Graduate education and competency development in advanced practice provider roles: A scoping review.

As the development of generative Artificial Intelligence Technology continues to advance, problems related to Copyright Infringement caused by AI-generated Works are rising more frequently; In particular, User Liability due to Use of such Technologies is also On-the-rise. Systematically analyse the liability determination pathways of direct infringement, contributory infringement and fair use defences triggered by artificial intelligence-generated content in relation to substantial similarity under the specific circumstances of AI. Further demonstrate the difficulties of adapting to traditional safe harbours for AI governance. According to this, the paper presents a phased-separated and subject-based liability-sharing mechanism. Considering both types of criteria that belong to the "controls" and "Contextualisation", so as to find an appropriate way between technological innovation and copyright protection. This offers viable theoretical reference for optimising copyright systems in the AI era.

The copyrightability of mixed musical works, which contain a blend of human and generative AI elements, is an issue of increasing prevalence in copyright law. While there has been some discussion on the copyright status of fully generative AI works, this mainly resides in state law, and much of the federal policy found in Copyright Guides published by the United States Copyright Office is a non-binding opinion. Additionally, the same circuit courts contradict themselves, as seen with the differing views on the fair use of generative AI works in the Ninth Circuit Cases of Bartz v. Anthropic PBC (2024) and Kadrey v. Meta (2023). These issues combine to create a copyright “Dead Man’s Land” where the U.S. Copyright Office is forced to inspect mixed musical works on a case-by-case basis: an inefficient and ineffective mess for the modern day. This paper proposes that Congress pass legislation further itemizing the components of mixed musical works and only allowing generative AI to be used in one such component, alongside integrating state legislation. This policy would allow the U.S. Copyright Office to evaluate all mixed musical works under a unified framework. With the increase of artists creating mixed musical works, this solution only becomes more necessary as time progresses.

The convergence of artificial intelligence, blockchain, and non-fungible tokens (NFTs) has triggered a doctrinal crisis in copyright, contract, and evidence law across several major jurisdictions, including the United States, the European Union, and selected Asian legal systems. By 2025, over 70% of top NFT sales feature hybrid human–AI creations, yet most remain in legal and economic “gray zones” across jurisdictions. This article examines the challenge of partial AI authorship through a comparative analysis of U.S., EU, and Asian legal frameworks, revealing enduring gaps in originality doctrine, inconsistencies in the treatment of blockchain-based evidence, and contested approaches to smart contract enforceability and royalty mechanisms. Particular attention is given to the technical processes through which AI systems source, transform, and recombine data from public and private domains, raising unresolved questions of infringement, attribution, and authorship when copyrighted works are used without authorization. Drawing on originality doctrine, transformative use standards, and fair use principles, the analysis argues that legal protection should be confined to AI-assisted outputs that reflect meaningful human creative judgment and demonstrable transformation, rather than automated reproduction. Situating these doctrinal tensions within broader patterns of market volatility, regulatory arbitrage, and unequal access to justice. It concludes that adaptive, pluralist governance is essential to achieving legally coherent and socially sustainable outcomes in the digital creative economy.

Generative artificial intelligence (AI) is a developing technology that creates unique images, essays, and other outputs based on a user’s prompt. To train AI to produce such outputs, however, the program requires a large dataset to learn from—datasets that typically contain many human artists’ copyrighted works. AI’s reliance on these datasets potentially implicates copyright law when its outputs bear similarities to human artists’ works. This Note focuses on the legal challenges brought by and available to freelance visual artists, whose livelihoods are particularly vulnerable to AI image generation because they rely on social media and informal work-for-pay relationships. This Note argues that the generally accepted Fair Use analysis that AI outputs are obviously transformative fails to consider the full implications of the Andy Warhol Foundation for the Visual Arts v. Goldsmith decision and the potential impact of AI generated images replacing freelance social media artists. This Note advocates that courts should seize the opportunity of emerging copyright litigation to definitively hold that AI generated images are not inherently transformative.

