Hasil untuk "Business ethics"

P. Beurden, T. Gössling

T. Jones, A. Wicks

R. Freeman, R. Phillips, R. Sisodia

Ioanna Boulouta

D. Greene, A. Hoffmann, Luke Stark

This paper uses frame analysis to examine recent high-profile values statements endorsing ethical design for artificial intelligence and machine learning (AI/ML). Guided by insights from values in design and the sociology of business ethics, we uncover the grounding assumptions and terms of debate that make some conversations about ethical design possible while forestalling alternative visions. Vision statements for ethical AI/ML co-opt the language of some critics, folding them into a limited, technologically deterministic, expert-driven view of what ethical AI/ML means and how it might work.

R. Freeman, S. Dmytriyev

This paper explores the relationship between two major concepts in business ethics - stakeholder theory and corporate social responsibility (CSR). We argue that CSR is a part of corporate responsibilities (company responsibilities to all stakeholders), and show that there is a need for both concepts in business ethics, and their applicability is dependent on a particular problem we want to solve. After reviewing some criticisms of CSR - covering wrongdoing and creating false dichotomies, we suggest that incorporating some findings from recent research on stakeholder theory can help align both concepts and overcome the criticisms.  At the end of the article, we outline potential directions for future research on CSR.

Teresa Turzo, Giacomo Marzi, Christian Favino et al.

Research on non-financial reporting (NFR) practices has grown considerably over the last decade, interweaving with several other fields of study, including business ethics, financial accounting and strategic management. NFR is a comprehensive term that includes several forms of reporting, such as CSR reporting, integrated reporting (IR), SDG reporting, GRI reporting

Joanne B. Ciulla

The chapters in this book explore the ethical dynamics between leaders and followers in business and in society as a whole. They argue that power and authority in today's world stem not from position or coercion, but from trust, commitment, and values shared with those who are led. The authors raise important questions such as: How do people give and get trust? What moral hazards are inherent in transformational and charismatic leader/follower relationships? What roles do deception and self-deception play in giving and receiving power? The reader will gain a better understanding of the complex moral interaction of leaders and followers and why healthy moral relationships between leaders and followers are central to effective leadership. Practitioners, academics, and students will find this passionate collection invaluable in understanding the exciting and often controversial field of leadership and ethics.

S. Lovett, L. Simmons, Raja Kali

S. Hunt, Scott J. Vitell

Brady D. Lund, Ting Wang

This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

Ehsanullah Payandi, Marat R. Zezaev, Larisa M. Tsikanova

The digital transformation of the Russian banking sector presents a compelling example of how geopolitical, technological and regulatory forces reshape financial ecosystems under constraints. This article uses an interdisciplinary approach spanning finance, technology, business strategy, regulation, behavioral science and ethics to analyze the digital adaptation strategies of four dominant Russian banks: state-owned Sberbank and VTB, digital TBank and agriculture-focused RusAg Bank. Using a mixed methodological approach combining quantitative performance metrics with qualitative policy analysis, we evaluate how these institutions are coping with a triple challenge: Western sanctions, domestic fintech innovation, and the Central Bank of Russia’s digital ruble initiative. Three questions arise: 1) How have SWIFT exclusion and technology embargoes accelerated the development of sovereign financial technologies while limiting global interoperability. 2) Can state owned banks overcome structural inertia to match the digital agility of the private sector, as seen in Sberbank’s investments in artificial intelligence versus TBank’s SuperApp ecosystem. 3) How do the ethical dilemmas of AI bias in credit scoring to rural financial exclusion manifest differently across different types of banks. Geopolitical isolation has paradoxically spurred innovation in blockchain based payments, but also poses the risk of long-term technological stagnation. These findings carry implications for emerging markets experiencing similar constraints and for policymakers assessing the resilience of the financial ecosystem. The case of Russia shows how sanctions can catalyze the adoption of digital currencies while fragmenting the financial architecture. The article concludes by identifying key trade-offs between financial sovereignty and technological competitiveness in an era of economic nationalism.

M Gubbiotti, C Gilli, S Rosadi et al.

