From Principle to Practice: Value Alignment in AI Ethics and Governance

The rapid enterprise adoption of Large Language Models (LLMs) has transformed knowledge work, decision support, and digital service delivery, yet their deployment introduces substantial risks related to data integrity, privacy exposure, algorithmic bias, hallucinated outputs, and unclear accountability across organizational boundaries. Traditional data governance and information security models were not designed to manage probabilistic, generative systems whose behaviors evolve dynamically with context, prompting the need for a new class of trustworthy AI governance mechanisms. This paper proposes a comprehensive Trustworthy AI and Data Governance Architecture that embeds integrity, ethics, and compliance controls directly into the lifecycle of enterprise LLM deployments, spanning data acquisition, model training, fine-tuning, retrieval augmentation, prompt orchestration, inference monitoring, and post-deployment auditing. The framework integrates policy-driven guardrails, lineage tracking, explainability checkpoints, human-in-the-loop oversight, and continuous risk evaluation to ensure traceability and responsible decision making. Control layers are aligned with emerging guidance from National Institute of Standards and Technology risk management principles and regulatory expectations established by the European Commission AI governance initiatives, enabling measurable assurance of fairness, reliability, and accountability. Through a design-science methodology and enterprise reference scenarios, the architecture demonstrates how organizations can operationalize ethical AI practices without sacrificing scalability or performance. The proposed model advances governance-by-design, transforming trust from an abstract principle into enforceable technical and procedural controls, thereby enabling safe, compliant, and sustainable LLM adoption across complex enterprise platforms.

In this paper, we cover approaches to systematically govern, assess and quantify bias across the complete life cycle of machine learning models, from initial development and validation to ongoing production monitoring and guardrail implementation. Building upon our foundational work on the Bias Evaluation and Assessment Test Suite (BEATS) for Large Language Models, the authors share prevalent bias and fairness related gaps in Large Language Models (LLMs) and discuss data and AI governance framework to address Bias, Ethics, Fairness, and Factuality within LLMs. The data and AI governance approach discussed in this paper is suitable for practical, real-world applications, enabling rigorous benchmarking of LLMs prior to production deployment, facilitating continuous real-time evaluation, and proactively governing LLM generated responses. By implementing the data and AI governance across the life cycle of AI development, organizations can significantly enhance the safety and responsibility of their GenAI systems, effectively mitigating risks of discrimination and protecting against potential reputational or brand-related harm. Ultimately, through this article, we aim to contribute to advancement of the creation and deployment of socially responsible and ethically aligned generative artificial intelligence powered applications.

AI models, particularly generative AI and large language models, reshape digital information ecosystems by curating and amplifying content through user engagement metrics. Despite their capacity to reduce bias and promote inclusivity, these models simultaneously amplify cognitive biases, entrench filter bubbles, and spread misinformation. The intensification of human–machine interaction and hyper-industrialization complicates this further, as large language models increasingly mediate how information is produced and consumed. Socio-technical agency describes how AI systems co-construct human behavior and societal norms through their design, yet their effects remain understudied in regions with limited technological infrastructure. This paper investigates AI’s influence on information dissemination, cognitive biases, and user agency across digital media environments in key regions of the Global South. Drawing on qualitative interviews and a survey of 580 media technology users in South Africa, Indonesia, India, the Philippines, and Brazil it examines how generative AI affects emotional engagement, exposure to content, and perceptions of digital truth. Framed by media ecology theory, the study evaluates AI as a cognitive extension that can both reinforce and challenge digital biases. The study proposes strategies for using generative AI to support information integrity while addressing the risks of polarization and exclusion. By centering perspectives from regions in the Global South, it contributes to more equitable discourse on AI governance, advocating regulatory and design solutions responsive to diverse media ecologies.

