Modern business environments demand continuous reconfiguration of cross-functional processes, yet most enterprise systems remain organized around siloed departments, rigid workflows, and hard-coded automation. Meanwhile, large language models (LLMs) demonstrate strong capabilities in interpreting natural language and synthesizing unstructured information, but they lack deterministic, auditable execution of complex business logic. We introduce Autonomous Business System (AUTOBUS), a system that integrates LLM-based AI agents, predicate-logic programming, and business-semantics-centric enterprise data into a unified neuro-symbolic architecture for executing end-to-end business initiatives. AUTOBUS models a business initiative as a network of interrelated tasks with explicit pre- and post-conditions, required data, evaluation rules, and API-level actions. Enterprise data is organized as a knowledge graph, whose entities, relationships, and constraints are translated into logic facts and foundational rules that ground reasoning and ensure semantic consistency. Core AI agents synthesize task instructions, enterprise semantics, and available tools into task-specific logic programs, which are executed by a logic engine that enforces constraints, coordinates auxiliary tools, and produces deterministic outcomes. Humans specify task instructions, define and maintain business semantics and policies, curate tools, and supervise high-impact or ambiguous decisions, ensuring accountability and adaptability. We detail the AUTOBUS architecture, the structure of AI-generated logic programs, and the human-AI collaboration model and present a case study that demonstrates accelerated time to market in a data-rich organization. A reference implementation of the case study is available at https://github.com/cecilpang/autobus-paper.
Melissa Wilfley, Mengting Ai, Madelyn Rose Sanfilippo
Introduction. AI Ethics is framed distinctly across actors and stakeholder groups. We report results from a case study of OpenAI analysing ethical AI discourse. Method. Research addressed: How has OpenAI's public discourse leveraged 'ethics', 'safety', 'alignment' and adjacent related concepts over time, and what does discourse signal about framing in practice? A structured corpus, differentiating between communication for a general audience and communication with an academic audience, was assembled from public documentation. Analysis. Qualitative content analysis of ethical themes combined inductively derived and deductively applied codes. Quantitative analysis leveraged computational content analysis methods via NLP to model topics and quantify changes in rhetoric over time. Visualizations report aggregate results. For reproducible results, we have released our code at https://github.com/famous-blue-raincoat/AI_Ethics_Discourse. Results. Results indicate that safety and risk discourse dominate OpenAI's public communication and documentation, without applying academic and advocacy ethics frameworks or vocabularies. Conclusions. Implications for governance are presented, along with discussion of ethics-washing practices in industry.
Luz Maribel Vásquez-Vásquez, Elena Jesús Alvarado-Cáceres, Víctor Hugo Fernández-Bedoya
In recent years, Latin America has experienced a growing incorporation of Artificial Intelligence (AI) into business and organizational environments, driven by digital transformation, data availability, and competitive pressures. Across multiple sectors, AI-based tools are increasingly used to support complex decision-making processes, raising both opportunities and challenges related to efficiency, ethics, and organizational readiness. Within this context, this systematic review examines the scientific evidence on the implementation of AI in business decision-making in Latin America. Following PRISMA 2020 guidelines, a systematic search was conducted in the Scopus database for articles published between 2021 and 2025. The search strategy combined Boolean operators related to AI and decision-making. Inclusion criteria comprised original, open-access research articles conducted in Latin American countries and published in Spanish or Portuguese. After screening for temporality, geographic focus, language, document type, accessibility, duplication, and relevance, 27 studies were selected from an initial pool of 276,302 records. The studies originated mainly from Peru, Colombia, Chile, and Ecuador. The findings show that AI is applied across sectors such as industry, agriculture, finance, education, and public services, primarily to enhance predictive capacity, automate processes, and support data-driven decisions. While AI adoption improves efficiency, cost reduction, and strategic innovation, its effectiveness depends on staff training, ethical governance, and strategic alignment. Persistent challenges include resistance to change, data quality limitations, and concerns regarding transparency and algorithmic bias. Overall, AI emerges as a transformative but context-dependent tool for business decision-making in Latin America.
Political institutions and public administration (General)
This paper explores how ethical consumption can transform democratic governance toward sustainability by challenging traditional economic models centered on utility and efficiency. As societal values shift toward transparency equity and environmental responsibility ethical consumers increasingly influence markets. Drawing on Whites Kantian economic framework and Ingleharts theory of value change the paper proposes a model integrating moral imperatives into economic theory. Using a vector bundle approach it captures evolving ethical preferences advocating for an inclusive sustainability focused economic paradigm aligned with post materialist values.
