explainable ai

Algorithmic Publicity and the Right to a Fair Trial

This article examines how social media and AI-driven algorithmic publicity affect the right to a fair trial in Australian criminal jury proceedings. It outlines the common law and statutory controls on prejudicial publicity, including sub judice contempt, permanent stays, jury directions and evidentiary discretions, and shows how they operate in leading High Court and appellate decisions and recent law reform work. It then explains how search engines, social media feeds, recommender systems, synthetic media and generative AI, through engagement-optimised ranking and amplification, undermine broadcast-era assumptions about juror exposure. Drawing on empirical research on juror psychology, media effects and juror internet use, the article evaluates the practical capacity of safeguards such as contempt, stays (including delay, change of venue and sequestration), judge-alone trials and directions where prejudicial material is persistent, searchable and personalised. It argues for a robust fair-trial standard paired with a more candid account of digital exposure. Building on developments in the UK, EU, US, China and Estonia, it proposes doctrinal and procedural refinements, narrowly targeted platform duties and court-supervised AI monitoring tools to preserve jury trial and open justice while maintaining credible fair-trial guarantees in an algorithmic information environment.

Why Public Service AI Governance Frameworks Risk Failing in the Age of General-Purpose AI: Lessons from Policing

Public services face growing pressure to adopt artificial intelligence (AI) to close the gap between rising demand and falling resources. That pressure has intensified with general-purpose AI (GPAI): AI built on large language models that can be directed by prompt alone to perform an effectively unbounded range of tasks. We argue that the properties that make these models attractive - their generality, accessibility, and low deployment cost - undermine the conditions under which AI safety has historically been pursued. The safety concepts that public service governance frameworks foreground - accuracy, bias, explainability, and accountability - were made tractable by narrow, purpose-built AI, and the mitigations that guidance documents prescribe presuppose exactly what GPAI removes. Accuracy cannot be quantified over unbounded outputs. Bias cannot be disaggregated when outputs are free-text judgements rather than categorical predictions. Explainability gives way to the appearance of explanation, and accountability erodes as outputs are optimized to persuade. We develop this through the case of policing, where the consequences of governance failure are most severe, and show why the same failure is likely to recur across other public services. The two mitigations that dominate policing AI strategy - expert evaluation and human-in-the-loop oversight - both rest on assumptions that GPAI violates. Safety assurance thus shifts from an intrinsic feature of building an AI tool to an optional add-on. We recommend a clear taxonomic distinction between narrow and general-purpose AI in governance documentation, a preference for technological parsimony, a pause on operational deployment of GPAI in policing until adequate evidence exists, and a coordinated national safety infrastructure with the authority to generate that evidence and determine when responsible deployment is achievable.

