Generative AI tools like ChatGPT are rapidly reshaping how students and instructors engage with course material -- and how they think about academic integrity. This paper presents a classroom activity designed to help physics students critically examine the ethical and educational implications of using AI in coursework. Through a structured sequence of scenario analysis, boundary-setting, and reflective discussion, with optional individual policy writing, students develop the metacognitive, ethical, and collaborative capacities needed to navigate emerging technologies thoughtfully and responsibly. Grounded in research on social constructivist learning, metacognition, and ethics education, the activity positions students as co-creators of an engaged and reflective learning environment.
Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problems. A prominent application lies in business agents, which interact with databases and internal knowledge bases via tool calls to fulfill diverse user requirements. However, this domain is characterized by intricate data relationships and a wide range of heterogeneous tasks, from statistical data queries to knowledge-based question-answering. To address these challenges, we propose CRMWeaver, a novel approach that enhances business agents in such complex settings. To acclimate the agentic model to intricate business environments, we employ a synthesis data generation and RL-based paradigm during training, which significantly improves the model's ability to handle complex data and varied tasks. During inference, a shared memories mechanism is introduced, prompting the agent to learn from task guidelines in similar problems, thereby further boosting its effectiveness and generalization, especially in unseen scenarios. We validate the efficacy of our approach on the CRMArena-Pro dataset, where our lightweight model achieves competitive results in both B2B and B2C business scenarios, underscoring its practical value for real-world applications.
Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.
The increasing integration of artificial intelligence (AI) in digital ecosystems has reshaped privacy dynamics, particularly for young digital citizens navigating data-driven environments. This study explores evolving privacy concerns across three key stakeholder groups-young digital citizens, parents/educators, and AI professionals-and assesses differences in data ownership, trust, transparency, parental mediation, education, and risk-benefit perceptions. Employing a grounded theory methodology, this research synthesizes insights from key participants through structured surveys, qualitative interviews, and focus groups to identify distinct privacy expectations. Young digital citizens emphasized autonomy and digital agency, while parents and educators prioritized oversight and AI literacy. AI professionals focused on balancing ethical design with system performance. The analysis revealed significant gaps in transparency and digital literacy, underscoring the need for inclusive, stakeholder-driven privacy frameworks. Drawing on comparative thematic analysis, this study introduces the Privacy-Ethics Alignment in AI (PEA-AI) model, which conceptualizes privacy decision-making as a dynamic negotiation among stakeholders. By aligning empirical findings with governance implications, this research provides a scalable foundation for adaptive, youth-centered AI privacy governance.
This study explores the impact of eco-spirituality on sustainability performance and examines whether this influence is mediated by sustainable business model innovation in village-owned enterprises in Bali, a context in which business practices are highly influenced by spiritual values. Using a survey method, we collected data from 179 managers of village-owned enterprises operating across various areas in Bali. To test this hypothesis, we performed data analysis using partial least squares structural equation modeling (PLS-SEM). Our results reveal that eco-spirituality does not directly affect sustainability performance. However, it exerts a positive influence on sustainability performance through sustainable business model innovation, which serves as a crucial mediating mechanism in this relationship. Integrating eco-spirituality into business practices can serve as a source of practical wisdom, fostering sustainable practices aligned with environmental stewardship and social responsibility, while also creating an organizational environment that values spiritual and ecological principles, ultimately enhancing sustainability performance. Furthermore, this study distinguishes between village-owned enterprises operating in state villages and traditional villages, revealing that the strong spiritual foundation of traditional village-owned enterprises reinforces their commitment to sustainability by aligning business strategies with eco-spiritual values.
Kasper Kruithof, Rebecca C. Ruehle, Vivianne Dörenberg
et al.