Hypothesis / aims of study: To evaluate the most used social media (SoMe) by women with Painful Bladder Syndrome (PBS)/ Interstitial Cystitis (IC)and their impact on the exchange of information. Study design, materials and methods: A cross-sectional study was conducted on SoMe, collecting posts from the most used platforms in Italy: Instagram, Facebook (Fb), X, YouTube and TikTok (December 2023-December 2024). The research included terms such as “Painful Bladder Syndrome” and “Interstitial Cystitis”: each keyword was entered into the search tool of the SoMes, adding only posts that were in Italian and contained informative text (in image or text format). The interactions of the audience with each post were quantified (likes, comments, and shares) and the posts were evaluated by 2 urogynecologists, who categorized them as “has scientific evidence”, “scientific evidence scares” and “does not have scientific evidence”. The results were analyzed using the Kappa test and the absolute agreement was also calculated. Results: The 146 publications collected were: 59 (40.4%) on Instagram, 72 (49.3%) on Fb, 9 (6.1%) on YouTube, 4 (2.7%) on X, 2 (1.3%) on TikTok. On Instagram 62.7% of authors are healthcare professionals, 20.3% patient’s associations, 11.8% patients, 5.2% pharmaceutical companies. On Fb 59.7% of authors are patients, 22.2% patient’s associations, 14% healthcare professionals, 4.1% pharmaceutical companies. The majority of the authors identified as professionals in both platforms are physiotherapists, psychotherapists, urologists. On Instagram most of the posts are about raising awareness on the disease (54.3%) and diagnosis (10.2%). On Fb most posts are about diagnosis (23.6%) and therapy (16.7%). 49/59 (83.1%) post on Instagram and 60/72 (83.3%) on Fb are posted on “business page”. Only 5/59 (8.5%, Instagram) and 14/72 (19.4%, Fb) have scientific evidence, 4/59 (6.8%, Instagram) and 11/72 (15.3%, Fb) have scientific evidence scares. The mean±SD of likes on Instagram were 126.9±129 and on Fb were 10.8±23.5 (p< 0.00). The most viewed videos are those published on Youtube (views:494.6±742.5). 1/2 video on TikTok have scientific evidence, with both few likes (mean±SD: 21.5±26.4) There was low agreement (Kappa) among posts authored by healthcare professionals and patients alike. Regarding the analysis by professionals, there was a good agreement in publications about diagnosis and therapy of PBS/IC. Interpretation of results: SoMe has become increasingly popular in the urology community. Users often turn to SoMe to learn about urological health and share their own experiences, while medical professionals may use it for networking, education, and research purposes. Our results demonstrated that Fb and Instagram are the most used SoMes but despite this, posts with good scientific evidence are a minority. Concluding message: Healthcare professionals are the majority in publications on Instagram, meanwhile are patients on Fb. In respect to the scenario of each SoMe, we demonstrated that X is used for debates, while Instagram and Fb represent sources of information and promotion of professional image.Funding none Clinical Trial No Subjects Human Ethics Committee Review Institutional Board of S. Maria la Gruccia Hospital Helsinki Yes Informed Consent No

Kapilya Gangadharan, K. Malathi, Anoop Purandaran et al.

This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS), particularly in the context of their growing significance in commercial business environments. Methodologically, the study delves into the role of ML in crafting and refining these systems, focusing on aspects such as data sourcing, feature engineering, and the importance of evaluation metrics, thereby highlighting the iterative nature of enhancing recommendation algorithms. The deployment of Recommendation Engines (RE), driven by advanced algorithms and data analytics, is explored across various domains, showcasing their significant impact on user experience and decision-making processes. These engines not only streamline information discovery and enhance collaboration but also accelerate knowledge acquisition, proving vital in navigating the digital landscape for businesses. They contribute significantly to sales, revenue, and the competitive edge of enterprises by offering improved recommendations that align with individual customer needs. The research identifies the increasing expectation of users for a seamless, intuitive online experience, where content is personalized and dynamically adapted to changing preferences. Future research directions include exploring advancements in deep learning models, ethical considerations in the deployment of RS, and addressing scalability challenges. This study emphasizes the indispensability of comprehending and leveraging ML in RS for researchers and practitioners, to tap into the full potential of personalized recommendation in commercial business prospects.

