Artificial Intelligence's Legal Shadow: Global Regulatory Battles Over Intellectual Property and Content Liability

Int. Jr. of Contemp. Res. in Multi. Volume 4 Issue 2 [MarApr] Year 2025 440 © 2025 Keshva Nand. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND). https://creativecommons.org/licenses/by/4.0/ Research Article Artificial Intelligence’s Legal Shadow: Global Regulatory Battles Over Intellectual Property and Content Liability Keshva Nand * Assistant Professor, Faculty of Law, The ICFAI University, Himachal Pradesh, India Corresponding Author: * Keshva Nand DOI: https://doi.org/10.5281/zenodo.17778124 Abstract Manuscript Information This research paper investigates the profound legal disruptions caused by Generative Artificial Intelligence (GAI) across Intellectual Property (IP) rights and civil liability frameworks. GAI, encompassing large language models (LLMs) and specialised generators, presents unprecedented challenges to the human-centric foundations of copyright(authorship and originality) and patent law(inventorship), necessitating doctrinal adaptation and legislative intervention.[1] Critically, the Supreme Court’s decision in Andy Warhol Foundation for the Visual Arts, Inc. v. Lynn Goldsmith, Inc.[2] Sharpens the commercial nexus in fair use analysis, significantly raising infringement risks for GAI outputs, particularly those competing in established creative markets. Concurrently, the opacity and functional autonomy of GAI create a substantial liability gap, prompting novel regulatory approaches such as the European Union’s proposed Artificial Intelligence Liability Directive (AILD)[3] This analysis offers a comparative global perspective on evolving legal responses and proposes systemic reforms, including statutory collective licensing for training data and enhanced transparency mandates, designed to balance technological innovation with creator compensation and legal accountability.[4] ▪ ISSN No: 2583-7397 ▪ Received: 16-03-2025 ▪ Accepted: 21-04-2025 ▪ Published: 30-04-2025 ▪ IJCRM:4(2); 2025: 440-448 ▪ ©2025, All Rights Reserved ▪ Plagiarism Checked: Yes ▪ Peer Review Process: Yes How to Cite this Article Nand K. Artificial Intelligence’s Legal Shadow: Global Regulatory Battles Over Intellectual Property and Content Liability. Int J Contemp Res Multidiscip. 2025;4(2):440-448. Access this Article Online www.multiarticlesjournal.com KEYWORDS: Generative Artificial Intelligence, Intellectual Property rights, Civil liability, Fair use, Global Perspective, Transparency Mandate. Int. Jr. of Contemp. Res. in Multi. Volume 4 Issue 2 [MarApr] Year 2025 441 © 2025 Keshva Nand. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND). https://creativecommons.org/licenses/by/4.0/ 1. INTRODUCTION 1.1 Defining the Technological Disruption The recent and rapid advancement of generative artificial intelligence (GAI) has attracted widespread legal controversy, particularly within intellectual property (IP) law, where the innovative capabilities of GAI systems directly question the foundational principles of creative and inventive protection. [5] GAI, capable of generating art, literary works, music, and other creative content, presents new challenges to copyright law. [6] These systems operate by analysing rich, massive data sets, extrapolating patterns, and generating entirely new outputs without continuous, direct human instruction.[7] This technological leap forces a critical re-examination of legal doctrines governing authorship, ownership, and copyright protection, issues that are increasingly vital as AI becomes deeply incorporated into creative and inventive industries. [8] 1.2 The Fundamental Tension The core legal challenge presented by GAI lies in the conflict between existing IP laws and autonomous computational creation. Traditional IP legal frameworks are overwhelmingly predicated on human creativity and ingenuity.[9] In copyright law, protection historically requires a “human author” and works rooted in the “imaginative powers of the mind,” as established in landmark cases such as Feist Publications, Inc. v. Rural Telephone Service Co. [10] In patent law, the inventor must uniformly be a “natural person”.[11] GAI’s ability to produce high-quality, non-obvious creative works without direct human intellectual control places severe strain on these doctrines, leading to significant legal uncertainty regarding who owns the rights to AI generated content and whether such content is even eligible for IP protection.[12] Furthermore, the incorporation of mass amounts of pre-existing, often copyrighted, material into AI training data introduces immediate and complex copyright infringement risks across the AI lifecycle, a primary concern in the current legal landscape.