Background: Digital assistive technology (DigAT) holds promise for reducing healthcare costs, improving access to care, and supporting independent living for older adults. However, realizing these benefits remains challenging as seemingly effective and cost-efficient forms of DigAT often fail in real-world settings due to misalignment with users’ needs, values and practices. With the ultimate aim of contributing to more effective DigAT use among community-dwelling older adults, we explored the benefits and harms they experienced using DigAT. Methods: We systematically searched PubMed, CINAHL, Philosopher's Index, and PsycINFO for qualitative studies on community-dwelling older adults' experiences with DigAT, and conducted an interpretative synthesis of thirty-one studies. Results: As intended, DigAT resulted in experienced benefits related to health, safety, self-reliance, wellbeing, motivation, empowerment, and access to care. Autonomy and independence were mostly framed as aspirational benefits, dependent on maintaining health and safety to age-in-place. Unexpected benefits of DigAT included self-confidence, feeling cared for, and social inclusion. However, users also reported various harms, including perceived unsafety, burdening others or being burdened, privacy concerns, feeling controlled and judged, alienation, powerlessness, loneliness, stigma, and emotional distress. When DigAT did not align with users' needs, values, or practices, it resulted in misuse, non-use, or adapted use. Conclusions: Our synthesis highlights the need for an intentional and person-centered approach to DigAT design and implementation, ensuring alignment with older adults' needs, values, and practices. Such an approach could enhance DigAT's perceived value, and thereby support the realization of its promise to increase healthcare accessibility and support independent living.
Climate change-related disinformation is omnipresent. The Czech Republic is an EU member state where chain emails are heavily used for it. However, the understanding of their motivation, mechanisms, and targets is limited. Given the current developments, it is critical to fill this gap by employing current data and conducting a national case study. The Czech Elves, an informal civic movement fighting against disinformation, maintain a public database, Eldariel, which contains and categorizes disinformation emails, including 50 Czech chain emails about the EU’s commitment to the environment and climate change sent between January and May 2025. Additionally, seven recipients of these chain emails were located and interviewed. Hence, a Czech case study is built on a reciprocal snowball triangular mechanism to address four research questions based on subject headings, assigned tags, and the content of these emails, as well as their perception by their recipients, to examine how climate change information is manipulated. Consequently, this article engages with a pioneering, holistic, and systematic ten-step analysis of these chain emails and their perception, addressing how they attract attention (RQ1), where and in what context (RQ2), what they challenge (RQ3), and why they do so (RQ4). The juxtaposition of findings regarding these four research questions reveals how climate change information, particularly regarding the European Green Deal, is leveraged within the EU to support disinformation campaigns, both related to and unrelated to environmental protection. This calls for further longitudinal studies encompassing the jurisdictions of other EU member states.
In the face of growing global social, economic, and environmental challenges, social entrepreneurship is gaining recognition as a viable model for sustainable value creation. Social enterprises aim to generate income, address complex social issues, and contribute to long-term societal well-being. Despite growing academic interest in social value, there remains a lack of empirical research on how social value is practically created and evaluated, particularly in emerging contexts such as Lithuania. This study seeks to bridge that gap by examining the process of creating social value within Lithuanian social enterprises. Drawing on a multiple case study approach, the article presents and compares seven Lithuanian social enterprises’ semi-structured interviews. The findings illustrate the multidimensional nature of social value creation, highlighting how Lithuanian social enterprises foster social inclusion, community empowerment, and sustainable impact despite systemic challenges such as unclear legal frameworks and limited financial resources. The study contributes to a deeper theoretical understanding of social value creation and provides practical insights for policymakers and social innovation practitioners seeking to strengthen the ecosystem for social entrepreneurship in Lithuania.
This study examines the effects of environmental films on audience beliefs and perceptions regarding food system issues. Specifically, it evaluates the impact of viewing documentary Food, Inc. and fictional film Okja on undergraduate film students. Using a Stimulus-Organism-Response framework, the researchers conducted pre-and post-questionnaires to measure participant motivations, beliefs, and perceptions. Statistical analysis using paired-sample t-tests revealed no significant differences in belief evaluations before and after viewing either film. However, both films observed substantial changes in perception assessments, with Okja demonstrating a stronger effect than Food, Inc. The study found that while both films increased audience knowledge about food systems and sustainability issues, they did not significantly alter food consumption habits. Instead, the films functioned as moral fables, providing a foundation for environmental ethics rather than directly influencing behavior. Food, Inc. primarily addressed instrumental-value ecological ethics, focusing on human health and business practices, while Okja emphasized intrinsic-value ethics, particularly animal rights. The research challenges the notion that environmental films’ primary purpose is to change audience behavior directly, arguing instead that they serve as a form of ecological communication with distinct moral and political agendas. The study concludes that these films engage in moral pedagogy to promote environmental ethics by narrating injustices in the existing food system, ultimately aiming to influence social change through policies rather than individual actions. This research contributes to understanding the nuanced effects of environmental films on audience perceptions and beliefs, highlighting the importance of considering both media logic and media effects in analyzing their impact.
