the potential for unforeseen legal exposure exists, while courts may face inconsistent judgments in complex cases. Establishing well-defined liability channels offers clarity for all stakeholders—developers, users, regulators, and consumers—by identifying who bears responsibility in specific scenarios. In copyright contexts, predictable frameworks delineate ownership of AI-generated works, outline infringement liabilities, and reduce disputes over authorship. This clarity enables companies to structure contracts, insurance policies, and compliance measures confidently, fostering a stable ecosystem for AI deployment. Predictability in legal outcomes also reassures victims that justice is attainable, bridging the gap between technological complexity and societal protection.

4. Promotes Ethical Use of Data and AI Systems

Assigning liability inherently encourages ethical practices in the collection, storage, and use of data that feed AI models. Developers and corporations must ensure datasets are sourced legally, respect privacy norms, and avoid discriminatory biases. By holding entities accountable for violations, liability mechanisms prevent exploitation of data, discourage unethical behavior, and uphold societal standards. For example, in cases where AI generates content based on copyrighted works, liability ensures creators are recognized and compensated, discouraging unlawful replication. Similarly, in financial or healthcare applications, accountable AI frameworks compel companies to avoid manipulation, fraud, or negligence. Ethical compliance thus becomes a practical requirement rather than a moral suggestion, reinforcing the credibility of AI technologies in public and commercial domains.

5. Encourages Proactive Risk Management

Liability drives proactive risk assessment and management strategies among AI developers and companies. Anticipating potential harms, vulnerabilities, and unintended consequences becomes a central part of the design and deployment process. Companies are incentivized to implement internal audit mechanisms, continuous monitoring, and compliance reporting to preempt legal exposure. This merit ensures that AI systems operate safely within intended parameters, reduces instances of unforeseen harm, and strengthens corporate governance structures. In addition, risk management extends to intellectual property, cybersecurity, and operational safety, creating a holistic approach to responsible innovation. By fostering proactive rather than reactive responses, liability frameworks reduce long-term legal costs and enhance public confidence in AI applications.

6. Facilitates Regulatory Compliance and Oversight

Liability assignment also strengthens the enforceability of AI regulations. When laws clearly identify responsible entities, regulators can monitor compliance more effectively, conduct inspections, and impose penalties without ambiguity. This facilitates enforcement of standards such as fairness, transparency, and accountability, particularly in sensitive sectors like healthcare, autonomous vehicles, and financial services. By linking legal responsibility to human actors, liability mechanisms provide actionable targets for regulators, ensuring AI systems do not bypass governance structures. Additionally, adherence to liability obligations can integrate seamlessly with national or international frameworks, supporting harmonized AI oversight.

7. Supports Public Trust and Social Acceptance

Finally, clear liability promotes public trust in AI systems. Users are more willing to adopt technologies when they know responsible parties can be held accountable for harm. Public perception is crucial in sectors like autonomous transportation, AI-based healthcare diagnostics, and content generation tools. Legal accountability not only mitigates risk but signals that innovation is not pursued at the expense of societal welfare. In copyright and creative contexts, users and creators gain confidence that AI-generated works respect existing rights. Trust and social acceptance are fundamental to long-term adoption, economic growth, and ethical integration of AI into everyday life.

Demerits of Assigning Liability to AI Ecosystems

1. Over-Criminalization of Developers

Holding developers strictly liable for all AI outcomes risks over-criminalization. Developers may face legal consequences even when harm arises from unforeseeable events or malicious misuse by end-users. AI systems often evolve through machine learning, producing outputs beyond the original programming. Penalizing developers for these emergent behaviors may be unfair and stifle innovation. Startups and small businesses may struggle to absorb potential liabilities, discouraging experimentation and technological progress. Over-criminalization could also lead to excessive legal defenses and defensive coding, diverting resources from productive innovation to litigation mitigation. Balancing accountability with fair risk allocation is a critical challenge for legal systems.

2. Corporate Escape Mechanisms

Assigning liability to corporations may inadvertently allow wealthy organizations to treat penalties as routine costs. Large companies could absorb fines without implementing meaningful systemic changes or compensating victims adequately. Liability without stringent enforcement may become symbolic rather than transformative, reducing its deterrent effect. Corporations may also exploit complex legal structures to limit responsibility, such as creating subsidiaries or outsourcing AI development. In such cases, the legal framework fails to ensure justice or prevent harm, undermining the intended societal benefit of liability assignment.