Database of Byzantine Book Epigrams - Archive

This dataset is an extension of the publication of the Database of Byzantine Book Epigrams (2023). The 2023 publication is a copy of the data used for the live application on dbbe.ugent.be. The present dataset aims to offer a simplified structure of the DBBE data, facilitating computational research and data analysis. The dataset provides structured access to the data behind dbbe.ugent.be, enabling intuitive exploration and reuse. It is based on production PostgreSQL and Elasticsearch instances and is organized around six core concepts. The data is updated weekly to reflect the latest research and additions. Occurrences:Individual epigrams reproduced as faithfully as possible from manuscripts, preserving orthography and punctuation. Types: Normalized or critically established texts linked to one or more Occurrences, enabling comparison across similar Occurrences and providing interpretive metadata such as translations, genres, and subjects. Verses: Verses of Occurrences, grouped in Verse Groups to record textual parallels across Occurrences. Manuscripts: Metadata on the context of manuscripts, including provenance and related documentation. Persons: Information on authors, scribes, editors, and other historical or modern contributors linked to Occurrences, Types, or Manuscripts. Bibliographies: Structured references connecting literature and publications to the relevant entities in the corpus. Exploring the Dataset The dataset can be explored and analyzed in multiple ways: Web-based viewing: Tools such as SQLite Viewer allow users to simply upload the SQLite file and inspect its contents directly in the browser, requiring no technical setup. Graphical interface and export: DB Browser for SQLite offers an intuitive interface for browsing the data and exporting tables to CSV for further analysis without need for scripting based interaction. AI-assisted semantic querying: The database file can be uploaded to a large language model for natural language queries. For example, one could ask: "Give me five occurrences from the eleventh century discussing harbours." To protect sensitive data, consider using self-hosted models or paid, secure AI services rather than free online tools. Custom development: The structured SQLite format provides a robust foundation for programmatic analysis, integration with other tools like datasette, or the development of custom applications tailored to specific research questions. Database schema For a full visual of the database schema, please visit yED live. 1. Occurrences This table stores individual Occurrences (= short epigrams or poems, literally how they have been found in a manuscript, including marks for gaps and missing text.) Columns include: id, created, modified, public_comment, incipit, text_stemmer, text_original, location_in_ms*, completion_date_floor, completion_date_ceiling, palaeographical_info, contextual_info, manuscript_id, title *Note that, in the current version, the location of occurrences within the manuscript is given as plain text (ex. p. 394-395 for pages or f. 18r-18v for folia). For manuscripts that have more than 1 way of numbering pages, the alternative location is marked as f. 14r -- (alt.) p. 27. Related tables: occurrence_person_role: Links Occurrences to Persons, indicating which Role a Person plays in the given Occurrence. Example: Scribe ( = historical person), transcriber (=modern person), contributor (=modern person)... occurrence_genre: genre attributed to this Occurrence (Can be more than 1) occurrence_metre: metre attributed to this Occurrence (Can be more than 1) occurrence_management: Internal information. For example: To do's in the processing of this Occurrence occurrence_acknowledgement: Plain text acknowledgement of people who helped in the publication of this Occurrence. occurrence_text_status: An Occurrence text can have statuses like partially/completely (un)known occurrence_related_occurrence and occurrence_relation_definition: An Occurrence can be related to other Occurrence if (a) some of their verses share Verse Groups or (b) they share a Type. The relationship type is defined in occurrence_relation_definition. This works in one direction: if occurrenceA --> related to --> occurrenceB is set, then occurrenceB --> related to --> occurrenceA is not set. occurrence_keyword: Keywords telling what the Occurrence is about 2. Verses This table contains verse-level information about an Occurrence. Columns include id, occurrence_id, manuscript_id, text, order_in_occurrence, verse_group_id. Verse Groups are groupings of similar verses across occurrences. 3. Types This table contains prototypes of Occurrences. A lot of Occurrences have a high level of similarity. DBBE proposes prototypes for every group of similar Occurrences. Related tables: type_person_role: Links Types to Persons, indicating which Role a Person plays in the given Type. Example: Creator, Translitor, Editor, Contributor, ... type_genre: genre attributed to this Type. More than 1 Genre can be attributed. type_metre: metre attributed to this Type. More than 1 Metre can be attributed. type_management: Internal information. For example: To do's in the processing of this Type type_acknowledgement: Plain text acknowledgement of people who helped in the publication of this Type. type_text_status: Type text can be either completely known or partially unknown type_related_type: Groups of similar Types. The relationship is defined in type_relation_definition. This works in one direction: if typeA --> related to --> typeB is set, then typeB --> related to --> typeA is not set. type_tag: Explains the function of the Type (ex: introducing a subject, making a comment on the content,...). type_occurrence: Occurrences linked to this Type. Note that this is a many-to-many relationship: one occurrence can be linked to several types, one type can have several occurrence linked to it. type_editorial_status: editorial states for types. Currently only critical text / not a critical text. type_keyword: Keywords telling what the type is about 4. Manuscripts This table contains metadata about manuscripts. Related tables: manuscript_person_role: Any possible role a Person could play in the publication of this manuscript. Example: Patron ( = historical person), Illuminator (=historical person), contributor (=modern person)... manuscript_acknowledgement: Plain text acknowledgement of people who helped in the publication of this Manuscript. manuscript_content: Explains what the manuscript is about. Careful: content is a hierarchical table. For example, a manuscript can be about Biblica -> Novum Testamentum. In this table, the lowest leaf (Novum Testamentum) is stored. The parent_id column of the content table can be used to trace the full content. manuscript_identification: Links a manuscript to one or more IDs that were used in canonical works to refer to this manuscript (ex: Diktyon) manuscript_management: Internal information. For example: To do's in the processing of this manuscript manuscript_location: The location where the manuscript was written. Careful: location is a hierarchical table. If a manuscript was written in Brussels, it is linked to Brussels, but via the parent_id column of the location table, you could also see that Brussels is in Belgium. 5. Persons This table contains metadata about persons involved (authors, editors, patrons, etc.). Related tables: person_acknowledgement: Plain text acknowledgement of people who helped in the publication of the information on this (historical) person. person_identification: Used to link persons to canonical IDs set by different authorities. person_management: Internal information. For example: To do's in the processing of this person person_self_designation: Used for scribes: How a scribe describes himself person_office: Used for scribes: The official title of a person. 6. Bibliographies Bibliographies are modelled as concrete entity types, rather than a single table as in the original setup. article book book_chapter blog_post bib_varia: This table is usually avoided but contains entries for which no other bibliographical type exists. online_source phd Each bibliographic entity has: its own table as mentioned above a corresponding _person_role table (ex.: article_person_role: could contain authors, contributors, reviewers, ... for a given article) tables linking to the item the bibliography is about: manuscripts (ex: manuscript_article: contains articles about a given manuscript) occurrence (ex: occurrence_book: contains books about a given Occurrences) persons (ex: person_article: contains articles about - usually historical - persons) type (ex: type_article: contains articles about given Types) Additional structures: journal and journal_issue: Articles may be linked to journals and journal issues. Note that, for now, some of these bibliography tables were added for completeness sake: not every concept (Manuscript / Occurrence / Person / Type) has all types of bibliographies linked to it (online sources, PhDs, etc.). Lookup / Metadata Tables roles — defines roles for persons (ex. Author, Scribe, Contributor, ...) text_status — textual status of Occurrence or Type. (ex. Text completely known, text partially unknown,...) keywords — keywords for Occurrence and Type (ex. Holy Trinity, Seven Sages, Last Judgement, ...) tag — tag for Type: Explains the function of the Type (ex: introducing a subject, making a comment on the content,...). metre — metre classification (ex. Dodecasyllable, Elegiacs,...) genre — genre classification. (ex. Scribe-related epigram, Text-related epigram, Reader-related epigram) management — administrative metadata. (ex. Bibliography to check) acknowledgement — acknowledgement linked to occurrence, manuscripts, type, or persons. (ex. Inf