As artificial intelligence (AI) continues to rapidly advance, there is a growing demand to integrate AI capabilities into existing business applications. However, a significant gap exists between the rapid progress in AI and how slowly AI is being embedded into business environments. Deploying well-performing lab models into production settings, especially in on-premise environments, often entails specialized expertise and imposes a heavy burden of model management, creating significant barriers to implementing AI models in real-world applications. KModels leverages proven libraries and platforms (Kubeflow Pipelines, KServe) to streamline AI adoption by supporting both AI developers and consumers. It allows model developers to focus solely on model development and share models as transportable units (Templates), abstracting away complex production deployment concerns. KModels enables AI consumers to eliminate the need for a dedicated data scientist, as the templates encapsulate most data science considerations while providing business-oriented control. This paper presents the architecture of KModels and the key decisions that shape it. We outline KModels' main components as well as its interfaces. Furthermore, we explain how KModels is highly suited for on-premise deployment but can also be used in cloud environments. The efficacy of KModels is demonstrated through the successful deployment of three AI models within an existing Work Order Management system. These models operate in a client's data center and are trained on local data, without data scientist intervention. One model improved the accuracy of Failure Code specification for work orders from 46% to 83%, showcasing the substantial benefit of accessible and localized AI solutions.
José Antonio Siqueira de Cerqueira, Mamia Agbese, Rebekah Rousi
et al.
AI-based systems, including Large Language Models (LLM), impact millions by supporting diverse tasks but face issues like misinformation, bias, and misuse. AI ethics is crucial as new technologies and concerns emerge, but objective, practical guidance remains debated. This study examines the use of LLMs for AI ethics in practice, assessing how LLM trustworthiness-enhancing techniques affect software development in this context. Using the Design Science Research (DSR) method, we identify techniques for LLM trustworthiness: multi-agents, distinct roles, structured communication, and multiple rounds of debate. We design a multi-agent prototype LLM-MAS, where agents engage in structured discussions on real-world AI ethics issues from the AI Incident Database. We evaluate the prototype across three case scenarios using thematic analysis, hierarchical clustering, comparative (baseline) studies, and running source code. The system generates approximately 2,000 lines of code per case, compared to only 80 lines in baseline trials. Discussions reveal terms like bias detection, transparency, accountability, user consent, GDPR compliance, fairness evaluation, and EU AI Act compliance, showing this prototype ability to generate extensive source code and documentation addressing often overlooked AI ethics issues. However, practical challenges in source code integration and dependency management may limit its use by practitioners.
In light of the rise of generative AI and recent debates about the socio-political implications of large-language models and chatbots, this article investigates the E.U.'s Artificial Intelligence Act (AIA), the world's first major attempt by a government body to address and mitigate the potentially negative impacts of AI technologies. The article critically analyzes the AIA from a distinct economic ethics perspective, i.e., ordoliberalism 2.0 - a perspective currently lacking in the academic literature. It evaluates, in particular, the AIA's ordoliberal strengths and weaknesses and proposes reform measures that could be taken to strengthen the AIA.
Thibault Douzon, Stefan Duffner, Christophe Garcia
et al.
