In the governance of emerging technologies, ethical guidance has often relied on so-called soft law instruments--codes of conduct, guidelines, or frameworks--designed to promote responsible behavior without imposing binding legal constraints. This is partly due to the difficulty of imposing harmonized regulations across the EU, especially in a global context characterized by strong reservations expressed by other international actors, e.g. the United States of America, with regard to the regulation of artificial intelligence (AI) that "unduly burdens AI innovation" (Kratsios, Sacks, and Rubio 2025) . Another reason is related to the principle, upheld in several member states such as Germany, that protects scientific freedom by constitutional law. Nevertheless, the recent trajectory of technological regulation in the European Union shows that soft law can evolve into hard law: this has been the case, notably, with the adoption of the AI Act (European Commission 2022; Terpan 2015) .

This study focuses on understanding the complex dynamics between humans and AI systems by analyzing user reviews. While previous research has explored various aspects of human-AI interaction, such as user perceptions and ethical considerations, there remains a gap in understanding the specific concerns and challenges users face. By using a lexical approach to analyze 55,968 online reviews from G2.com, Producthunt.com, and Trustpilot.com, this preliminary research aims to analyze human-AI interaction. Initial results from factor analysis reveal key factors influencing these interactions. The study aims to provide deeper insights into these factors through content analysis, contributing to the development of more user-centric AI systems. The findings are expected to enhance our understanding of human-AI interaction and inform future AI technology and user experience improvements.

Chalkidis et al. ( 2019) introduces the ECtHR dataset that consists of 11k cases from the European Court of Human Rights. Niklaus et al. ( 2021) releases the Swiss-Judgements-Prediction dataset that consists of 85k multilingual cases-German, French, and Italian-from the Federal Supreme Court of Switzerland. Xiao et al. ( 2018) introduces the CAIL dataset which consists of 2.7m Chinese criminal cases. The court debates are not publicly available in Korea. Chalkidis et al. ( 2022a) introduces a benchmark dataset for legal NLU in English focusing on Chalkidis et al. ( 2022b) investigate legal fairness over four legal judgement datasets with additional A.2 Precedent redaction rule Data subjected to anonymization are as follows Other personally identifible information: Social security number is deleted.

This is the accepted manuscript version. The final published version will appear in College & Research Libraries, November 2026. AI Literacy in UAE Libraries: Assessing Competencies, Training Needs, and Ethical Considerations for the Digital Age Zafar Imam Khan, Learning Resources Manager, Hamdan Bin Mohammed Smart University, Dubai, United Arab Emirates, Email: zafarimamkhan@gmail.com, https://orcid.org/0000 - 0003 - 2081 - 0951 Abstract The study explores the current state of artificial intelligence (AI) literacy levels among library professionals employing a quantitative approach consisting of 92 surveys of LIS professionals in the United Arab Emirates (UAE). Findings of the study reveal ed the presence of strong cognitive competencies, while there were gaps observed in behavioral and normative competencies, especially related to AI biases, AI - powered learning, and ethical considerations. There was a disconnect observed between the perceiv ed importance of AI skills and the effectiveness of the current training programs. Introduction Generative AI has created massive disruption in all sectors, such as manufacturing, services, agriculture, medicine, and education, and has transformed a range of operations and services. Libraries are transforming and gearing up to harness the power of AI, which can enhance efficiency, accessibility, and personalization of services; thereby reshaping the traditional library landscape. This transformation has been observed in several of the traditional library services as AI is automating routine tasks such as cataloguing and classification of collections, and enhancing search functionalities and information retrieval, thereby creating a much more accurate and organized library system while librarians have more time to focus on intellectually stimulating act ivities (Preethi, 2024). There is a race to integrate AI into library services at a global level, and this has presented both opportunities and challenges in terms of AI literacy among library professionals. AI literacy involves understanding of AI tools, their applications, and ethical considerations surrounding their use.

We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.

This chapter introduces the AI & Data Acumen Learning Outcomes Framework, a comprehensive tool designed to guide the integration of AI literacy across higher education. Developed through a collaborative process, the framework defines key AI and data-related competencies across four proficiency levels and seven knowledge dimensions. It provides a structured approach for educators to scaffold student learning in AI, balancing technical skills with ethical considerations and sociocultural awareness. The chapter outlines the framework's development process, its structure, and practical strategies for implementation in curriculum design, learning activities, and assessment. We address challenges in implementation and future directions for AI education. By offering a roadmap for developing students' holistic AI literacy, this framework prepares learners to leverage generative AI capabilities in both academic and professional contexts.

Under-resourced languages, such as Quechuas, face data and resource scarcity, hindering their development in speech technology. To address this issue, Common Voice presents a crucial opportunity to foster an open and community-driven speech dataset creation. This paper examines the integration of Quechua languages into Common Voice. We detail the current 17 Quechua languages, presenting Puno Quechua (ISO 639-3: qxp) as a focused case study that includes language onboarding and corpus collection of both reading and spontaneous speech data. Our results demonstrate that Common Voice now hosts 191.1 hours of Quechua speech (86\% validated), with Puno Quechua contributing 12 hours (77\% validated), highlighting the Common Voice's potential. We further propose a research agenda addressing technical challenges, alongside ethical considerations for community engagement and indigenous data sovereignty. Our work contributes towards inclusive voice technology and digital empowerment of under-resourced language communities.

To address this need, we contribute Holistic Evaluation of Multimodal Models ( HEMM), visualized in Figure 1. HEMM, as an evaluation framework, goes beyond conventional lists of datasets to emphasize holistic benchmarking at three levels.