3. Ethical and Philosophical Concerns

Liability frameworks assume intention, moral blame, and conscious decision-making. Applying these concepts to AI ecosystems raises ethical dilemmas. Developers and corporations may face punishment for outcomes that are technically emergent or unintended, challenging principles of fairness. Similarly, in copyright law, extending rights to AI-generated works could devalue human creativity by equating machine output with human authorship. The philosophical tension between legal accountability and moral agency highlights the complexity of assigning liability in domains where AI acts autonomously or unpredictably.

4. Enforcement Challenges

Even with robust liability rules, practical enforcement remains difficult. AI systems often operate across borders, generating harm in multiple jurisdictions. Identifying negligent parties, proving causation, and demonstrating foreseeability in complex algorithms is highly challenging. The opacity of AI, sometimes described as the “black box” problem, complicates judicial analysis. Regulators may lack technical expertise, resulting in delayed or inconsistent enforcement. Without effective mechanisms to overcome these obstacles, liability may not translate into meaningful protection for victims or societal oversight.

5. Innovation Deterrence

Liability can unintentionally deter innovation by creating fear of legal exposure. Developers may avoid high-risk but socially beneficial AI projects, preferring safer, incremental solutions. This conservatism could slow progress in critical areas such as autonomous medicine, robotics, or environmental monitoring. Fear of litigation may also discourage collaboration, open-source contributions, or interdisciplinary experimentation, limiting the potential of AI ecosystems to generate transformative societal benefits.

6. Complexity in Liability Allocation

AI ecosystems involve multiple actors-developers, corporations, users, and data providers. Determining the proportion of liability for harm is highly complex, especially when causation is shared or indirect. Legal disputes may devolve into lengthy, resource-intensive litigation to establish accountability. This complexity increases legal costs, burdens courts, and risks inconsistent outcomes. It may also create gaps in victim compensation if parties deflect responsibility.

7. Potential for Regulatory Arbitrage

Strict liability frameworks could drive AI companies to jurisdictions with lax regulations, creating regulatory arbitrage. Corporations may relocate operations or exploit differences in liability laws to minimize accountability. This undermines domestic legal frameworks and may leave citizens exposed to harm from foreign-developed AI systems. Without international cooperation, global harmonization of AI liability remains challenging, reducing the effectiveness of national legal reforms.

Case Examples

The Uber Self-Driving Car Accident (2018)

In Arizona, an autonomous Uber car struck and killed a pedestrian. Prosecutors charged the safety driver for negligence, while Uber avoided direct criminal liability. This case illustrates the difficulty of pinning responsibility when AI, corporate policies, and human oversight all intersect. It demonstrates that AI itself is not treated as liable, but humans and corporations remain accountable.

Tesla Autopilot Crashes

Tesla’s Autopilot has been involved in several fatal crashes. Courts and regulators have generally held Tesla and drivers responsible, not individual engineers. The cases underscore that liability is distributed between corporations marketing AI as semi-autonomous and users relying excessively on it.

Deepfake Fraud Cases

Deepfake technology has been misused for fraud and misinformation. In India and abroad, liability has primarily fallen on users creating deceptive videos, not developers of deepfake tools. However, rising public pressure may lead to stricter rules for developers providing such technologies without safeguards.

U.S. Copyright Office and AI Works

In 2019 and again in 2021, the U.S. Copyright Office rejected copyright claims for works created solely by AI, such as the “Creativity Machine” artwork. These rulings highlight the insistence on human authorship as a precondition for copyright protection. They also raise questions for hybrid works involving both humans and AI.

Indian Context: IT Act and Copyright Act

India has not yet developed AI-specific legislation. Under the Information Technology Act 2000, intermediaries may be liable for hosting unlawful content, but AI personhood is not recognized. The Copyright Act 1957 assumes human authorship, leaving AI-generated works outside its scope. Indian courts have not yet decided cases directly on AI liability, but negligence provisions under the Indian Penal Code could apply to developers in extreme cases.

Suggestions

For India and other jurisdictions, AI liability requires a balanced and progressive approach. A hybrid liability model may offer the best solution. Users should be liable for intentional misuse, corporations for unsafe deployment, and developers for reckless or negligent coding. This ensures accountability across the ecosystem without unfairly penalizing one actor.

