Hasil untuk "Business ethics"

Weilun Xu, Alexander Rusnak, Frederic Kaplan

When large language models make ethical judgments, do their internal representations distinguish between normative frameworks, or collapse ethics into a single acceptability dimension? We probe hidden representations across five ethical frameworks (deontology, utilitarianism, virtue, justice, commonsense) in six LLMs spanning 4B--72B parameters. Our analysis reveals differentiated ethical subspaces with asymmetric transfer patterns -- e.g., deontology probes partially generalize to virtue scenarios while commonsense probes fail catastrophically on justice. Disagreement between deontological and utilitarian probes correlates with higher behavioral entropy across architectures, though this relationship may partly reflect shared sensitivity to scenario difficulty. Post-hoc validation reveals that probes partially depend on surface features of benchmark templates, motivating cautious interpretation. We discuss both the structural insights these methods provide and their epistemological limitations.

Yik Chan Chin, David A. Raho, Hag-Min Kim et al.

This policy report draws on country studies from China, South Korea, Singapore, and the United Kingdom to identify effective tools and key barriers to interoperability in AI safety governance. It offers practical recommendations to support a globally informed yet locally grounded governance ecosystem. Interoperability is a central goal of AI governance, vital for reducing risks, fostering innovation, enhancing competitiveness, promoting standardization, and building public trust. However, structural gaps such as fragmented regulations and lack of global coordination, and conceptual gaps, including limited Global South engagement, continue to hinder progress. Focusing on three high-stakes domains - autonomous vehicles, education, and cross-border data flows - the report compares ethical, legal, and technical frameworks across the four countries. It identifies areas of convergence, divergence, and potential alignment, offering policy recommendations that support the development of interoperability mechanisms aligned with the Global Digital Compact and relevant UN resolutions. The analysis covers seven components: objectives, regulators, ethics, binding measures, targeted frameworks, technical standards, and key risks.

Javed I. Khan, Sharmila Rahman Prithula

The rapid advancements in large language models (LLMs) have revolutionized natural language processing, unlocking unprecedented capabilities in communication, automation, and knowledge generation. However, the ethical implications of LLM development, particularly in data harnessing, remain a critical challenge. Despite widespread discussion about the ethical compliance of LLMs -- especially concerning their data harnessing processes, there remains a notable absence of concrete frameworks to systematically guide or measure the ethical risks involved. In this paper we discuss a potential pathway for building an Ethical Risk Scoring (ERS) system to quantitatively assess the ethical integrity of the data harnessing process for AI systems. This system is based on a set of assessment questions grounded in core ethical principles, which are, in turn, supported by commanding ethical theories. By integrating measurable scoring mechanisms, this approach aims to foster responsible LLM development, balancing technological innovation with ethical accountability.

Jaemarie Solyst, Ruth Karen Nakigozi, Chloe Fong et al.

There is an increasing need for young people to become critically AI literate, understanding not only how AI works but also its limitations and ethical nuances. Yet, designing learning experiences that make such complex, serious topics engaging remains a challenge. This paper explores transformational games as a promising approach for supporting youth learning about generative AI (GenAI) and ethics. We designed and implemented two games, Diversity Duel and Secret Agent, that integrate GenAI tools with gameplay elements. This work investigates how the games' elements: (1) peer evaluation, (2) constraint-based creativity, and (3) social deduction supported socio-ethical reasoning about GenAI. Participants recognized and debated bias in GenAI outputs, connected these patterns to real-world inequities, and developed nuanced understandings of bias. Participants further came to see how prompt design shapes AI behavior. Our findings suggest that group-based games with these elements can support fostering critical AI literacy.

Shalini Chakraborty, Lola Burgueño, Nathalie Moreno et al.