Artificial intelligence is changing industries and raising important questions about fair compensation for intellectual property owners when their work is used by AI systems. Existing research has focused on global AI regulations, particularly the European Union AI Act, but comparatively little attention has been given examination of how these frameworks compare to Malaysia’s National Guidelines on AI Governance and Ethics. This study therefore explores the legal and governance challenges related to AI’s use of copyrighted works and patented inventions in Malaysia, where current laws struggle to address issues of authorship, inventorship, and fair remuneration. Analyzing these differences highlights gaps in transparency, enforcement, and accountability in Malaysia’s AI governance. This study uses a qualitative legal research approach, reviewing policy frameworks, legal cases, and comparative governance models to propose a sustainable legal framework for AI in Malaysia. This framework provides lawmakers with actionable steps to position Malaysia as a regional leader in ethical AI governance. The findings suggest that regulatory shortcomings must be addressed through clearer legal provisions, stronger enforcement mechanisms, and structured remuneration models to support AI-driven innovation while protecting the rights of IP owners. The study contributes to the discussion on AI governance by offering policy recommendations that balance technological progress with the protection of intellectual property in Malaysia’s growing digital economy.

BackgroundArtificial intelligence (AI) systems are widely used in the health care sector. Mainly applied for individualized care, AI is increasingly aimed at population health. This raises important ethical considerations but also calls for responsible governance, considering that this will affect the population. However, the literature points to a lack of citizen participation in the governance of AI in health. Therefore, it is necessary to investigate the governance of the ethical and societal implications of AI in population health.ObjectiveThis study aimed to explore the perspectives and attitudes of citizens and experts regarding the ethics of AI in population health, the engagement of citizens in AI governance, and the potential of a digital app to foster citizen engagement.MethodsWe recruited a panel of 21 citizens and experts. Using a web-based survey, we explored their perspectives and attitudes on the ethical issues of AI in population health, the relative role of citizens and other actors in AI governance, and the ways in which citizens can be supported to participate in AI governance through a digital app. The responses of the participants were analyzed quantitatively and qualitatively.ResultsAccording to the participants, AI is perceived to be already present in population health and its benefits are regarded positively, but there is a consensus that AI has substantial societal implications. The participants also showed a high level of agreement toward involving citizens into AI governance. They highlighted the aspects to be considered in the creation of a digital app to foster this involvement. They recognized the importance of creating an app that is both accessible and transparent.ConclusionsThese results offer avenues for the development of a digital app to raise awareness, to survey, and to support citizens’ decision-making regarding the ethical, legal, and social issues of AI in population health.

As artificial intelligence AI technologies continue to advance, the need for effective governance, the need for effective governance frameworks has become increasingly pressing. The rise of generative AIs capable of producing synthetic data, images, and text has raised significant concerns regarding intellectual property, accountability, and the potential for misuse. This article explores the key challenges associated with generative AIs and proposes a comprehensive approach to AI governance, incorporating regulatory reforms, technological innovations, and public awareness initiatives. This article addresses the pressing need for robust AI governance frameworks in response to the growing influence of generative AI technologies. It highlights the legal, ethical, and regulatory challenges, particularly around intellectual property and accountability. The article advocates for reforms in regulation, technological innovations, and public awareness initiatives, proposing solutions such as AI driven tools for disinformation detection and the AI Risk Atlas for identifying risks. The purpose of this article is to explore the challenges posed by generative AI technologies and propose a comprehensive governance framework to address these challenges. This study is significant because it provides a timely analysis of the governance needs for generative AI, which is crucial for ensuring that AI benefits are realized without compromising ethical and legal standards.