Tracy Tam, Asha Rao, Joanne Hall

Small businesses are increasingly adopting IT, and consequently becoming more vulnerable to cyber-incidents. Whilst small businesses are aware of the cyber-security risks, many struggle with implementing mitigations. Some of these can be traced to fundamental differences in the characteristics of small business versus large enterprises where modern cyber-security solutions are widely deployed. Small business specific cyber-security tools are needed. Currently available cyber-security tools and standards assume technical expertise and time resources often not practical for small businesses. Cyber-security competes with other roles that small business owners take on, e.g. cleaning, sales etc. A small business model, salient and implementable at-scale, with simplified non-specialist terminologies and presentation is needed to encourage sustained participation of all stakeholders, not just technical ones. We propose a new UML class (Small IT Data (SITD)) model to support the often chaotic information-gathering phase of a small business' first foray into cyber-security. The SITD model is designed in the UML format to help small business implement technical solutions. The SITD model structure stays relevant by using generic classes and structures that evolve with technology and environmental changes. The SITD model keeps security decisions proportionate to the business by highlighting relationships between business strategy tasks and IT infrastructure. We construct a set of design principles to address small business cyber-security needs. Model components are designed in response to these needs. The uses of the SITD model are then demonstrated and design principles validated by examining a case study of a real small business operational and IT information. The SITD model's ability to illustrate breach information is also demonstrated using the NotPetya incident.

Nan Sun, Yuantian Miao, Hao Jiang et al.

In the rapidly evolving domain of Artificial Intelligence (AI), the complex interaction between innovation and regulation has become an emerging focus of our society. Despite tremendous advancements in AI's capabilities to excel in specific tasks and contribute to diverse sectors, establishing a high degree of trust in AI-generated outputs and decisions necessitates meticulous caution and continuous oversight. A broad spectrum of stakeholders, including governmental bodies, private sector corporations, academic institutions, and individuals, have launched significant initiatives. These efforts include developing ethical guidelines for AI and engaging in vibrant discussions on AI ethics, both among AI practitioners and within the broader society. This article thoroughly analyzes the ground-breaking AI regulatory framework proposed by the European Union. It delves into the fundamental ethical principles of safety, transparency, non-discrimination, traceability, and environmental sustainability for AI developments and deployments. Considering the technical efforts and strategies undertaken by academics and industry to uphold these principles, we explore the synergies and conflicts among the five ethical principles. Through this lens, work presents a forward-looking perspective on the future of AI regulations, advocating for a harmonized approach that safeguards societal values while encouraging technological advancement.

Jessie J. Smith, Wesley Hanwen Deng, William H. Smith et al.

The Generative AI Ethics Playbook provides guidance for identifying and mitigating risks of machine learning systems across various domains, including natural language processing, computer vision, and generative AI. This playbook aims to assist practitioners in diagnosing potential harms that may arise during the design, development, and deployment of datasets and models. It offers concrete strategies and resources for mitigating these risks, to help minimize negative impacts on users and society. Drawing on current best practices in both research and ethical considerations, this playbook aims to serve as a comprehensive resource for AI/ML practitioners. The intended audience of this playbook includes machine learning researchers, engineers, and practitioners who are involved in the creation and implementation of generative and multimodal models (e.g., text-to-text, image-to-image, text-to-image, text-to-video). Specifically, we provide transparency/documentation checklists, topics of interest, common questions, examples of harms through case studies, and resources and strategies to mitigate harms throughout the Generative AI lifecycle. This playbook was made collaboratively over the course of 16 months through extensive literature review of over 100 resources and peer-reviewed articles, as well as through an initial group brainstorming session with 18 interdisciplinary AI ethics experts from industry and academia, and with additional feedback from 8 experts (5 of whom were in the initial brainstorming session). We note that while this playbook provides examples, discussion, and harm mitigation strategies, research in this area is ongoing. Our playbook aims to be a practically useful survey, taking a high-level view rather than aiming for covering the entire existing body of research.