ABSTRACT This study assessed the ethical consciousness and research literacy of research students at a public university in Bangladesh. Using convenience sampling, hardcopy questionnaires were distributed to students, and 226 were obtained. Despite using the internet for a variety of research objectives, respondents classified themselves as novices regarding online ability. While answering fifteen statements covering subjects including coming up with research ideas, gap analysis, search strategies, research ethics, and scholarly communication, students evaluated their proficiency in each research literacy category. Students’ assessments of their comprehension of research literacy and their actual practical understanding differ, even if they are given practical activities that are pertinent to certain research components. The respondents completed fifteen tasks, and four of these got higher correct answers (more than 50%). These tasks covered topics such as authorship, plagiarism, in-text citation, and interpretation of study results. Most of the responses to three more tasks – self-plagiarism, fair use, and referencing – were incorrect (below 30%). For the tasks including paraphrasing, intellectual property, phrase search, Boolean operator, truncation symbols, parentheses, proximity operator, and predatory journals, correct answers ranged from 30 to 45%. To assist students in becoming proficient researchers, the author suggested designing curricula with more research and ethical contents.

ABSTRACT With the evolution of online commerce due to changes in technology, the demands of consumers regarding fair use of data and proper management of the digital world are on the rise. This paper examines the effects of ethical information disclosure behaviour on customer confidence, perceptions of e-commerce ethics and buying behaviour in electronic commerce contexts. Based on semi-structured interviews with 20 respondents and processed with the help of thematic analysis and fuzzy-set Qualitative Comparative Analysis (fsQCA), the paper reveals two different routes through which trust is built on the online marketplaces. The results demonstrate that one type of consumer attaches importance to the indicators of transparency, privacy and social proof rather than to the signs of institutional trust, and another group of consumers values signs of formal trust (seals and technical certifications) higher. These findings indicate the fact that technological practices that are ethical, especially concerning transparency and communication of information, can greatly influence consumers’ confidence and decision-making. This paper can help the field of responsible digital innovation by connecting consumer behaviour with responsible digital innovation and providing practical solutions to achieve the introduction of an ESG-aligned disclosure system into e-commerce practices.

Copyright enforcement has become one of the most contested and consequential aspects of algorithmic content moderation. While online platforms increasingly deploy automated systems to identify and manage infringing material, these mechanisms often lack contextual nuance, over-prioritise corporate rightsholders and suppress lawful content such as transformative works and fair use. YouTube’s Content ID exemplifies how copyright-focused moderation reshapes digital governance, raising concerns over bias, opacity and limited procedural safeguards. This article critically examines the copyright implications of algorithmic enforcement by comparing United States and European Union legal frameworks, with a detailed case study of Content ID. To address these shortcomings, the study proposes data trusts as a governance model capable of ensuring fairer and more transparent copyright enforcement. By shifting oversight from private platforms to independent fiduciary entities, data trusts may improve enforcement accuracy while protecting users’ expressive rights. Given the implementation challenges, including legal uncertainty and institutional resistance, the article explores regulatory sandboxes as a structured pathway to test and refine data trust models before legislative adoption. By integrating copyright law, regulatory design and platform governance theory, this article contributes to ongoing efforts to create rights-respecting, economically viable alternatives to platform-dominated enforcement systems. It calls for a proactive, evidence-based shift towards fairer digital governance that protects both intellectual property and fundamental freedoms.