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

<sec> <title>BACKGROUND</title> Patient recruitment remains a major bottleneck in clinical trials. Large language models (LLMs) show promise for patient–trial matching and eligibility screening, but existing work emphasizes algorithmic performance in controlled settings. Less is known about how LLMs should be embedded in multi-stakeholder recruitment workflows where human oversight, process design, and economic incentives jointly determine outcomes. </sec> <sec> <title>OBJECTIVE</title> To propose a theory-grounded conceptual framework—the LLM-Embedded Clinical Recruitment Architecture (LECRA)—that explains how LLM integration in clinical trial recruitment may influence operational and economic outcomes through intermediate mechanisms involving data complexity, LLM processing, and human–AI collaboration. </sec> <sec> <title>METHODS</title> We synthesize evidence from recent empirical and review studies on LLM-based patient–trial matching, human–AI teaming in clinical settings, and AI governance in healthcare. Socio-technical systems theory is used to justify joint optimization of technical and social subsystems, and transaction cost economics is used to interpret information-processing costs, uncertainty, and scale effects. The framework is specified as four interdependent layers, and six propositions are derived for future empirical testing. </sec> <sec> <title>RESULTS</title> LECRA distinguishes (1) data complexity, (2) LLM processing, (3) human–AI collaboration, and (4) economic impact. The framework posits that LLM value is not solely a property of model accuracy; collaboration design (e.g., teaming mode, oversight, explainability) mediates how technical capability translates into recruitment performance, and economic returns may increase with scale as fixed deployment costs are spread across cases. The propositions link complexity, teaming, and scale to expected differences in processing efficiency and cost-related outcomes. </sec> <sec> <title>CONCLUSIONS</title> This conceptual work reframes LLM-enabled recruitment as a socio-technical and economic problem, not only an algorithmic one. It offers a structured basis for empirical studies—especially quasi-experimental evaluations in organizational settings—and for practice by highlighting workflow integration and governance choices that determine whether LLM adoption realizes its potential benefits. </sec> <sec> <title>CLINICALTRIAL</title> Not applicable. This manuscript presents a conceptual framework and literature synthesis; it does not report a prospectively registered interventional clinical trial. </sec>

The rapid deployment of large language models (LLMs) on mobile devices has introduced significant privacy concerns, particularly regarding data collection, user profiling, and compliance with evolving AI regulations such as the GDPR and the AI Act. While these on-device LLMs promise improved latency and user experience, their potential to inadvertently leak sensitive information remains understudied. This paper proposes a red teaming framework to systematically assess the privacy risks of phone-based LLMs, simulating adversarial attacks to identify vulnerabilities in model behavior, data storage, and inference processes. We evaluate popular mobile LLMs under scenarios such as prompt injection, side-channel exploitation, and unintended memorization, measuring their compliance with strict privacy-by-design principles. Our findings reveal critical gaps in current safeguards, including susceptibility to context-aware deanonymization and insufficient data minimization. We further discuss regulatory implications, advocating for adaptive red teaming as a mandatory evaluation step in AI governance. By integrating adversarial testing into the development lifecycle, stakeholders can preemptively align phone-based AI systems with legal and ethical privacy standards while maintaining functional utility.

Generative AI has drawn significant attention from stakeholders in higher education. As it introduces new opportunities for personalized learning and tutoring support, it simultaneously poses challenges to academic integrity and leads to ethical issues. Consequently, governing responsible AI usage within higher education institutions (HEIs) becomes increasingly important. Leading universities have already published guidelines on Generative AI, with most attempting to embrace this technology responsibly. This study provides a new perspective by focusing on strategies for responsible AI governance as demonstrated in these guidelines. Through a case study of 14 prestigious universities in the United States, we identified the multi-unit governance of AI, the role-specific governance of AI, and the academic characteristics of AI governance from their AI guidelines. The strengths and potential limitations of these strategies and characteristics are discussed. The findings offer practical implications for guiding responsible AI usage in HEIs and beyond.

This chapter critically examines the ethical, legal, and societal implications of integrating large language models (LLMs) into political and economic structures. By drawing from interdisciplinary perspectives in psychology, AI ethics, and legal scholarship, it explores the evolving landscape of AI governance and its impact on society. The discussion focuses on the challenges of granting AI autonomy and agency, assessing how LLMs influence decision-making and governance. A key aspect of the chapter is its proposal for a framework of “Constitutional AI,” which seeks to align AI decision-making with constitutional values such as fairness, justice, and transparency. It highlights the need for explainable AI (XAI) techniques, robust governance policies, and ethical considerations in AI system design. The potential risks of AI misuse, manipulation, and opacity are also addressed, emphasizing accountability and user empowerment. The chapter further examines psychological and philosophical concepts like agency, normativity, and the metaphysics of self-constitution, linking them to AI’s role in human decision-making. Ultimately, it advocates for AI systems that operate in a safe, secure, and trustworthy manner, ensuring their development benefits society while maintaining ethical integrity and legal compliance.