[13] 1.3 Scope and Structure of the Study This paper undertakes a comprehensive analysis of the legal implications of AI-generated content. The study addresses two primary legal domains: intellectual property rights (focusing on Copyright and Patents) and the assignment of non-contractual civil liability for harms caused by autonomous AI outputs. The analysis incorporates a multi-jurisdictional perspective, examining evolving jurisprudence and regulatory initiatives in major territories, including the United States (US), the United Kingdom (UK), the European Union (EU), and key Asia-Pacific jurisdictions. The subsequent sections explore the crisis of human-centric standards, analyse the shifting doctrine of fair use, investigate emerging liability models, and conclude with concrete proposals for systemic legislative reform necessary to harmonise technological advancement with equitable legal principles. 2. The Crisis of Authorship and Inventorship in AIGenerated Content The emergence of GAI necessitates a fundamental reckoning with the concepts of authorship and inventorship, which serve as the legal foundation of copyright and patent protection. 2.1 Copyright and the Necessity of Human Authorship: A Comparative Review For a creative work to be eligible for copyright protection, existing legal systems typically require a demonstrable link to human intellectual effort.[14] The question of who is the author of works generated by AI remains largely unresolved in present legal frameworks, although administrative and judicial guidance is quickly emerging. 2.1.1 The US Position: Human Creativity Mandate The U.S. Copyright Office (USCO) has maintained a restrictive, human-centric standard, affirming that copyright protection is only available where a human author has determined “sufficient expressive elements” in the work. [15] This position stems from the long-standing requirement that copyrighted works must be the “products of scholarly work” rooted in the “imaginative powers of the mind.” [16] The USCO guidance confirms that while the use of AI to assist in the creation or the inclusion of AIgenerated material in a larger human-generated work does not bar copyrightability, the mere provision of textual prompts to a GAI system is insufficient to establish human authorship.[17] This strict stance was underscored by the USCO’s decision to dismiss the copyright claim for the comic book Zarya of the Dawn after initial approval, demonstrating the administrative body’s commitment to reviewing whether the expressive elements were determined by a machine rather than human intellect. [18] 2.1.2 The UK Exception: Computer-Generated Works (CGWs) In contrast to the strict human mandate of the US, the United Kingdom’s Copyright, Designs and Patents Act (CDPA) 1988 provides a unique, pragmatic legal mechanism for the protection of purely computer-generated works (CGWs). [19] Section 9(3) of the CDPA stipulates that for a literary, dramatic, musical, or artistic work which is computer-generated, authorship shall be taken to be the “person by whom the arrangements necessary for the creation of the work are undertaken.”[20] This provision allows protection for works generated without original creative contribution from the developer or user, provided the work would otherwise be considered original if created by a human. However, this CGW protection is functionally weaker; copyright lasts only 50 years, and moral rights are not conferred. This constitutes a pragmatic, albeit diminished, form of protection designed to address the unique category of fully autonomous creative output. 2.1.3 Emerging Clarity in China Recent jurisprudence in China also reflects a convergence toward the human contribution standard. The 2023 Beijing Internet Court recognised copyright protection for an AI-generated Int. Jr. of Contemp. Res. in Multi. Volume 4 Issue 2 [MarApr] Year 2025 442 © 2025 Keshva Nand. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND). https://creativecommons.org/licenses/by/4.0/ image, but this protection was contingent upon there being a “demonstrable human intellectual effort involved.” [22] This judicial flexibility aligns functionally with the USCO’s requirement for human determination of expressive elements, favouring protection only where a human intermediary demonstrates clear creative input. 2.2 Defining Originality in the Age of Computation The crux of the copyright challenge centres on the definition of originality. Traditional copyright demands that work be the “products of scholarly work” rooted in the “imaginative powers of the mind.” GAI systems, operating by analysing massive data sets and generating content based on complex statistical and probabilistic determinations, do not possess intentional, selfaware creativity. This technical reality challenges the core requirement of human-derived originality. In cases of collaboration, where a human provides input and an AI executes the creative task, the question of shared ownership becomes complex. Current framework slack precise methods to quantify the relative creative contribution of the human user (the prompter/curator) versus the machine (the statistical executor). An analysis of these varying international responses—US administrative policy, UK statutory provision, and Chinese judicial flexibility—reveals a significant global trend. Despite differing legislative origins, the practical application in IP offices and courts is converging on a minimum requirement of human intellectual effort or arrangement to qualify for IP protection. This movement suggests that legislative reforms should prioritise clarifying the nature and quality of human input required—such as inventive prompting, or post-generation selection and modification—rather than debating the legal personhood of the AI itself. A critical implication is that setting an overly stringent standard for human contribution could inadvertently stifle legitimate AI-assisted creation by denying protection to works where human direction is present but the machine executes the bulk of the creation. 