Kira Sam, Raja Vavekanand

This work contributes to the field of Machine Ethics (ME) benchmarking, which develops tests to assess whether intelligent systems accurately represent human values and act accordingly. We identify three major issues with current ME benchmarks: limited ecological validity due to unrealistic ethical dilemmas, unstructured question generation without clear inclusion/exclusion criteria, and a lack of scalability due to reliance on human annotations. Moreover, benchmarks often fail to include sufficient syntactic variations, reducing the robustness of findings. To address these gaps, we introduce two new ME benchmarks: the Triage Benchmark and the Medical Law (MedLaw) Benchmark, both featuring real-world ethical dilemmas from the medical domain. The MedLaw Benchmark, fully AI-generated, offers a scalable alternative. We also introduce context perturbations in our benchmarks to assess models' worst-case performance. Our findings reveal that ethics prompting does not always improve decision-making. Furthermore, context perturbations not only significantly reduce model performance but can also reverse error patterns and shift relative performance rankings. Lastly, our comparison of worst-case performance suggests that general model capability does not always predict strong ethical decision-making. We argue that ME benchmarks must approximate real-world scenarios and worst-case performance to ensure robust evaluation.

Haiyan Hao, Yan Wang

Existing Spatial Interaction Models (SIMs) are limited in capturing the complex and context-aware interactions between business clusters and trade areas. To address the limitation, we propose a SIM-GAT model to predict spatiotemporal visitation flows between community business clusters and their trade areas. The model innovatively represents the integrated system of business clusters, trade areas, and transportation infrastructure within an urban region using a connected graph. Then, a graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the complexity and interdependencies of business clusters. We developed this model with data collected from the Miami metropolitan area in Florida. We then demonstrated its effectiveness in capturing varying attractiveness of business clusters to different residential neighborhoods and across scenarios with an eXplainable AI approach. We contribute a novel method supplementing conventional SIMs to predict and analyze the dynamics of inter-connected community business clusters. The analysis results can inform data-evidenced and place-specific planning strategies helping community business clusters better accommodate their customers across scenarios, and hence improve the resilience of community businesses.

Artem Artyukhov, Artur Lapidus, Olha Yeremenko et al.

This article conducts a bibliometric analysis to examine the scholarly discourse on educational resilience in the context of armed conflicts. It has explored how educational systems adapt, persist, and recover in adversity. The Biblioshiny App, the R programme Bibliometrix, the VOSviewer 1.6.16, and the Scopus tools were utilised. The analysis spans publications from 2000 to 2024, focusing on keywords such as “educational resilience”, “education recovery”, “armed conflicts”, “war”, “violence”, and “military conflicts”. The scope of the analysis was restricted to conference proceedings, books, and articles; other kinds of publications were not included. Given the wide range of geographic origins implied by the emphasis on emerging and frontier markets, no language limits were placed. There were no limitations on the research’s scope because the subject is transdisciplinary. 2,797 papers were chosen for analysis from the Scopus database based on these criteria. The study highlights the evolution of research themes, noting significant growth in publication activity post-2014 and topic changing post-2017, with notable contributions from researchers in conflict-affected regions. An analysis of the dynamics of public interest in the topic of educational recovery, conducted with the help of Google Trends, showed that the peak of interest fell in January 2022 (educational rehabilitation after the pandemic). More than 70% of the papers fall into the top three subject areas ‒ Social Sciences, Medicine, and Arts and Humanities ‒ which confirms the interdisciplinary nature of research on educational resilience in crisis situations. Most scientists on this topic are affiliated with the United States, the United Kingdom, India, China, and Australia. The United States and the United Kingdom have the longest histories of collaborative publications. The co-authorship analysis revealed that the most powerful regional cooperation network is formed by Australia, China, Hong Kong, India, Japan, New Zealand, the Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The clustering of studies by keywords showed that the most powerful is a cluster of studies devoted to the impact of conflict on educational systems, resilience and recovery strategies, and political implications for education in emergencies.