The rapid advancement of artificial intelligence (AI) is transforming economic, managerial, and legal systems, creating new opportunities and risks for sustainable development and social equity. Within global digitalisation and Ukraine’s integration into the European legal and technological framework, the study of AI development management becomes a key element of national digital transformation. The topic holds significant scholarly and practical importance as it bridges law, economics, management, and sustainability. The aim of this study is to identify and systematise the legal and institutional mechanisms governing AI development in the European Union and Ukraine, and to assess their alignment with the global sustainable development agenda, based on three hypotheses: (1) the interdisciplinary nature of AI and law; (2) the integration of the UN Sustainable Development Goals into the EU AI Act (Regulation (EU) 2024/1689); and (3) the normative and institutional convergence between Ukraine and the European Union. The methodology applies a triangulated approach combining bibliometric analysis (PRISMA protocol), formal content analysis, and SWOT analysis, ensuring a comprehensive evaluation of legal and managerial aspects of AI governance. The findings show that the EU model balances innovation and accountability through ESG principles, digital ethics, and human-centred governance. Ukraine demonstrates growing alignment with EU law via strategic documents, enhanced digital governance, and participation in international harmonisation programmes. The theoretical contribution lies in conceptualising AI development management as part of a sustainable digital regulation model, reflecting the evolving roles of the state, academia, and business in responsible technology governance. The practical implications include recommendations for developing a national strategy for AI regulation and management, drafting AI legislation harmonised with the EU AI Act, and improving digital governance in the public sector.
Aloys Iyamuremye, Jeannette Musengimana, Ezechiel Nsabayezu
et al.
Abstract Unemployment among science graduates in Rwanda remains a pressing challenge due to limited industrial opportunities and insufficient market demand for scientific skills. This study assesses chemistry teachers’ entrepreneurial competencies for developing small chemical-based businesses. This study adopted an explanatory sequential design, combining quantitative and qualitative methods. A structured questionnaire was administered to 75 chemistry teachers from secondary schools across Rwanda. Quantitative data were analyzed using descriptive and inferential statistics. Descriptive statistics were used to summarize competency levels, while Multivariate Analysis of Variance was employed to analyze the influence of gender, teaching experience, school ownership, and location on five key entrepreneurial domains: entrepreneurship skills, innovation, teaching and mentoring, communication and collaboration, and ethics and professionalism. The quantitative results revealed that teachers exhibit moderate strengths in communication and collaborative skills (44%). However, financial planning and market research emerged as the weakest areas, with over 60% of teachers uncertain about budgeting and identifying market opportunities. In addition, 47% of teachers lack confidence in adhering to business regulations. Furthermore, most teachers (61%) lack problem solving and innovation skills related to creating chemical-based business. The inferential analysis showed significant differences in entrepreneurial competencies based on gender (p < 0.05) in favor of male teachers and teaching experience in favor experienced teachers above 6 years of working experience (p < 0.05). On the other side, the school location and ownership did not show significant effects. (p>0.05) These findings suggest that experience and gender-related factors influence how chemistry teachers develop and apply entrepreneurial knowledge in educational settings. Therefore, the study highlights the need for targeted professional development and curriculum integration to promote chemical entrepreneurship among chemistry teachers. In this context, project-based learning, mentorship, and stronger policy support are recommended to foster entrepreneurial mindsets among teachers and students.
Digital transformation (DX) has recently become a pressing issue for many companies as the latest digital technologies, such as artificial intelligence and the Internet of Things, can be easily utilized. However, devising new business models is not easy for compa-nies, though they can improve their operations through digital technologies. Thus, business model design support methods are needed by people who lack digital tech-nology expertise. In contrast, large language models (LLMs) represented by ChatGPT and natural language processing utilizing LLMs have been developed revolutionarily. A business model design support system that utilizes these technologies has great potential. However, research on this area is scant. Accordingly, this study proposes an LLM-based method for comparing and analyzing similar companies from different business do-mains as a first step toward business model design support utilizing LLMs. This method can support idea generation in digital business model design.