Transformer-based Language Models are widely used in Natural Language Processing related tasks. Thanks to their pre-training, they have been successfully adapted to Information Extraction in business documents. However, most pre-training tasks proposed in the literature for business documents are too generic and not sufficient to learn more complex structures. In this paper, we use LayoutLM, a language model pre-trained on a collection of business documents, and introduce two new pre-training tasks that further improve its capacity to extract relevant information. The first is aimed at better understanding the complex layout of documents, and the second focuses on numeric values and their order of magnitude. These tasks force the model to learn better-contextualized representations of the scanned documents. We further introduce a new post-processing algorithm to decode BIESO tags in Information Extraction that performs better with complex entities. Our method significantly improves extraction performance on both public (from 93.88 to 95.50 F1 score) and private (from 84.35 to 84.84 F1 score) datasets composed of expense receipts, invoices, and purchase orders.
The rise of information technology has transformed the business landscape, with organizations increasingly relying on information systems to collect and store vast amounts of data. To stay competitive, businesses must harness this data to make informed decisions that optimize their actions in response to the market. Business intelligence (BI) is an approach that enables organizations to leverage data-driven insights for better decision-making, but implementing BI comes with its own set of challenges. Accordingly, understanding the key factors that contribute to successful implementation is crucial. This study examines the factors affecting the implementation of BI projects by analyzing the interactions between these factors using system dynamics modeling. The research draws on interviews with five BI experts and a review of the background literature to identify effective implementation strategies. Specifically, the study compares traditional and self-service implementation approaches and simulates their respective impacts on organizational acceptance of BI. The results show that the two approaches were equally effective in generating organizational acceptance until the twenty-fifth month of implementation, after which the self-service strategy generated significantly higher levels of acceptance than the traditional strategy. In fact, after 60 months, the self-service approach was associated with a 30% increase in organizational acceptance over the traditional approach. The paper also provides recommendations for increasing the acceptance of BI in both implementation strategies. Overall, this study underscores the importance of identifying and addressing key factors that impact BI implementation success, offering practical guidance to organizations seeking to leverage the power of BI in today's competitive business environment.
Abstract Compared with other aquaculture issues, attention to human and social dimensions is lagging behind. Sectoral development, policy, and programmatic factors have created inequities and sub‐optimal social outcomes, which are jeopardizing the broader contribution the sector could make to human well‐being. Human rights in aquaculture are at the core of this article, which argues that aquaculture development, as a major economic and food producing sector, needs to be about human development. The article reviews: the application of human rights in aquaculture, and the related right to decent work; the notions of justice and equity including the idea of Blue Justice and its relevance in aquaculture; and ethics and social license to operate with the challenges that business ethics and public acceptance pose to the sector. It also reviews how these issues affect people: women, along with slow progress in gender equality in the sector; youth and their engagement in aquaculture, while noting that ‘youth’ does not equate to “jobs” and requires the lifting of many more societal hurdles for their full participation in the sector; indigenous people and local ecological knowledge—a precious asset for future aquaculture as well as the survival and enhancement of the cultural value of aquaculture; and people with disabilities and other minorities who have yet to become fully visible and accounted for in aquaculture development. Redressing human and social issues in aquaculture, and placing people at the center of aquaculture development requires a fundamental change from business as usual. To humanize aquaculture development, a renewed human relationship with aquaculture is proposed, which is founded on recognizing substantive equality and agency, embracing intersectionality, that is, the multiple social dimensions of identity and interaction, and valuing cross‐disciplinary knowledge systems. It would be implemented through new, inclusive, business models, social provisioning approaches, and procedural justice and governance mechanisms for overcoming inequalities. Public, private, and non‐state actors will need to be involved, inclusive of small‐scale farmers, women, youth, people with disabilities, and indigenous communities as key groups. Six key messages conclude the article.
Hacer un análisis objetivo del ejercicio de la autonomía en la persona con una discapacidad puede llegar a ser muy complejo, si se tiene en cuenta de antemano la carga importante de pesadumbre derivada de las limitaciones soportadas por el individuo, secundaria a la naturaleza misma de su condición y los factores externos implicados. Revisar algunos conceptos teóricos sobre las posibles variables en juego en la toma de decisiones en la discapacidad, puede brindar una visión más clara al respecto, evitando caer en prejuicios o no dar el valor apropiado a la manifestación de la voluntariedad de la persona, situaciones con un impacto significativo en la bioética clínica.