CAMERON VU, Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443, and DARIA PANOVA, Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443, and JOSIAH KOWALSKI, Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443, and Dr. W. LIAO (Faculty Advisor), Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443, and Dr. O. Guzide (Faculty Advisor), Dept of Computer Science and Math &amp; ENGR, Shepherd University, Shepherdstown, WV, 25443. Smart Parking Space Detection with Generative Artificial Intelligence and Large Language Models. The increasing relevance of generative AI and large language models is reshaping various sectors of modern society. These advancements have spurred notable progress in fields such as healthcare, finance, and education. Yet, the application of AI extends beyond expert domains, offering simplified solutions to everyday tasks for the general populace. This project harnesses the power of generative artificial intelligence and large language models to develop a practical application: smart parking space detection. By leveraging these technologies, individuals can effortlessly ascertain the availability of parking spots in monitored lots via camera or photographic monitoring, facilitated by a straightforward algorithm. The overarching objective is twofold: to engineer a user-friendly system utilizing generative AI principles and to demonstrate the potential for such technologies to enhance the daily experiences of ordinary individuals.

The current endeavor of moving AI ethics from theory to practice can frequently be observed in academia and industry and indicates a major achievement in the theoretical understanding of responsible AI. Its practical application, however, currently poses challenges, as mechanisms for translating the proposed principles into easily feasible actions are often considered unclear and not ready for practice. In particular, a lack of uniform, standardized approaches that are aligned with regulatory provisions is often highlighted by practitioners as a major drawback to the practical realization of AI governance. To address these challenges, we propose a stronger shift in focus from solely the trustworthiness of AI products to the perceived trustworthiness of the development process by introducing a concept for a trustworthy development process for AI systems. We derive this process from a semi-systematic literature analysis of common AI governance documents to identify the most prominent measures for operationalizing responsible AI and compare them to implications for AI providers from EU-centered regulatory frameworks. Assessing the resulting process along derived characteristics of trustworthy processes shows that, while clarity is often mentioned as a major drawback, and many AI providers tend to wait for finalized regulations before reacting, the summarized landscape of proposed AI governance mechanisms can already cover many of the binding and non-binding demands circulating similar activities to address fundamental risks. Furthermore, while many factors of procedural trustworthiness are already fulfilled, limitations are seen particularly due to the vagueness of currently proposed measures, calling for a detailing of measures based on use cases and the system’s context.