Second, India should consider legislative reforms similar to the EU AI Act, focusing on risk-based regulation. High-risk AI systems—such as autonomous vehicles or healthcare algorithms—should face stricter obligations, including transparency, audits, and human oversight. This would align with India’s broader push for digital governance under the Digital India initiative.

Third, copyright law must evolve to recognize hybrid authorship models. Users providing creative inputs should be treated as authors, with AI as a tool rather than an autonomous creator. However, when AI contributions are substantial, frameworks could allow shared authorship or special categories of protection. India’s Copyright Act could be amended to clarify these issues.

Fourth, ethical frameworks must accompany legal reforms. Over-reliance on AI for decision-making raises concerns about bias, discrimination, and erosion of human creativity. Education, public consultation, and social awareness campaigns are essential to ensure that reforms reflect public values.

Finally, international harmonization is crucial. Since AI operates globally, fragmented national laws will create loopholes. India should collaborate with global bodies like WIPO and OECD to develop consistent standards on AI liability and copyright.

Methodology

This blog adopts a doctrinal research methodology, analyzing statutory provisions, judicial decisions, and academic literature on AI liability and copyright. The focus is on comparative analysis, drawing from the EU, U.S., and Indian frameworks to highlight gaps and opportunities. The study also employs case analysis to illustrate how courts have responded to AI-related disputes in criminal and copyright contexts. Secondary sources include reports from the European Commission, Indian government committees, and scholarly articles on AI ethics. The methodology emphasizes critical analysis rather than descriptive repetition, aiming to develop practical suggestions for Indian policymakers.

Conclusion

AI represents both opportunity and challenge. It has the potential to transform industries, governance, and creativity, but it also disrupts established legal categories of liability and authorship. At present, AI cannot be held criminally liable because it lacks mens rea and consciousness. Responsibility continues to rest with users, developers, and corporations. In copyright, AI-generated works remain outside protection unless significant human input is involved.

The way forward lies in developing hybrid liability models, risk-based regulation, and adaptive copyright laws. India, with its vibrant tech industry and robust legal tradition, has the chance to lead in creating frameworks that balance innovation with accountability. The challenge for lawmakers is to design systems that protect society, compensate victims, and incentivize responsible innovation without stifling creativity.

FAQs

1. Can AI be held criminally liable in India?

- No. Indian law does not recognize AI as a legal person. Liability rests with users, developers, or corporations.

2. Can AI-generated works get copyright in India?

- Not currently. The Copyright Act 1957 assumes human authorship. Hybrid models may be considered in the future.

3. Who was liable in the Uber self-driving car accident?

- The safety driver was held responsible, while Uber faced regulatory scrutiny but no direct criminal charges.

4. Does Tesla’s Autopilot make Tesla criminally liable?

- Courts have held Tesla and drivers responsible for negligence, not AI engineers individually.

5. How do deepfakes raise copyright and liability issues?

- Users misusing deepfake technology are liable for fraud, while developers may face future regulation if safeguards are ignored.

6. What reforms are needed in India for AI liability?

- India needs AI-specific legislation, hybrid liability frameworks, and copyright reforms recognizing human-AI collaboration.

References

1. United States Copyright Office (2021).

2. European Commission (2020).

Artificial Intelligence and Intellectual Property: Discussion Paper

3. Samuelson, P. (2021). "Copyright and the Algorithm: A Critical Assessment of AI's Impact on Copyright Law." Harvard Journal of Law & Technology, 34(1), 1-24.

4. Ginsburg, J. C. (2018). "The Author's Rights in the Age of Artificial Intelligence." Columbia Journal of Law and the Arts, 42(3), 278-296.

5. UK Intellectual Property Office (2020).

AI and Intellectual Property: Current Legal and Ethical Challenges

6. Indian Penal Code 1860

7. Information Technology Act 2000

8. Copyright Act 1957

9. Union of India v. Reliance Industries Ltd (2018)

10. Registrar of Companies v. Gopal Doss Jeevan Lal (2015)

11. Shayara Bano v. Union of India (2017)

12. European Union Artificial Intelligence Act 2024

13. U.S. Copyright Office, “Policy on AI-generated Works” (2019, 2021)

14. OECD Principles on AI (2019)

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