We aimed to analyse trends and demographic and clinical profiles in initial prescriptions of semaglutide ("Ozempic") by general practitioners in New South Wales between 2020 and 2023. This retrospective cohort study used electronic medical records from the New South Wales Health Lumos program, covering 680 general practices from January 2020 to November 2023. Individuals aged ≥18 years with a first general practice prescription for semaglutide ("initiators") were included. Counts and proportions of semaglutide initiators by year, sex, age, socioeconomic status, body mass index, and type 2 diabetes mellitus (T2DM) status were compared over time. Between 2020 and 2023, 59,009 individuals had a first general practice prescription for semaglutide, increasing from 448 in 2020 to 36,814 in 2023. Women comprised 63.6% of total initiators. The proportion of initiators with T2DM decreased from 92.2% in 2020 to 65.9% in 2023. Initiators with T2DM had a higher median age (women 53 years, men 59 years) compared to non-T2DM initiators (45 years for both sexes) and were more likely to live in socioeconomically disadvantaged areas. Greater proportions of non-T2DM initiators were aged <40 years, were female, and resided in less disadvantaged areas. Non-T2DM female initiators were less likely to be obese than those with T2DM (75.7% vs 79.3%), but the converse was true for men (91.0% vs 77.3%). Almost 30% of male initiators and 20% of female initiators without T2DM had a body mass index of >40 kg/m2. The use of semaglutide in Australia is expanding rapidly, particularly among individuals without T2DM. Patterns in general practice prescribing of semaglutide differ by sex, T2DM status, and socioeconomic factors, raising important questions about equitable access. These trends underscore the need for coordinated policy responses to support fair and sustainable use as demand continues to grow.

Synthetic data generation in paediatrics and paediatric nursing: what, how, and why?

Data centers, integral to the digital economy and services like cloud computing, artificial intelligence, and online communication, have become essential infrastructure. Yet the rapid demand presents challenges, including land scarcity, environmental impacts, and monopolistic control by Big Tech. This Comment argues for classifying data centers as public utilities, using legal precedents and state statutes to demonstrate their role as essential services with natural monopoly traits. Selective public utility regulation would ensure equitable access, sustainable practices, and fair resource use, including tailored eminent domain powers to support development. By addressing these challenges through public utility regulation, this framework balances public needs with market innovation in an increasingly digital world.

Consumer demand for improved animal welfare is rising, leading to the use of welfare labels that emphasize enhanced conditions for farm animals. However, on the farmers' side, complying with these standards often requires extensive and burdensome documentation. Precision livestock farming (PLF) technologies can simplify the collection of animal welfare data, such as health, behavior, and environmental conditions, thus reducing the documentation burden and enhancing transparency. To investigate current practices in animal welfare data collection on dairy farms and evaluate farmers' willingness to share this data with relevant institutions, a survey among 277 dairy farmers in Germany was conducted between June and September 2024. Partial least squares structural equation modeling was applied. Trust in secure and fair data use and clear on-farm benefits-such as time savings and reduced documentation workload-emerge as the strongest drivers, whereas consumer-related considerations and social pressure play minor roles. Farmers are more inclined to share productivity and housing data, whereas health and behavioral data are probably perceived as more sensitive and thus less likely to be shared. Furthermore, farmers prefer private schemes over public authorities. These insights suggest that transparent data-governance rules and demonstrable farm-level advantages are pivotal levers for unlocking PLF data flows. Embedding such enabling conditions in animal welfare programs could streamline documentation, cut audit costs, increase farmer participation, strengthen consumer confidence in animal welfare labels, and provide guidance for policy and program design.

The exploitation of voice identity by artificial intelligence development and user companies through voice cloning technology has caused tangible economic losses. The fundamental issue lies within the positive legal systems in Indonesia and Thailand, which experience qualification barriers. Civil instruments such as torts become paralyzed because cyber law, intellectual property, and data protection regimes fail to classify voice as a commercial property right object. Therefore, this research aims to comparatively analyze these civil law qualification barriers using the United States and the European Union as benchmarks, while simultaneously formulating a prescriptive transplantation of the right of publicity doctrine. This normative legal research utilizes the statute approach, comparative approach, and conceptual approach to comprehensively dissect domestic instruments and foreign case law. The results of the comparative analysis affirm the United States’ supremacy in recognising voice as a property right. This protection standard is refined by an ethical restriction layer in the form of an absolute prohibition as a default prohibition from the European Union. Overcoming domestic paralysis requires reconceptualizing property boundaries within the Civil Code. Recognizing voice as an intangible property will directly activate the tort lawsuit instrument. This activation aims to demand the disgorgement of profits from entities that develop artificial intelligence without authorisation and train algorithms without authorisation. This lawsuit also applies to commercial user entities conducting advertising through voice cloning. This research concludes that the adoption of the right of publicity requires the formulation of specific regulations (lex specialis) to protect personality rights. These regulations must explicitly separate corporate liability and adopt a layered consent standard. These rules must also contain a fair use exception to maintain a balance between economic property protection and freedom of expression in society.