Generative Artificial Intelligence (GenAI) is rapidly gaining momentum in software modeling education, embraced by both students and educators. As GenAI assists with interpreting requirements, formalizing models, and translating students' mental models into structured notations, it increasingly shapes core learning outcomes such as domain comprehension, diagrammatic thinking, and modeling fluency without clear ethical oversight or pedagogical guidelines. Yet, the ethical implications of this integration remain underexplored. In this paper, we conduct a systematic literature review across six major digital libraries in computer science (ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, SpringerLink, and Web of Science). Our aim is to identify studies discussing the ethical aspects of GenAI in software modeling education, including responsibility, fairness, transparency, diversity, and inclusion among others. Out of 1,386 unique papers initially retrieved, only three explicitly addressed ethical considerations. This scarcity highlights the critical absence of ethical discourse surrounding GenAI in modeling education and raises urgent questions about the responsible integration of AI in modeling curricula, as well as it evinces the pressing need for structured ethical frameworks in this emerging educational landscape. We examine these three studies and explore the emerging research opportunities as well as the challenges that have arisen in this field.

Anshika Rawal, Abhijeet Kumar, Mridul Mishra

Extracting useful signals or pattern to support important business decisions for example analyzing investment product traction and discovering customer preference, risk monitoring etc. from unstructured text is a challenging task. Capturing interaction of entities or concepts and association mining is a crucial component in text mining, enabling information extraction and reasoning over and knowledge discovery from text. Furthermore, it can be used to enrich or filter knowledge graphs to guide exploration processes, descriptive analytics and uncover hidden stories in the text. In this paper, we introduce a domain independent pipeline i.e., generalized framework to enable document filtering, entity extraction using various sources (or techniques) as plug-ins and association mining to build any text mining business use-case and quantitatively define a scoring metric for ranking purpose. The proposed framework has three major components a) Document filtering: filtering documents/text of interest from massive amount of texts b) Configurable entity extraction pipeline: include entity extraction techniques i.e., i) DBpedia Spotlight, ii) Spacy NER, iii) Custom Entity Matcher, iv) Phrase extraction (or dictionary) based c) Association Relationship Mining: To generates co-occurrence graph to analyse potential relationships among entities, concepts. Further, co-occurrence count based frequency statistics provide a holistic window to observe association trends or buzz rate in specific business context. The paper demonstrates the usage of framework as fundamental building box in two financial use-cases namely brand product discovery and vendor risk monitoring. We aim that such framework will remove duplicated effort, minimize the development effort, and encourage reusability and rapid prototyping in association mining business applications for institutions.

Fanfan Lin, Peter Wilson, Xinze Li et al.

Artificial intelligence (AI) is rapidly transforming power electronics, with AI-related publications in IEEE Power Electronics Society selected journals increasing more than fourfold from 2020 to 2025. However, the ethical dimensions of this transformation have received limited attention. This article underscores the urgent need for an ethical framework to guide responsible AI integration in power electronics, not only to prevent AI-related incidents but also to comply with legal and regulatory responsibilities. In this context, this article identifies four core pillars of AI ethics in power electronics: Security & Safety, Explainability & Transparency, Energy Sustainability, and Evolving Roles of Engineers. Each pillar is supported by practical and actionable insights to ensure that ethical principles are embedded in algorithm design, system deployment, and the preparation of an AI-ready engineering workforce. The authors advocate for power electronics engineers to lead the ethical discourse, given their deep technical understanding of both AI systems and power conversion technologies. The paper concludes by calling on the IEEE Power Electronics Society to spearhead the establishment of ethical standards, talent development initiatives, and best practices that ensure AI innovations are not only technically advanced but also oriented toward human and societal benefit.

Vanessa Utz

As generative AI systems become widely adopted, they enable unprecedented creation levels of synthetic data across text, images, audio, and video modalities. While research has addressed the energy consumption of model training and inference, a critical sustainability challenge remains understudied: digital waste. This term refers to stored data that consumes resources without serving a specific (and/or immediate) purpose. This paper presents this terminology in the AI context and introduces digital waste as an ethical imperative within (generative) AI development, positioning environmental sustainability as core for responsible innovation. Drawing from established digital resource management approaches, we examine how other disciplines manage digital waste and identify transferable approaches for the AI community. We propose specific recommendations encompassing re-search directions, technical interventions, and cultural shifts to mitigate the environmental consequences of in-definite data storage. By expanding AI ethics beyond immediate concerns like bias and privacy to include inter-generational environmental justice, this work contributes to a more comprehensive ethical framework that considers the complete lifecycle impact of generative AI systems.