A Correlational Study between Anthropometric Measurements and Posture Indicators in an AI-Based Body Assessment System: Focusing on Gender Differences among Young Adults in Their 20s

This study examined the correlational relationships between anthropometric measurements and composite posture assessment scores using data from an AI-based fitness classification system (FitTrix) among 20s adults residing in Seoul and Gyeonggi Province. A total of 150 adults in their 20s (50 males, 100 females) participated in the study. Descriptive statistics, Pearson correlation analyses, independent samples t-tests, multiple linear regression analyses, and chi-square tests were performed. Results indicated that waist circumference (r = 0.51), shoulder width (r = 0.42), and pelvic width (r = 0.38) showed significant positive correlations with composite scores (all p < 0.001). Statistically significant differences were found between genders in all anthropometric measurements (p < 0.001), with height (t = 13.24, p < 0.001, Cohen’s d = 2.10) and shoulder width (t = 8.64, p < 0.001, Cohen’s d = 1.37) demonstrating very large effect sizes. Multiple linear regression analysis revealed that waist circumference, shoulder width, and pelvic width significantly explained variance in composite scores (R² = 0.57, F = 30.12, p < 0.001), with the female model showing higher explanatory power (R² = 0.61) than the male model (R² = 0.50). Posture classification indicators showed significant gender differences (χ² = 23.15 ~ 41.23, p < 0.001). These findings provide important scientific evidence for early posture management in 20s adults, developing individualized fitness prescriptions, and enhancing the reliability of AI-based body assessment systems.

