Intellectual Property Challenges of Artificial Intelligence : By Prof. (Dr.) Tabrez Ahmad

The fast growth in Artificial Intelligence (AI), specially generative AI, has brought complicated issues to intellectual property rights (IPR) frameworks globally. Below is a detailed analysis of key issues and fast grwowing trends based on latest legal, technological, and ethical developments: 1. Authorship and Ownership of AI-Generated Content Defining Authorship: Traditional IP laws attribute authorship to humans, but AI-generated works (e.g., art, music, or text) blur this distinction. For instance, the European Court of Justice ruled that AI-generated content cannot have a human author, though the AI developer or user may claim ownership 611. In India, the Copyright Office initially recognized an AI tool (RAGHAV) as a co-author but later retracted the decision, highlighting legal ambiguities. Ownership Disputes: Courts and legislators are grappling with whether rights belong to the AI developer, user, or the AI itself. For example, IBM and MIT’s AI co-invented a semiconductor material, but patent laws in many jurisdictions (e.g., the U.S. and India) restrict inventorship to humans. 2. Infringement Risks in AI Training Data Data Scraping and Copyright: AI models like Stable Diffusion and ChatGPT rely on vast datasets, often scraped from copyrighted works. Legal disputes, such as Andersen v. Stability AI and Getty Images v. Stable Diffusion, center on whether unlicensed use of copyrighted material for training constitutes infringement. Courts are evaluating whether such use qualifies as "fair use" or violates derivative work protections. Regurgitation Concerns: AI outputs that closely mimic training data (e.g., verbatim text or near-identical images) risk infringement claims. However, proving "substantial similarity" remains a hurdle for plaintiffs, as most AI outputs are transformative. 3. Jurisdictional and Regulatory Fragmentation Divergent Legal Frameworks: The U.S. Copyright Office rejects AI-generated works for lacking human authorship, while the UK recognizes the AI system’s operator as the author. The EU is drafting legislation to address AI’s role in IP creation, but global consensus remains elusive. Cross-Border Enforcement: AI’s global reach complicates enforcement, as seen in cases like Alibaba’s AI-powered platform removing counterfeit goods across multiple jurisdictions. Blockchain integration is emerging as a tool to track ownership and automate licensing. 4. Ethical and Procedural Challenges Bias and Transparency: AI tools used for IP enforcement may perpetuate biases if trained on skewed datasets. For example, biased algorithms could disproportionately target certain demographics in trademark disputes. Litigation Hurdles: Plaintiffs face procedural barriers, such as proving specific copyrighted works were used in training data. In Millette v. OpenAI, only plaintiffs with registered copyrights could proceed, underscoring the importance of formal IP protections. 5. Policy Recommendations and Future Directions Adapting Legal Frameworks: The OECD’s GPAI report advocates for voluntary codes of conduct, standardized contracts, and technical solutions (e.g., data access controls) to balance innovation with rights protection. Collaborative Ownership Models: Joint ownership of AI-assisted works (human + AI) and recognition of original content creators in AI-generated outputs are proposed solutions. Recapitulation: AI’s transformative impact on Intellectual Proeprty demands proactive modification of laws, international cooperation, and ethical safeguards. Institutions and corporations should adopt AI tools for IPregulation & management, stay informed on legislative changes, and collaborate with legal experts to navigate this evolving landscape. For a deeper dive, refer to sources like the WIPO Conversations on AI 11 and the GPAI report on data scraping. Refrences: 1. https://omnuslaw.com/insights/ai-and-the-evolution-of-intellectual-property-in-2025/ 2. https://www.wipo.int/about-ip/en/frontier_technologies/ai_and_ip.html 3. https://csipr.nliu.ac.in/miscellaneous/navigating-the-ip-landscape-in-the-age-of-ai-challenges-and-opportunities/ 4. https://techcrunch.com/2025/02/17/what-the-us-first-major-ai-copyright-ruling-might-mean-for-ip-law/ 5. https://www.debevoise.com/insights/publications/2025/01/lessons-learned-from-2024-and-the-year-ahead-in-ai 6. https://oecd.ai/en/wonk/ip-data-scraping 7.