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Collective endorsement of shared values across diverse social groups is essential for the development and sustainability of democratic societies, yet capturing the perspectives of marginalised populations remains a persistent challenge, particularly when examined through ethical, legal, and social (ELS) lenses. This study develops a structured Migration ELS taxonomy to guide a GenAI-assisted semantic classification model designed to identify ELS dimensions in textual data. The model is fine-tuned and evaluated within a human-in-the-loop framework using expert annotations to ensure reliability and interpretive accuracy. As an empirical case, the approach is applied to migration-related official policy documents and narratives of Ukrainian migrants published on the Telegram platform. The resulting framework enables the analysis of alignment between governmental and migrant perspectives, revealing thematic and temporal divergences in ELS dimensions across institutional and user-generated discourse. The findings demonstrate the potential of this scalable framework, which combines taxonomy-driven modelling with generative AI and expert-in-the-loop validation, to reveal patterns of alignment and temporal dynamics in the representation of values across different social groups.

As Artificial Intelligence (AI) systems increasingly permeate sensitive domains such as finance, healthcare, and media, ensuring their ethical deployment has become a central concern for researchers, policymakers, and practitioners. Current auditing tools often assess isolated principles, such as fairness or explainability, lacking a comprehensive view of the ethical risks involved. This paper presents a multidimensional framework for ethical evaluation of AI systems, designed to support responsible AI governance and alignment with the United Nations Sustainable Development Goals (SDGs). The proposed approach enables the simultaneous analysis of key ethical dimensions, including fairness, bias, explainability, robustness, transparency, and legal compliance. We demonstrate the applicability of this tool through one extensive case study: a credit scoring system, considered high-risk under the AI Act. This work contributes to operationalizing responsible AI governance, providing insight for policymakers, regulators, and practitioners to ensure ethical, legally compliant, and socially responsible AI deployment.

The increasing integration of Artificial Intelligence (AI) across various sectors of society raises complex ethical challenges requiring systematic and scalable oversight mechanisms. While tools such as AIF360 and Aequitas address specific dimensions, namely fairness, there remains a lack of comprehensive frameworks capable of auditing multiple ethical principles simultaneously. This paper introduces a multidimensional AI auditing tool designed to evaluate systems across key dimensions: fairness, explainability, robustness, transparency, bias, sustainability, and legal compliance. Unlike existing tools, our framework enables simultaneous assessment of these dimensions, supporting more holistic and accountable AI deployment. We demonstrate the tool's applicability through use cases and discuss its implications for building trust and aligning AI development with fundamental ethical standards.

As AI becomes increasingly integrated into public sector services, identifying the professional profiles and competencies necessary for its responsible implementation has become a critical priority. This study investigates the specific skills, knowledge areas, and professional attributes required for the ethical and effective deployment of AI in public services. Drawing on empirical insights from an expert workshop and follow-up interviews, we propose a competency framework that emphasizes not only technical expertise but also policy, legal, and ethical dimensions. Our findings contribute to ongoing debates on workforce preparedness and educational program design for AI governance in both the public and private sectors.

As modern society is extremely dependent on the Internet, social networks, and developing technologies, a threat to the security of both the individual and society as a whole has emerged. The openness and global nature of the Internet create opportunities both for criminals, who can use the available information for criminal purposes, and for law enforcement officials. Police officers, in turn, can deal with the preventive processing of data from the Internet in order to prevent crimes. This type of data analysis can detect illegal or criminal actions even at the stage of their formation. Crime-Related Events Identification Based on Text Analytics Approach describes the possibilities of extracting crime-related facts from semi-structured information from various Internet sources so that crime can be combatted. This book is aimed at police officers and IT professionals working in online investigations.

Text analytics of frequency, context, and media portrayal of individuals in war reporting provides insights into key figures, biases, and socio-political narratives. However, due to named entity ambiguity, the number of unique individuals mentioned does not always align with the total number of PERSON entities identified in the dataset, which leads to reduced accuracy in the text analysis. To address this challenge and improve the accuracy of individual identification while ensuring a more reliable analysis of the dataset, we applied the Damerau-Levenshtein distance metric and machine learning techniques to identify and consolidate mentions of personal named entities in news coverage of the Russian-Ukrainian war in 2022. As a result, we created a comprehensive personal names dictionary containing 6,414 entries, with each entry grouping name variants that refer to the same individual.

This document is the preface of the Computational Linguistics Workshop of the 8th International Conference on Computational Linguistics and Intelligent Systems (CoLInS 2025), May 15-16, 2025, held in Kharkiv, Ukraine (https://colins.in.ua/workshops/computational-linguistics-workshop/).

This document is the preface of the PhD Workshop on Artificial Intelligence in Computer Science of the 9th International Conference on Computational Linguistics and Intelligent Systems (CoLInS 2025), May 15–16, 2025, held in Kharkiv, Ukraine (https://colins.in.ua/phd-workshop-on-artificialintelligence-in-computer-science/).

This preface briefly presents the organisation and outcomes of the workshop and tutorial days of the Third International Conference on Hybrid Human-Artificial Intelligence (HHAI) 2024, introducing the conference topic and giving key highlights of the specifics of the proceedings.