Song Monuments: From Tourism Monuments to Copyright Doctrine: Korea’s Supreme Court on Fair Use Timing and CMO Authorisation

Objective: this study examines how artificial intelligence (AI) can be combined with empirical linguistic data to develop models of universal phonological and orthographic systems. The broader aim is to contribute to more sustainable and inclusive tools for cross-linguistic communication. The work focuses on a central challenge in contemporary linguistics: the lack of reproducible AI-driven methods that link computational modeling with theoretical analysis and that ensure fair and accessible use of digital language technologies. Method: A mixed-method framework has been adopted, in which corpus-driven linguistic analysis has been integrated with neural-network modeling. The empirical data have been drawn from two open-access resources: PHOIBLE (Phonetics Information Base and Lexicon) and the r12a database (r12a.github.io). After standardization and tokenization, the datasets have been processed using Python-based AI modules to extract frequency distributions, identify clusters and detect structural patterns. The analytical workflow has followed a clear, reproducible sequence of steps informed by PRISMA principles, ensuring transparency and methodological rigor. Originality/Relevance: the paper brings together corpus linguistics, interlinguistics and artificial intelligence to propose a data-driven approach for identifying shared phonological and orthographic patterns across languages. By combining extensive linguistic datasets with computational techniques, the study demonstrates the potential of AI to support the creation of sustainable knowledge infrastructures and to promote more inclusive forms of digital communication — domains that are becoming central to innovation and strategic growth in the humanities. Main conclusions: the analysis revealed a relatively small set of phonemes and grapheme correspondences that recur across a wide range of the world’s languages. These results offer empirical support for developing streamlined, accessible alphabetic systems and for designing universal auxiliary language models. The study further shows that AI-supported modeling can improve linguistic inclusivity and analytical precision, especially in low-resource and multilingual settings, while still relying on the interpretive judgement of human specialists. Theoretical/methodological contributions: the research contributes to interlinguistics by bringing together the concept of language universals and contemporary AI techniques. It outlines a reproducible pathway for connecting empirical linguistic data with computational tools and theoretical interpretation. In doing so, the study supports the sustainable development of language technologies and enriches our understanding of how human expertise and artificial intelligence can work together to strengthen global communication. Practical implications: identifying a universal phoneme core and stable sound-script correspondences can streamline multilingual analytical workflows, lessen structural biases toward non-Latin scripts and lower the overall costs of integrating low-resource languages into sustainable and reproducible knowledge systems.

This study examines the duty of care for AI software developers, focusing on copyright infringement issues raised by the proliferation of generative AI. Specifically, this study attempts to interpret the specific provisions of copyright law that may be infringed by AI software developers amid the proliferation of generative AI technology, distinguishing between the learning stage and the output and distribution stages.Currently, there is considerable confusion among Korean developers regarding the duty of care they must bear when developing generative AI software. This study examines the potential for copyright infringement at each stage of generative AI software technology and examines the potential legal issues that may arise. Next, this study explores the relationship between generative AI technology and copyright. To assist Korean developers in pursuing a sophisticated legal approach, this study explains the differences between traditional copyright law and the copyright requirements for generative AI technology. Therefore, to address the current anxiety surrounding the still significant uncertainty surrounding the ex post facto, fact-based, and expost facto duty of care for generative AI software developers, it is necessary to consider enacting a universal legal principle that can be summarized as follows: 1) only use legally accessible data for training; 2) minimize the removal or modification of Rights Management Information (RMI) and ensure transparency; and 3) control substantial similarity and market substitutability at the output stage. This represents a balance that protects the legitimate interests of copyright holders without hindering the development of AI technology. Going forward, our legal system should also gradually establish explicit regulations on TDM, transparency standards, and an accountability structure centered on output control, rather than relying solely on fair use interpretations to address the uncertainty surrounding AI learning. Such institutional reforms will lay the foundation for transforming generative AI technology from a “dangerous gray area” to a predictable and reliable area of innovation.