Medical philosophy. Medical ethics, Business ethics
Today, the texts governing the Algerian economy claim equality between men and women in the labour market: no training, no profession, and no position of responsibility is legally closed to women. Algerian law prohibits all discrimination in hiring and career development. The imbalance has even been reversed in one essential respect: women are entering the university field more than men and are more successful. However, they account for only 19% (ONS, 2020) of the working population and are still only marginally present in positions of responsibility in the public and private sectors. However, this progressive and constant access of women to so-called 'male' bastions has led to the emergence of a professional category, namely female managers. The reason that led us to choose this research object is the desire to understand and apprehend the professional pathway of this category of women (executives), to attempt, through professional and family representations and perceptions, to understand the identity construction of women occupying executive positions. In this perspective, empirical research built on a qualitative approach, based on semi-directive interviews with 20 women executives working in a public paramilitary institution, appeared relevant. This research aimed to understand the career path through the professional experiences of women managers insofar as it highlighted professional trajectories interacting with family life and different professional rhythms in terms of career. More generally, how women managers in this institution articulate their private and professional life and place them in an organisational context. In this sense, studying the practices and social representations of women managers means understanding the codes, values, and ideologies that women's work occupies in Algerian society as a whole and this paramilitary institution.
Robert Carruthers, Isabel Straw, James K Ruffle
et al.
Equity is widely held to be fundamental to the ethics of healthcare. In the context of clinical decision-making, it rests on the comparative fidelity of the intelligence -- evidence-based or intuitive -- guiding the management of each individual patient. Though brought to recent attention by the individuating power of contemporary machine learning, such epistemic equity arises in the context of any decision guidance, whether traditional or innovative. Yet no general framework for its quantification, let alone assurance, currently exists. Here we formulate epistemic equity in terms of model fidelity evaluated over learnt multi-dimensional representations of identity crafted to maximise the captured diversity of the population, introducing a comprehensive framework for Representational Ethical Model Calibration. We demonstrate use of the framework on large-scale multimodal data from UK Biobank to derive diverse representations of the population, quantify model performance, and institute responsive remediation. We offer our approach as a principled solution to quantifying and assuring epistemic equity in healthcare, with applications across the research, clinical, and regulatory domains.
The main purpose of the research is to estimate the extent of excess Covid-19 cases and mortalities in India and examine its relationship with the degree of economic progress in various parts of the country especially given the uneven nature of the impact of the pandemic throughout the nation. The main hypotheses of the study were: 1) in areas with a high level of income per capita, the death rate per 100,000 population will be lower; 2) areas with a high level of income per capita tend to be more urbanized, economically active, and therefore quite densely populated, which increases the probability of morbidity and mortality. The object of research is over 20 million Covid-19 cases and over 370,000 deaths in 31 States and Union Territories (UTs) in India beginning in the first months of the pandemic and going through the middle of 2021. The methodological tools of the conducted research were the methods of regression analysis. The study of a relative measure of success in pandemic management (less than one-half of the median death rate as the relative threshold for measuring success) empirically confirms and theoretically proves that India had at least 16.6 million excess Covid-19 cases and over 228,000 excess COVID-19 deaths as of June 18, 2021. The paper presents the results of an empirical analysis of the relationship between excess deaths of the population from Covid-19 and state-level per-capita income (as an explanatory variable), which testified that about 60% of actual and excess deaths can be explained by the per-capita income alone. According to the results of the analysis, it was proved that actual and excess deaths are both higher in richer states. Poorer states did considerably well in keeping Covid-19 mortality low compared to their more affluent counterparts. The positive relationship between Covid-19 mortality and per-capita income does not go away even after controlling for the caseloads used as a proxy for the spread of the pandemic. This augmented model explains about 80% of the actual and excess deaths from the Covid-19 pandemic in India. After controlling for caseloads, a thousand Rupees increase in per-capita income contributed to about 15 additional deaths per 100,000 population. The article presents the results of an empirical analysis of the relationship between economic development (as measured by the per-capita income) and excess mortality from COVID-19, which proved a positive relationship between them and proved a potentially adverse impact of economic progress on human immunity, especially if population density, living conditions, and food security moderate that relationship.