Chris Schmitz, Joanna Bryson

Public-sector bureaucracies seek to reap the benefits of artificial intelligence (AI), but face important concerns about accountability and transparency when using AI systems. In particular, perception or actuality of AI agency might create ethics sinks - constructs that facilitate dissipation of responsibility when AI systems of disputed moral status interface with bureaucratic structures. Here, we reject the notion that ethics sinks are a necessary consequence of introducing AI systems into bureaucracies. Rather, where they appear, they are the product of structural design decisions across both the technology and the institution deploying it. We support this claim via a systematic application of conceptions of moral agency in AI ethics to Weberian bureaucracy. We establish that it is both desirable and feasible to render AI systems as tools for the generation of organizational transparency and legibility, which continue the processes of Weberian rationalization initiated by previous waves of digitalization. We present a three-point Moral Agency Framework for legitimate integration of AI in bureaucratic structures: (a) maintain clear and just human lines of accountability, (b) ensure humans whose work is augmented by AI systems can verify the systems are functioning correctly, and (c) introduce AI only where it doesn't inhibit the capacity of bureaucracies towards either of their twin aims of legitimacy and stewardship. We suggest that AI introduced within this framework can not only improve efficiency and productivity while avoiding ethics sinks, but also improve the transparency and even the legitimacy of a bureaucracy.

Amir Reza Asadi, Shiva Ghasemi

This systematic literature review paper explores perspectives on the ideal metaverse from user experience, business, and national levels, considering both academic and industry viewpoints. The study examines the metaverse as a sociotechnical imaginary, enabled collectively by virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. Through a systematic literature review, n=144 records were included and by employing grounded theory for analysis of data, we developed three research models, which can guide researchers in examining the metaverse as a sociotechnical future of information technology. Designers can apply the metaverse user experience maturity model to develop more user-friendly services, while business strategists can use the metaverse business readiness model to assess their firms' current state and prepare for transformation. Additionally, policymakers and policy analysts can utilize the metaverse national competitiveness model to track their countries' competitiveness during this paradigm shift. The synthesis of the results also led to the development of practical assessment tools derived from these models that can guide researchers

Kyrie Zhixuan Zhou, Zachary Kilhoffer, Madelyn Rose Sanfilippo et al.

Large Language Models (LLMs) are advancing quickly and impacting people's lives for better or worse. In higher education, concerns have emerged such as students' misuse of LLMs and degraded education outcomes. To unpack the ethical concerns of LLMs for higher education, we conducted a case study consisting of stakeholder interviews (n=20) in higher education computer science. We found that students use several distinct mental models to interact with LLMs - LLMs serve as a tool for (a) writing, (b) coding, and (c) information retrieval, which differ somewhat in ethical considerations. Students and teachers brought up ethical issues that directly impact them, such as inaccurate LLM responses, hallucinations, biases, privacy leakage, and academic integrity issues. Participants emphasized the necessity of guidance and rules for the use of LLMs in higher education, including teaching digital literacy, rethinking education, and having cautious and contextual policies. We reflect on the ethical challenges and propose solutions.

Kostas Karpouzis

Gamification, the integration of game mechanics in non-game settings, has become increasingly prevalent in various digital platforms; however, its ethical and societal impacts are often overlooked. This paper delves into how Platonic and Aristotelian philosophies can provide a critical framework for understanding and evaluating the ethical dimensions of gamification. Plato's allegory of the cave and theory of forms are used to analyse the perception of reality in gamified environments, questioning their authenticity and the value of virtual achievements, while Aristotle's virtue ethics, with its emphasis on moderation, virtue, and eudaimonia (true and full happiness), can help assess how gamification influences user behaviour and ethical decision-making. The paper critically examines various gamification elements, such as the hero's journey, altruistic actions, badge levels, and user autonomy, through these philosophical lenses, and addresses the ethical responsibilities of gamification designers, advocating for a balanced approach that prioritizes user well-being and ethical development over commercial interests. By bridging ancient philosophical insights with modern digital culture, this research contributes to a deeper understanding of the ethical implications of gamification, emphasizing the need for responsible and virtuous design in digital applications.