基于固定语义锚点与分层变换函数的AI原生认知架构——脱离统计拟合的类人理解范式

Current mainstream artificial intelligence models including convolutional neural networks and large language models rely on statistical fitting over fragmented input symbols. Vision models fit pixel distributions, while language models predict next‑token probabilities. This paradigm is essentially pattern‑matching and probabilistic speculation rather than genuine semantic understanding, and it inherently produces hallucinations, semantic drift, long‑tail failures and uncontrolled emergence. Breaking away from statistical‑learning frameworks, this paper proposes an AI‑native cognitive dynamical architecture built upon read‑only fixed semantic anchors. Instead of adopting human surface‑level grammar as internal computation rules, we construct a stack of layered transformation functions together with a graded semantic‑matching validation mechanism. Without token‑level probability sampling or massive pre‑training, five‑step cognitive dynamics (anchor assembling, verb stacking, complement modification, word‑order rearrangement and equivalent word replacement) reproduce core human‑like language comprehension and generation. A small‑scale sandbox experiment with ten basic semantic anchors demonstrates that the architecture eliminates root‑cause hallucinations and semantic drift, featuring full traceability, low computational cost and strong generalization. It represents a new human‑like cognitive paradigm alternative to statistical AI. Note: The mathematical dynamical function for semantic coupling, which inherits the prior “glue‑temporal‑grid” hypothesis, is not developed in this paper and will be presented in a follow‑up independent publication. Keywords AI‑native cognition; semantic anchor; transformation‑function stack; AI‑native grammar; statistics‑free modelling; explainable AI; hallucination mitigation

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

Context: Manual qualitative data analysis is time-intensive and can compromise validity and replicability, affecting analysis design, implementation, and reporting. Large Language Models (LLMs) enable human-bot collaboration in Software Engineering (SE), but their potential for qualitative data analysis in SE remains largely unexplored. Objective: The objective of this study is to design and develop an LLM-based multi-agent system that synergizes human decision support with AI to automate various qualitative data analysis approaches. Methods: We used LLM-based multi-agents systems to assist the qualitative data analysis process, deploying 27 agents, each responsible for a specific task, such as text summarization, initial code generation, and extracting themes and patterns. Results: The main findings are: (1) the LLM-based multi-agent system accelerates the qualitative data analysis process, (2) the system effectively automates tasks such as text summarization, initial code generation, and theme extraction, and (3) the publicly accessible code facilitates validation and further evaluation. Conclusion: The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners. Future improvements focus on enhancing multilingual performance and integrating continuous expert feedback. The source code of proposed system and system details can be found here: https://github.com/GPT-Laboratory/Qualitative-Analysis-with-an-LLM-Based-Agentts

Engineering a Governance-Aware AI Sandbox: Design, Implementation, and Lessons Learned

Collaborative AI experimentation in industry-academia requires environments that support rapid trials while maintaining controlled access, organisational isolation, and traceable workflows. Although interest in AI sandboxes is increasing, practical guidance on designing and building governance-aware experimentation platforms remains limited. This work designs and operationalizes a governance-aware, multi-tenant AI sandbox that supports structured experimentation and produces reusable evaluation evidence across stakeholders. The sandbox was developed in an industry-academia ecosystem using iteratively validated requirements gathered from industrial partners. The solution adopts a layered reference architecture that separates a multi-tenant presentation layer from a backend control plane and isolates execution and data management concerns into dedicated layers. The sandbox supports governed onboarding, project-based collaboration, controlled access to AI services, and traceable experimentation through approval workflows and audit logging. By structuring experiment context and governance decisions as persistent records, the sandbox enables evaluation evidence to be reused and compared across projects and stakeholders. The development experience yields lessons learned and practical considerations that inform deployment and future evolution of governance-aware sandbox platforms.

