Oct 2026· International Journal of Law Management & Humanities· 0 citations· 21 references
Law, AI, and Intellectual Property
Abstract
Artificial intelligence (AI) is increasingly embedded in invention workflows, yet patent systems continue to define inventorship in human terms. The central problem is not whether AI contributes to technical creativity, but whether existing legal doctrines can allocate ownership, responsibility, and evidentiary accountability when inventive processes are distributed across humans and AI systems. Prior scholarship has examined DABUS disputes and personhood debates, but the literature remains fragmented across jurisdictions and often under-specifies operational reform pathways for patent offices and courts. This article addresses that gap through a comparative doctrinal design covering the United States, United Kingdom, European Union/Germany, Australia, South Africa, India, China, and selected emerging jurisdictions. The analysis triangulates statutes, judicial decisions, patent-office guidance, and Scopus-indexed legal scholarship from 2019–2026. The findings show convergence on human-only inventorship, but persistent divergence in disclosure expectations, contribution tests, and evidentiary practices. These divergences produce transparency deficits, cross-border uncertainty, and uneven enforcement risk for multinational portfolios. The article’s original contribution is a six-part harmonized reform model that preserves human-centered inventorship while modernizing AI-contribution disclosure, contribution assessment, evidentiary protocols, interoperability standards, targeted supplementary protection for edge cases, and governance integration. The model is explicitly grounded in an accountability architecture framework that separates legal status from evidentiary governance. This framework offers a practical route to doctrinal stability and policy coherence without requiring full AI legal personhood.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.
Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al.· arXiv.org· 109 citations· ⚡19
A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.
Jiaqi Xue, Meng Zheng, Yebowen Hu et al.· arXiv.org· 109 citations· ⚡8
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.