Joschka Haltaufderheide, Robert Ranisch

With the introduction of ChatGPT, Large Language Models (LLMs) have received enormous attention in healthcare. Despite their potential benefits, researchers have underscored various ethical implications. While individual instances have drawn much attention, the debate lacks a systematic overview of practical applications currently researched and ethical issues connected to them. Against this background, this work aims to map the ethical landscape surrounding the current stage of deployment of LLMs in medicine and healthcare. Electronic databases and preprint servers were queried using a comprehensive search strategy. Studies were screened and extracted following a modified rapid review approach. Methodological quality was assessed using a hybrid approach. For 53 records, a meta-aggregative synthesis was performed. Four fields of applications emerged and testify to a vivid exploration phase. Advantages of using LLMs are attributed to their capacity in data analysis, personalized information provisioning, support in decision-making, mitigating information loss and enhancing information accessibility. However, we also identifies recurrent ethical concerns connected to fairness, bias, non-maleficence, transparency, and privacy. A distinctive concern is the tendency to produce harmful misinformation or convincingly but inaccurate content. A recurrent plea for ethical guidance and human oversight is evident. Given the variety of use cases, it is suggested that the ethical guidance debate be reframed to focus on defining what constitutes acceptable human oversight across the spectrum of applications. This involves considering diverse settings, varying potentials for harm, and different acceptable thresholds for performance and certainty in healthcare. In addition, a critical inquiry is necessary to determine the extent to which the current experimental use of LLMs is necessary and justified.

Rik Koncel-Kedziorski, Michael Krumdick, Viet Lai et al.

Answering questions within business and finance requires reasoning, precision, and a wide-breadth of technical knowledge. Together, these requirements make this domain difficult for large language models (LLMs). We introduce BizBench, a benchmark for evaluating models' ability to reason about realistic financial problems. BizBench comprises eight quantitative reasoning tasks, focusing on question-answering (QA) over financial data via program synthesis. We include three financially-themed code-generation tasks from newly collected and augmented QA data. Additionally, we isolate the reasoning capabilities required for financial QA: reading comprehension of financial text and tables for extracting intermediate values, and understanding financial concepts and formulas needed to calculate complex solutions. Collectively, these tasks evaluate a model's financial background knowledge, ability to parse financial documents, and capacity to solve problems with code. We conduct an in-depth evaluation of open-source and commercial LLMs, comparing and contrasting the behavior of code-focused and language-focused models. We demonstrate that the current bottleneck in performance is due to LLMs' limited business and financial understanding, highlighting the value of a challenging benchmark for quantitative reasoning within this domain.

Ammar Jamshed

Quantum computing is an advancing area of computing sciences and provides a new base of development for many futuristic technologies discussions on how it can help developing economies will further help developed economies in technology transfer and economic development initiatives related to Research and development within developing countries thus providing a new means of foreign direct investment(FDI) and business innovation for the majority of the globe that lacks infrastructure economic resources required for growth in the technology landscape and cyberinfrastructure for growth in computing applications. Discussion of which areas of support quantum computing can help will further assist developing economies in implementing it for growth opportunities for local systems and businesses.

Laurie A. Schintler, Connie L. McNeely, James Witte

Recent advancements in artificial intelligence (AI) systems, including large language models like ChatGPT, offer promise and peril for scholarly peer review. On the one hand, AI can enhance efficiency by addressing issues like long publication delays. On the other hand, it brings ethical and social concerns that could compromise the integrity of the peer review process and outcomes. However, human peer review systems are also fraught with related problems, such as biases, abuses, and a lack of transparency, which already diminish credibility. While there is increasing attention to the use of AI in peer review, discussions revolve mainly around plagiarism and authorship in academic journal publishing, ignoring the broader epistemic, social, cultural, and societal epistemic in which peer review is positioned. The legitimacy of AI-driven peer review hinges on the alignment with the scientific ethos, encompassing moral and epistemic norms that define appropriate conduct in the scholarly community. In this regard, there is a "norm-counternorm continuum," where the acceptability of AI in peer review is shaped by institutional logics, ethical practices, and internal regulatory mechanisms. The discussion here emphasizes the need to critically assess the legitimacy of AI-driven peer review, addressing the benefits and downsides relative to the broader epistemic, social, ethical, and regulatory factors that sculpt its implementation and impact.