TERE4AI: A Knowledge Graph-Based Tool for Generating EU AI Act Compliant Requirements

AI Sandbox: Technical Report

Collaborative AI experimentation across industry and academia requires platforms that enable rapid prototyping while preserving controlled access, tenant separation, and transparent workflows. Despite growing interest in AI sandboxes, there is still limited practical guidance on how to design and implement platforms that integrate experimentation capabilities with governance requirements. This work presents the design and implementation of a governance-aware, multi-tenant AI sandbox for structured experimentation and the generation of reusable evaluation evidence across projects and stakeholder groups. The sandbox was developed within an industry-academia collaboration based on requirements that were iteratively refined with industrial partners. Its reference architecture separates the multi-tenant user interface from the backend control plane and places execution and data-management functions in dedicated layers. The platform supports governed user onboarding, project-centered collaboration, managed access to AI services, approval workflows, audit logging, and traceable experimentation. Experiment configurations, contextual information, and governance decisions are stored as persistent records, allowing evidence and outcomes to be compared and reused across projects. The development process provides practical lessons for deploying and extending governance-aware AI sandbox platforms in collaborative research and industrial environments.

Agentic Frameworks for Reasoning Tasks: An Empirical Study

Recent advances in agentic frameworks have enabled AI agents to perform complex reasoning and decision-making. However, evidence comparing their reasoning performance, efficiency, and practical suitability remains limited. To address this gap, we empirically evaluate 22 widely used agentic frameworks across three reasoning benchmarks: BBH, GSM8K, and ARC. The frameworks were selected from 1,200 GitHub repositories collected between January 2023 and July 2025 and organized into a taxonomy based on architectural design. We evaluated them under a unified setting, measuring reasoning accuracy, execution time, computational cost, and cross-benchmark consistency. Our results show that 19 of the 22 frameworks completed all three benchmarks. Among these, 12 showed stable performance, with mean accuracy of 74.6-75.9%, execution time of 4-6 seconds per task, and cost of 0.14-0.18 cents per task. Poorer results were mainly caused by orchestration problems rather than reasoning limits. For example, Camel failed to complete BBH after 11 days because of uncontrolled context growth, while Upsonic consumed USD 1,434 in one day because repeated extraction failures triggered costly retries. AutoGen and Mastra also exhausted API quotas through iterative interactions that increased prompt length without improving results. We also found a sharp drop in mathematical reasoning. Mean accuracy on GSM8K was 44.35%, compared with 89.80% on BBH and 89.56% on ARC. Overall, this study provides the first large-scale empirical comparison of agentic frameworks for reasoning-intensive software engineering tasks and shows that framework selection should prioritize orchestration quality, especially memory control, failure handling, and cost management.

Examining Privacy and Trust Issues at the Edge of Isomorphic IoT Architectures: Case Liquid AI

The growing domain of liquidity in computing extends its boundaries to include advancements like liquid artificial intelligence (AI). Liquid AI leverages liquid software using isomorphic Internet of Things (IoT) architecture to enhance computation at the edge. This innovation unveils vast opportunities yet also introduces significant challenges, particularly around privacy and trust. We explore the vulnerabilities that might hinder the progression of this technological fusion toward achieving trustworthy AI. Through an intensive examination of the literature, this research highlights the heightened threats to data integrity and stakeholder trust in these evolving ecosystems. Four main challenges: Data collection, Data storage and Access, Data utilization and sharing, and Surveillance and profiling were identified and examined under privacy, and two, Algorithms and decision-making and Security of IoT infrastructure under trust. The concerns are further categorized to highlight their impact on the development of trustworthy AI. The study acknowledges the early state of the field. Consequently, this research navigates through the limited available literature, initiating a pioneering discourse emphasizing fostering a foundation for developing secure and trustworthy Liquid AI environments.

Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development

The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activities. At the same time, organizations are increasingly embedding governance mechanisms into GenAI-assisted development to support trust, transparency, and accountability. However, these governance mechanisms introduce additional computational workloads, including repeated inference, regeneration cycles, and expanded validation pipelines, increasing energy use and the carbon footprint of GenAI-assisted development. This paper proposes Carbon-Aware Governance Gates (CAGG), an architectural extension that embeds carbon budgets, energy provenance, and sustainability-aware validation orchestration into human-AI governance layers. CAGG comprises three components: (i) an Energy and Carbon Provenance Ledger, (ii) a Carbon Budget Manager, and (iii) a Green Validation Orchestrator, operationalized through governance policies and reusable design patterns.