No Fair Use Without a Statutory Basis

This paper examines the enduring conflict between the trademark protection and freedom of expression, with an emphasis on whether parody can be considered a separate defense under modern trademark law. Thus, it comes into direct conflict with anti-dilution rules aimed at preserving the distinctiveness and commercial reputation of trademarks. Yet, existing legal frameworks offer no coherent solution. Through an examination of the legal systems of the United Kingdom, the European Union, and the United States, it becomes clear that different jurisdictions adopt distinct regulatory approaches. In particular, the UK and EU emphasize the preservation of trademark identity, while US law, influenced by the First Amendment, gives greater protection to artistic and critical expression. Despite these differences, no jurisdiction provides a coherent parody defense, and current outcomes rely on fragmented doctrines such as fair use or free-speech balancing. The results indicate that these cross-jurisdictional differences reflect broader structural developments, including the expanding communicative functions of well-known marks and the increasing privatization of cultural symbols. Furthermore, it proposes a limited and structured parody defense to prevent abuse, safeguard the core economic interests of trademark owners, and preserve a predictable space for cultural commentary within the public domain.

Copyright protection requires creative choices—creative choices that manifest in the work for which protection is sought. We all understand this point in theory, but in practice it has proven difficult to apply to visual works. The contemporary treatment of photography and generative artificial intelligence (AI), two different technologies used to create images, illustrates this. Examples of courts holding photographs unprotected by copyright are few and far between; in contrast, the Copyright Office and federal courts have been reluctant to grant copyright protection to AI-generated elements of visual works. To sharpen the contrast with real-world examples: A photo of a sudden arrest, snapped without thought on a smartphone with default settings, has been found to be protected by copyright, while an AI-generated image refined over hundreds of prompts to illustrate a graphic novel has been denied protection. These seemingly incongruous outcomes may pose a challenge for copyright law as it seeks to regulate intellectual property rights across different media and technologies. We do not argue here that the Copyright Office has been too harsh on a controversial emerging technology. Rather, we suggest that contemporary treatment of photography has become lax, prone to granting copyright with little or no analysis. Often, courts assume any photograph is protected by copyright and defer examination of what elements in the photograph (if any) might be protectable until forced to do so in connection with the infringement or fair use analysis. Instead, we suggest, courts should fully examine the originality of a work at the copyrightability stage. Recent Copyright Office actions on generative AI can provide a useful roadmap for a more rigorous originality analysis of visual works—specifically, by emphasizing the importance of tracing purported creative choices to an expressive result that manifests in the final work. Existing doctrines designed for infringement analysis, such as “thin copyright” and “dissection,” also offer useful conceptual models. Just as not every element in an image will be deemed creative for purposes of determining infringement, not every human choice should necessarily be deemed creative for purposes of finding originality. By closely analyzing the connection between creative choice and resulting expression in photographs at the originality stage, courts can return coherence and rigor to copyright doctrine for visual works.

The growing prevalence of digital academic resources in tertiary institutions in Oyo State has heightened the need for copyright literacy and fair use awareness among librarians and users. This study examined strategies adopted by academic libraries to promote ethical use of digital materials, the perceived roles of librarians, and the institutional support available. The population comprised academic librarians, drawn across three tertiary institutions in Oyo State, with a sample of 167 respondents selected through purposive sampling. Data were collected via a structured questionnaire and analyzed using descriptive statistics. Results showed that only 33.5% of respondents agreed that their libraries regularly organized copyright training, while 37.1% confirmed the availability of printed fair use guides. Just 22.2% felt adequately trained to educate users, and only 17.4% reported strong institutional support. Although 28.8% strongly agreed or agreed that promoting copyright awareness was part of their role, higher disagreement rates suggest a gap between role recognition and implementation. The study concludes that limited training, inadequate resources, and weak institutional backing hinder effective promotion of digital copyright literacy. The study recommends continuous professional development for librarians, integration of copyright literacy into user education programs, and stronger institutional policies to support awareness initiatives. Strengthening these areas is essential to enhance compliance with ethical and legal use of digital resources in academic libraries.