<sec><title>Objective</title> As urban design faces increasing demands for contextual responsiveness, iterative optimization, and data-informed reasoning, integrating artificial intelligence into the design process has gained renewed relevance. Among emerging technologies, generative artificial intelligence (GAI) shows strong potential for automating content creation and simulating spatial configurations. This research provides a comprehensive review of recent developments in the application of GAI to urban design. The research identifies representative technical pathways, their respective intervention stages, and the functional mechanisms by which generative models are reshaping the design workflow. This research presents a structured, theory-informed synthesis of how different generative models contribute to tasks such as intention modeling, spatial reasoning, and performance-driven design. Building on design thinking and a descriptive lens informed by the technology acceptance model (TAM), the research examines how model type, data modality, and task characteristics affect GAI’s functional role, usability, and acceptance. Particular attention is given to mapping deployment forms, from isolated tools to coordinated multi-model workflows, and to characterizing cross-cutting challenges of controllability, transparency, and contextual adaptability in urban design settings. </sec><sec><title>Methods</title> Following the PRISMA protocol, the research conducts a multi-stage literature review combining automated search and expert screening. A total of 125 peer-reviewed articles and high-impact preprints are selected from Web of Science, CNKI, arXiv, and selected industry sources, covering the period from 2014 to July 2025. Search terms such as “generative AI”, “AIGC”, “GAN”, “diffusion model”, “variational autoencoder”, “autoregressive model”, “large language model”, and urban-related keywords are used in various combinations. Based on the collected literature, four types of generative models are summarized as image-driven, language-driven, structure-driven, and feedback-optimized models, according to their application characteristics in urban design tasks. These types are aligned with four stages of the design process: preliminary analysis, scheme generation, evaluation and decision-making, and outcome expression. On this basis, a two-dimensional framework to examine how different GAI pathways intervene across tasks is formed. To refine the mapping, each design stage is further broken down into three representative sub-tasks. Preliminary analysis includes public demand analysis, urban data enhancement, case/task framing, and spatial element recognition. Scheme generation covers design intention modeling, spatial layout generation, and 3D form construction. The evaluation and decision-making stage includes multi-objective optimization, scheme evaluation, and scenario prediction. The final expression stage involves textual documentation, 2D representation, and visual rendering. A quantitative analysis is also conducted to show the distribution of model types over design stages, identify common combinations, and trace the evolution of research focus over time. TAM informs a descriptive synthesis of perceived usefulness (PU) and perceived ease of use (PEU) across model types to illuminate adoption patterns. </sec><sec><title>Results</title> The findings reveal that GAI models are increasingly integrated into urban design workflows but exhibit uneven adoption across task types and modalities. Image-driven models dominate in both early-stage analysis and final visual representation due to their high interpretability, usability, and compatibility with existing design practices. Language-driven models are commonly used in public demand analysis, participatory planning, and scenario scripting, enabled by the rise of large language models (LLMs) such as ChatGPT and DeepSeek. Structure-driven models, though less prevalent, show promise in generating street networks, land-parcel layouts, and spatial typologies using graph-based logic. Feedback-optimized models, which rely on reinforcement learning, evolutionary algorithms, and performance simulation are the least adopted, but demonstrate strong potential in multi-objective optimization and iterative decision-making. Recent research indicates an increasing use of multi-model workflows, such as text-to-image pipelines integrated with urban simulation or feedback loops. While GAI applications increasingly support design iteration, their adoption is heavily influenced by the controllability, explainability, and contextual adaptability of models. PU and PEU vary significantly by model type, with image-driven models rated highest and structure-driven and feedback-optimized models facing usability challenges due to complexity and low transparency. </sec><sec><title>Conclusion</title> Although GAI has demonstrated broad applicability across the urban design process, current implementations are largely procedural and auxiliary in nature. Most models recombine existing inputs rather than construct original logic, and few possess autonomous reasoning or normative awareness. This limits their role to content augmentation rather than conceptual guidance in design development. Moreover, issues such as opaque decision logic, lack of domain-specific knowledge embedding, and poor adaptability to local planning norms hinder practical adoption. Addressing these challenges requires multi-level efforts: 1) Construct structured, regionally grounded urban design datasets; 2) improve model interpretability, controllability, and responsiveness to professional input; and 3) develop modular, multi-model systems that support seamless interaction across design stages. Human – AI collaboration mechanisms — especially those based on iterative prompts and semantic feedback-must be enhanced to enable AI not just as a tool, but as an active design partner. This review offers a comprehensive reference for scholars and practitioners seeking to understand how GAI is reshaping the logic, structure, and agency of urban design. </sec>

The emergence of large language models (LLMs) has ushered in a new era of generative AI, exhibiting broad human-level capabilities and achieving meaningful progress toward Artificial General Intelligence (AGI). To realize AGI that truly benefits each individual, one next key direction for generative AI is to develop personal intelligence -understanding individuals and enhancing personal thinking, planning, and life experiences through personalization. Although evolving generative AI toward personal intelligence is emerging, it has attracted considerable interest from both research institutions and industry, even becoming a strategic direction for many companies. This workshop aims to provide a platform for discussing innovative ideas that facilitate personal intelligence with generative AI, 1) envisioning the future of personal intelligence powered by generative AI, 2) item establishing benchmarks for personal intelligence tasks, 3) advancing user modeling/personalization techniques for generative models/agents, 4) enhancing real-world applications, and 4) ensuring privacy and trustworthiness. This workshop aims to deepen understanding, accelerate progress, and support the transformative development of personal intelligence. The website for the workshop is: https://generative-rec.github.io/workshop-www25.