Jonas Wanner, Christopher Wissuchek, Giacomo Welsch et al.

Fueled by increasing data availability and the rise of technological advances for data processing and communication, business analytics is a key driver for smart manufacturing. However, due to the multitude of different local advances as well as its multidisciplinary complexity, both researchers and practitioners struggle to keep track of the progress and acquire new knowledge within the field, as there is a lack of a holistic conceptualization. To address this issue, we performed an extensive structured literature review, yielding 904 relevant hits, to develop a quadripartite taxonomy as well as to derive archetypes of business analytics in smart manufacturing. The taxonomy comprises the following meta-characteristics: application domain, orientation as the objective of the analysis, data origins, and analysis techniques. Collectively, they comprise eight dimensions with a total of 52 distinct characteristics. Using a cluster analysis, we found six archetypes that represent a synthesis of existing knowledge on planning, maintenance (reactive, offline, and online predictive), monitoring, and quality management. A temporal analysis highlights the push beyond predictive approaches and confirms that deep learning already dominates novel applications. Our results constitute an entry point to the field but can also serve as a reference work and a guide with which to assess the adequacy of one's own instruments.

Thomas Baudel, Manon Verbockhaven, Guillaume Roy et al.

How do algorithmic decision aids introduced in business decision processes affect task performance? In a first experiment, we study effective collaboration. Faced with a decision, subjects alone have a success rate of 72%; Aided by a recommender that has a 75% success rate, their success rate reaches 76%. The human-system collaboration had thus a greater success rate than each taken alone. However, we noted a complacency/authority bias that degraded the quality of decisions by 5% when the recommender was wrong. This suggests that any lingering algorithmic bias may be amplified by decision aids. In a second experiment, we evaluated the effectiveness of 5 presentation variants in reducing complacency bias. We found that optional presentation increases subjects' resistance to wrong recommendations. We conclude by arguing that our metrics, in real usage scenarios, where decision aids are embedded as system-wide features in Business Process Management software, can lead to enhanced benefits.

Marat Akhmet, Zhanar Akhmetova, Mehmet Onur Fen

We propose a novel approach to generate chaotic business cycles in a deterministic setting. Rather than producing chaos endogenously, we consider aggregate economic models with limit cycles and equilibriums, subject them to chaotic exogenous shocks and obtain chaotic cyclical motions. Thus, we emphasize that chaotic cycles, which are inevitable in economics, are not only interior properties of economic models, but also can be considered as a result of interaction of several economical systems. This provides a comprehension of chaos (unpredictability, lack of forecasting) and control of chaos as a global economic phenomenon from the deterministic point of view. We suppose that the results of our paper are contribution to the mixed exogenous-endogenous theories of business cycles in classification by P.A. Samuelson [76]. Moreover, they demonstrate that the irregularity of the extended chaos can be structured, and this distinguishes them from the generalized synchronization. The advantage of the knowledge of the structure is that by applying instruments, which already have been developed for deterministic chaos one can control the chaos, emphasizing a parameter or a type of motion. For the globalization of cyclic chaos phenomenon we utilize new mechanisms such that entrainment by chaos, attraction of chaotic cycles by equilibriums and bifurcation of chaotic cycles developed in our earlier papers.

Dan Ladley, Ian Wilkinson, Louise Young

The development of cooperative relations within and between firms plays an important role in the successful implementation of business strategy. How to produce such relations is less well understood. We build on work in relational contract theory and the evolution of cooperation to examine the conditions under which group based incentives outperform individual based incentives and how they produce more cooperative behavior. Group interactions are modeled as iterated games in which individuals learn optimal strategies under individual and group based reward mechanisms. The space of possible games is examined and it is found that, when individual and group interests are not aligned, group evaluation and reward systems lead to higher group performance and, counter-intuitively, higher individual performance. Such groups include individuals who, quite differently to free-riders, sacrifice their own performance for the good of the group. We discuss the implications of these results for the design of incentive systems.