2.3 Patent Law and the Inventor Paradox: Analysis of the DABUS Jurisprudence The challenge GAI poses to patent law is rooted in the nearuniversal requirement that an inventor must be a “natural person”. This was rigorously tested through the Thaler v. DABUS litigation, concerning two inventions generated by the AI system DABUS: a beverage container and a flashing light. [23] 2.3.1 The Global Consensus Against AI Inventorship Major patent jurisdictions, including the US, the European Patent Office (EPO), Germany, and Australia, have ultimately rejected AI inventorship.[24] The German Federal Court of Justice (BGH) in 2024 reinforced that “Human influence is mandatory” in the invention process.[25] Similarly, the US Patent and Trademark Office (USPTO) requires a natural person to have “significantly contributed to each claim” in a patent application. [26] The Full Federal Court of Australia also found against AI inventorship, upholding the principle that “inventor” is inherently human.[27] This global alignment confirms the legal incompatibility of purely autonomous AI systems with current patent doctrine. 2.3.2 Jurisdictional Nuances and the Invention Gap While South Africa’s initial acceptance of DABUS as the inventor stands as an anomaly due to legislative gaps, [28] the 2025 decision by the Swiss Federal Administrative Court provided a valuable nuance. The Swiss court affirmed that a natural person must be named but found that the human activities of data preparation, training the AI, and the key step of recognition of the AI’s output as a patentable invention were sufficient to qualify the human operator (Thaler) as the legal inventor.[29] The immediate consequence of the global consensus is the “Invention Gap”: if a highly autonomous AI creates an invention, current law mandates that it is unpatentable and falls into the public domain. This lack of IP protection acts as a severe disincentive for investment in sophisticated AI research aimed at autonomous invention. 2.4 Ownership Gaps: Allocating Rights to the Developer, User, or System Since an AI system lacks legal personality, ownership of AIgenerated content must be allocated through existing humancentric doctrines. Allocation generally relies on either the ’Work for Hire’ doctrine (assigning ownership to the developer or employer) or user ownership (based on the input and “necessary arrangements”). [30] Critics note that granting rights solely to the developer or user risks undermining the value of human creativity, especially where the AI contributes novel elements. This approach rewards organisational effort rather than the intellectual contribution principal IP is meant to uphold. Table 1 provides a comparative overview of the established legal stances across major jurisdictions regarding the foundational IP concepts of authorship and inventorship in the context of GAI. Table 1: Comparative Jurisdictional Stances on AI Authorship and Inventorship Jurisdiction Copyright Authorship Stance Patent Inventorship Jurisdiction Copyright Authorship Stance Patent Inventorship Stance Primary Legal Basis/Case United (US) States Must be a human author; no protection for purely AI output [31] Must be a natural per son; AI rejected [32] Thaler v. Vidal; US Copyright Guidance [32] United Kingdom (UK) Protects Generated “Computer Works” (CGWs); author is per son making “necessary arrangements” [34] Must be a natural per son; AI rejected [35] CDPA Section 9(3); German BGH (Parallel Stance) [36] European Union (EU) Requires originality reflecting human personality [37] Must be a natural person; AI rejected Infopaq A/S v. Danske Dagblades Forening [39] China (PRC) Copyright protection recognized if demonstrable human intellectual effort is involved Requires “person who makes creative contributions” [40] Beijing Inter net Court Ruling (2023) [41] Australia Full Federal Court rejected AI as inventor [42] Must be a natural per son; AI rejected [43] Commissioner of Patents v Thaler (2022) FCAFC 62 [44] Int. Jr. of Contemp. Res. in Multi. Volume 4 Issue 2 [MarApr] Year 2025 443 © 2025 Keshva Nand. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND). https://creativecommons.org/licenses/by/4.0/ 3. Copyright Infringement and the New Fair Use Landscape The expansive capability of GAI to mimic, adapt, and generate content based on existing creative works makes the doctrine of copyright fair use the most unstable area of IP law in the AI era. 3.1 Copyright Risks Across the GAI Lifecycle The development and operation of generative AI systems can be segregated into three stages, each presenting distinct risks of copyright infringement: a) Model Training (Input Risk): This initial phase involves feeding vast, often uncleared, copyrighted datasets into the AI model. If the data is not appropriately cleared for use, the utilization of these massive sets poses infringement risks, even if acquired from data collectors. The legality hinges on whether this mass copying constitutes fair use. b) Model Operation (Output Risk): Users interact with the AI to generate content, such as text summaries or image creation. Infringement arises if the generated content replicates or adapts existing copyrighted material, risking the creation of an unlawful derivative work. For instance, using a GAI tool to create a quick synopsis of a copyrighted book competes directly with the author’s market by providing a substitute for the original work. [46] c) Model Optimization (Feedback Risk): The AI continuously learns and improves based on prior inputs and generated outputs. This self-learning process risks perpetuating or amplifying prior infringement if the data used in re-optimization includes uncleared or infringing content. 3.1.1 The Litigious Input Phase Current litigation is overwhelmingly concentrated on the Input Risk, with over 51 cases filed against major AI firms challenging the legality of training data scraping. Noteworthy examples include Getty Images v. Stability AI, where the claimant alleges unlawful scraping of millions of images to train the Stable Diffusion model and claims that the resulting software model itself constitutes an infringing copy. [47] The financial risks are substantial, as demonstrated by the $1.5 billion Anthropic classaction settlement preliminarily approved for copyrighted material used in training. The core argument in these cases is whether the massive data consumption for training constitutes fair use. While some legal analysts argue that the use of copyrighted material for training by itself is likely to be found transformative fair use in most circumstances, definitive judicial guidance is pending, with courts not expected to rule on fair use in AI training until 2026 at the earliest. 3.2 The Goldsmith Doctrine and the Commercial Test The US Supreme Court’s decision in Andy Warhol Foundation v. Lynn Goldsmith, Inc. (2023) fundamentally altered the fair use calculus, cautioning that trans-formativeness is only “a matter of degree”. The ruling shifted the analytical weight, making the fourth factor, the “Effect on the Market,” the central determinant of fair use. 3.2.1 The Doctrinal Pivot In Goldsmith, the court found that Warhol’s commercial silkscreen prints of Prince infringed Goldsmith’s copyright because they served the same commercial purpose—licensing for use in magazines—as the original photograph. This pivot means that GAI outputs are now highly vulnerable if they are used commercially in a way that directly competes with the market for the original work they are based on. For instance, if an AIgenerated image is used in a commercial context that competes with the market for the original photograph, it is less likely to qualify for fair use, regardless of how subjectively “new” the output appears. This stringent market test necessitates demonstrating that the AI output does not supplant or harm the market of the original work. Conversely, the dismissal of Sarah Silverman v. Meta AI indicated that without sufficient evidence showing the generated content diluted the market for the trained works, infringement claims based solely on training data usage may fail. [50] 3.3 Derivative Works and Substantial Similarity The Model Operation Stage focuses on the GAI’s capacity to produce derivative works, such as summaries, rewrites, or adaptations. The legal challenge lies in applying the test of “substantial similarity” when the AI output is a nuanced synthesis of learned patterns rather than a direct copy. If the AI output is “substantially similar to the copyrighted work from where it has learned,” such output would infringe the rights holder’s exclusive right to control derivative works. A critical tension exists between the fair use analysis applied to the input phase(training) and the output phase (commercial use). AI training data use is often defended as transformative because the resulting statistical model is fundamentally different from the raw data. However, Goldsmith demands a strict commercial test for the output. This creates a challenging asymmetry: an AI developer might successfully argue fair use for the training phase (Input Transformation), but the user who deploys the resulting model for commercial gain (Output Substitutability) could still be held liable under the demanding Goldsmith market test. This asymmetry shifts liability risk downstream from the major AI developers to the AI service providers and end-users, creating significant legal uncertainty for commercial GAI adoption in competitive markets. The following table details the analysis of the US fair use factors post-Goldsmith as they relate to GAI outputs. Int. Jr. of Contemp. Res. in Multi. Volume 4 Issue 2 [MarApr] Year 2025 444 © 2025 Keshva Nand. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND). https://creativecommons.org/licenses/by/4.0/ Table 2: Fair Use Analysis Post-Goldsmith for GAI Applications Fair Use Factor Traditional Interpretation Post-Goldsmith Interpretation Implication for GAI Outputs 1. Purpose & Character of the Use Transformation (new expression/meaning) favours fair use Greater Implication for GAI Outputs, emphasis on commercial use and whether the new work competes with the original High risk if GAI output is sold commercially and competes with original human work 2. Effect on the Market Examined but often secondary to transformative use Now, a central factor must not supplant the market for the original AI-generated derivative works (e.g., summaries, adaptations) that harm original markets are vulnerable to infringement claims 4. Non-Contractual Civil Liability for Autonomous AI Systems The functional autonomy of GAI systems raises complex legal issues concerning non-contractual civil liability for resulting harms, which include IP infringement, defamation, and physical damage. AI’s lack of legal personhood and intent creates a profound legal vacuum. 4.1 The Challenge of Intent, Causation, and Opacity The fundamental difficulty in assigning liability for AI-generated content is that AI lacks self-discernment, intent, and the capacity for punishment. Traditional fault-based liability requires victims to prove the wrongful action or omission (fault) by an identifiable person who caused the damage.[51] Since AI outputs are probabilistic and lack human-like intention, applying traditional fault-based liability is severely hindered. Furthermore, the “Black Box” nature of complex deep learning models—their complexity, autonomy, and opacity—makes it difficult or prohibitively expensive for victims to identify the liable party and establish a clear chain of causation.[52] Victims seeking compensation face high up-front costs and lengthy legal proceedings, which can deter them from claiming compensation altogether.[53] 4.2 Applying Existing Legal Lenses: Product and Vicarious Liability Legal experts have attempted to apply existing common law doctrines to bridge this liability gap. 4.2.1 Product Liability Framework Product liability, which holds manufacturers responsible for harms caused by defective products, is a potential framework. Under this model, the liability would fall to the AI developer or creator for damage caused by a “defective” AI system or its output. The central difficulty, however, is defining what constitutes a “defect” in an autonomous, evolving, and probabilistic AI system, particularly when the manufacturer lacks control over the user’s prompts or inputs. 4.2.2 Vicarious and Negligent Liability Vicarious liability seeks to assign fault to a principal (owner/employer) for the actions of an agent (the AI system). This framework requires establishing a relationship of control or agency between the human user and the autonomous AI output, a challenging task given the AI’s self-learning capacity. A flexible, negligence-based approach, focused on the human’s failure to exercise reasonable care in prompting the AI or in publishing the resulting content, has been proposed as a means to hold users liable for defamatory or harmful outputs. 4.3 Legislative Adaptation: The European Artificial Intelligence Liability Directive (AILD) The European Union has addressed these gaps with the proposed Artificial Intelligence Liability Directive (AILD) in 2022, designed to introduce uniform rules for non-contractual civil liability for AI-caused damage. [54] 4.3.1 Presumption of Causality and Disclosure Requirements The AILD is significant because it aims to ease the burden of proof for victims by creating a rebuttable “presumption of causality” for damages caused by high-risk AI systems. This shifts the onus of proof onto the AI provider or operator to demonstrate the system was not at fault. [55] To confront the “black box” problem, the AILD grants national courts the power to order the disclosure of evidence about high-risk AI systems suspected of causing damage, ensuring that necessary information is accessible to victims. [56] This cohesive EU regulatory framework, incorporating the transparency mandates of the AI Act with the liability provisions of the AILD, creates a statutory, risk-internalisation regime. The AI Act forces transparency (Input), which directly facilitates accountability under the AILD (Output Liability). [57] This signals a shift away from relying on complex common law interpretation toward a system where developers are legally required to manage and disclose risks associated with their models, mitigating legal uncertainty for consumers and fostering trust in AI deployment. [58] Table 3: Comparison of Proposed AI Liability Frameworks Framework Basis of Liability Target of Liability Key Challenge in AI Context Product Liability Defect or unexpected danger in the product (AI system/output) Manufacturer/Developer of the AI system Difficulty proving “defect” due to AI’s complexity and opacity; lack of control over user prompts Vicarious Liability Agency relationship (control/employment) Owner/User (principal) of the AI system Establishing the necessary level of control by human over the autonomous AI agent EUAI Liability Directive (AILD Non-contractual civil liability for damage High-Risk AI Provider/ Operator Requires courts to order disclosure of evidence; potential detrimental impact on innovation Int. Jr. of Contemp. Res. in Multi. Volume 4 Issue 2 [MarApr] Year 2025 445 © 2025 Keshva Nand. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND). https://creativecommons.org/licenses/by/4.0/ 5. Comparative Global Regulatory Responses and Emerging Legal Paradigms Jurisdictions globally are developing distinct, yet occasionally harmonizing, policy responses aimed at addressing the legal implications of GAI while fostering innovation. 5.1 European Union: Transparency and Governance The EU’s regulatory architecture, particularly the AI Act, mandates stringent transparency obligations for providers of General Purpose AI (GPAI) models. [59] These obligations are designed to provide guardrails for compliance, requiring technical documentation, providing information to downstream AI providers, and supplying a detailed summary of the training content and data utilised. [60] Furthermore, the Act imposes transparency rules for AI outputs, requiring that synthetic content, including deepfakes and AI-generated text of public interest, be clearly labelled as artificially created or modified. [61] These measures aim to reduce deception and enhance trust in the information ecosystem. These obligations supplement the EU’s Text and Data Mining (TDM) exceptions within the CDSM Directive, although commentators note that transparency alone may be insufficient to ensure fair compensation. 5.2 United States: Administrative Guidance and Judicial Review The US response has been guided by its administrative bodies, maintaining the stance that IP requires human creativity.[62] Concurrently, there is an ongoing debate regarding how to compensate rightsholders for the mass use of copyrighted works in AI training. Proposals include the establishment of compulsory licenses, the adaptation of Collective Management Organisations (CMOs), or implementing technological opt-out mechanisms. The USCO has expressed “normative and practical reservations” regarding nonvoluntary approaches such as compulsory licensing for training data, complicating efforts to create a clear compensation framework. 5.3 Asia-Pacific Perspectives: Judicial vs. Statutory Approaches The Asia-Pacific region presents diverse models. The 2023 Beijing Internet Court ruling demonstrated a willingness to judicially recognize copyright for AI-generated images, contingent on “demonstrable human intellectual effort,” introducing flexibility while upholding the human standard. [63] This flexibility contrasts sharply with Australia’s statutory rigidity; the final rejection of the DABUS patent by the Full Federal Court affirmed that legislative change would be necessary to recognise AI inventorship, adhering strictly to the natural person requirement.[64] 5.4 The Drive for Transformative Use as a Global Standard The traditional international standard for copyright exceptions, the Berne Convention’s “three-step test,” is widely criticised as unsuitable for GAI due to the massive, non-specialised nature of AI data consumption and the risk of the resulting content impeding the “ordinary use” of the original work. Legal scholars advocate for adopting and clarifying a standardised “transformative use” doctrine globally, particularly for the data training phase, to balance innovation with author compensation. However, the primary financial risk is shifting toward commercial AI outputs (due to Goldsmith and large litigation settlements), yet the core ethical challenge remains the noncompensated, mass use of copyrighted works for model training (Inputs). Transparency requirements alone are insufficient to ensure authors are fairly compensated for this foundational use. This situation highlights the necessity for a global policy transition toward a mandatory, systemic compensation model designed specifically for the AI training market, shifting the focus from prohibiting use to ensuring fair remuneration. 6. Proposals for Systemic Legal Reform and Future Policy Systemic legal reform is essential to manage the complexities introduced by GAI, moving beyond doctrinal interpretation toward concrete policy mechanisms. 6.1 Rethinking Compensation: Statutory Licensing Models To ensure equity in the foundational input phase, policy must guarantee remuneration for rights holders. 6.1.1 Compulsory Licensing A tailored statutory (compulsory) licensing scheme should be introduced for the use of copy righted material solely for training GAI models. This model provides legal clarity, significantly reduces litigation risks for developers regarding input data, and guarantees remuneration for rights holders, effectively addressing the “input market failure” where individual negotiation is infeasible. 6.1.2 Collective Management Organisations (CMOs) Legislative efforts should focus on adapting or establishing CMOs capable of managing licensing and distributing royalties for AI training data use at scale. A forward-looking framework must incorporate robust public oversight and mechanisms to address the global and decentralised nature of training data, moving beyond symbolic recognition of authors’ concerns toward tangible compensation. 6.2 Policy Recommendations for Transparency and Data Provenance Mandatory transparency is necessary for both compliance and enforcement. 6.2.1 Mandatory Training Data Disclosure The EUAI Act’s requirement for a detailed summary of training data content should be solidified legislatively across jurisdictions. This creates a mechanism for accountability and facilitates compliance verification by rights holders. 6.2.2 Data Provenance and Labelling Legal frameworks must support the development and adoption of technical standards for tracking the provenance of AI training data. Furthermore, machine-readable labelling of AI-generated content must be mandated to assist in enforcement and to prevent fraud and deception by ensuring users know when they are interacting with or exposed to synthetic content. Int. Jr. of Contemp. Res. in Multi. Volume 4 Issue 2 [MarApr] Year 2025 446 © 2025 Keshva Nand. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND). https://creativecommons.org/licenses/by/4.0/ 6.3 Strengthening Moral Rights and Attribution As GAI blur the line between human and machine creativity, protecting the identity and integrity of human creators is paramount. 6.3.1 Protecting Human Personality Legislative measures are required to strengthen the moral rights (right of attribution, right of integrity) for human creators whose works are used by AI. Reinforcing the language of “personality” in copyright is crucial for human creators seeking to explain the intrinsic value of their work in the age of generative AI. 6.3.2 Attribution Policy Statutes must mandate clear attribution whenever AI-generated content is published, detailing the human input and the model used. This ensures intellectual honesty and prevents fraudulent claims of purely human authorship. 6.4 Establishing a Statutory Liability Regime for GAI Developers Reliance on unpredictable common law doctrines for liability must be replaced by clear statutory standards. 6.4.1 Adopting the AILD Model Global consideration and possible harmonisation with the European AILD’s framework is recommended, particularly the “presumption of causality” for high-risk AI systems.[65] This approach establishes clear standards for non-contractual civil liability, ensuring that victims of AI harm can seek justified compensation, thereby providing necessary legal certainty to both consumers and businesses. 6.5 The Evolving Definition of “Inventor” The global rejection of AI as a legal inventor is firm.[66] However, the legal system is subtly adapting the definition of human inventorship to accommodate the reality of AI-assisted creation. The Swiss court ruling, which found that human acts of recognition and data provisioning qualified as sufficient inventive contribution, demonstrates this expansion.[67] Future patent law reform should focus on establishing a clear threshold of human engagement in the AI process(e.g., input curation, problem selection, output validation) that qualifies the human operator as a legal inventor, thus incentivising the development of autonomous systems without challenging the natural person requirement. 7. CONCLUSION Generative Artificial Intelligence has fundamentally destabilised the human-centric principles underpinning global intellectual property and civil liability law. The analysis demonstrates that US jurisprudence, heavily influenced by the Goldsmith commercial test, imposes significant market-based infringement risks on AI outputs, while global patent law remains uniformly rigid in its rejection of AI inventorship. The opacity and autonomy of GAI further expose profound gaps in traditional civil liability frameworks. The path forward requires legal frameworks to evolve beyond mere doctrinal interpretation. The necessary solutions involve the simultaneous implementation of strong transparency mandates, such as detailed training data disclosure, and predictable compensation mechanisms, including statutory collective licensing for training data. This integrated approach is essential to address the core tension between encouraging AI development and ensuring fair compensation and moral recognition for human creators. Only through deep and systemic legal amendment, establishing clear rules for both IP and statutory liability, can the legal system effectively govern AI technologies while preserving the fundamental balance between encouraging creativity and technological progress. Legal clarity and equity are the essential requirements for maximising the societal benefits of GAI. REFERENCES 1. 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This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY NC ND). https://creativecommons.org/licenses/by/4.0/ 68. Commissioner of Patents v Thaler. [2022] FCAFC 62 (Austl). European Commission. Proposal for AI Liability Directive. COM(2022) 496 final. 69. German Federal Court of Justice. X ZB 3/22. Commissioner of Patents v Thaler. [2022] FCAFC 62 (Austl). 70. Swiss Federal Administrative Court. Decision A-262/2024. 2025 Jan 14. Creative Commons (CC) License This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 4.0) license. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.