The discipline of business ethics has been slow to include Big Tech as a worthwhile object of examination. My goal in this presidential address is to make the case that the discipline of business ethics is overlooking novel harms and marginalized stakeholders in emerging and impactful technology industries. Furthermore, although the discipline is improving, the persistent narrowness of our field inhibits our ability to identify and examine novel issues in these important industries. I use standpoint theory to suggest one reason why we remain narrow in what we think counts in business ethics as valid objects of concern: because we are similarly narrow in who counts as a business ethicist. As scholars, we are a lens that we train on the world to identify who counts as a scholar, what we study, and who matters.
The rapid advancement of artificial intelligence (AI) such as the emergence of large language models including ChatGPT and DALLE 2 has brought both opportunities for improving productivity and raised ethical concerns. This paper investigates the ethics of using artificial intelligence (AI) in cartography, with a particular focus on the generation of maps using DALLE 2. To accomplish this, we first create an open-sourced dataset that includes synthetic (AI-generated) and real-world (human-designed) maps at multiple scales with a variety settings. We subsequently examine four potential ethical concerns that may arise from the characteristics of DALLE 2 generated maps, namely inaccuracies, misleading information, unanticipated features, and reproducibility. We then develop a deep learning-based ethical examination system that identifies those AI-generated maps. Our research emphasizes the importance of ethical considerations in the development and use of AI techniques in cartography, contributing to the growing body of work on trustworthy maps. We aim to raise public awareness of the potential risks associated with AI-generated maps and support the development of ethical guidelines for their future use.
Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr's three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the Moral Tractability Thesis (MTT).
Katharina Klatte, Constantin Sluka, Viktoria Gloy
et al.
Abstract Objective The registration of clinical trials is required by law in Switzerland. We investigated (1) the proportion of registered and prospectively registered clinical trials, (2) the availability of results for ethically approved trial protocols, (3) factors associated with increased registration, and (4) reasons for non-registration. Design and setting We included all clinical trials with mandatory prospective registration, which were approved by the ethics committee of Northwestern and Central Switzerland between January 1, 2016, and December 31, 2020. Methods We extracted relevant trial characteristics from the Swiss Business Administration System for Ethics Committees and systematically searched the International Clinical Trials Registry Platform and primary trial registries for corresponding registry entries. We used multivariable logistic regression to examine the association between trial characteristics and registration. We qualitatively assessed reasons for non-registration of trials through an email questionnaire for trial investigators. Results Of 473 included clinical trials, 432 (91%) were registered at all and 326 (69%) were prospectively registered. While the percentages of registration and prospective registration of investigator-sponsored trials increased from 85 to 93% and from 59 to 70% over 5 years, respectively, industry-sponsored trials consistently remained at a high level of prospective registration (92 to 100%). Trials with multiple centres, higher risk category, or methodological support from the local clinical trials unit were independently associated with increased registration rates. Of 103 clinical trials completed before August 2020, results were available for 70% of industry-sponsored trials and 45% of investigator-sponsored trials as peer-reviewed journal publications or in trial registries. Most common reasons for non-registration provided by investigators were lack of time or resources (53%), lack of knowledge (22%), and lack of reminders by the ethics committee (36%). Conclusions In Northwestern and Central Switzerland about 10% of clinical trials remained unregistered despite the obligation by law. More support for investigators and stricter enforcement by regulators are needed to improve the transparency of investigator-sponsored trials in particular.
One of the essential aspects in analyzing the topic of managing people's risk behavior in extreme situations is related to the issue of risk perception. The subject of this study was to reveal whether certain factors, such as cultural context, gender, age, education, religious beliefs, etc., mediate the perceived risk during the COVID-19 pandemic. The study was conducted online from April to June 2020. The two samples included a total of 2617 participants aged between 18 and 70 years, where 1412 of which were drawn from Asia and 1205 were representatives of Europe. Participants were asked to complete an internet-based version of a short questionnaire that described the COVID-19 pandemic situation, as one of the questions referred to the hazard perception of the infection. Results obtained showed that there was a higher level of perceived risk among members of the Asian culture group in comparison with the European study participants. Furthermore, it was revealed that gender is not a significant factor in the perception of risk in either the European or the Asian cultural group (p> 0.05). The results showed that levels of the perceived risk increased with age and that there were significant differences between young and old in threat perception for the European sample (p<0.001). Regarding the Asian sample, it turned out that all age groups perceive the risk of infection almost equally. Several demographic characteristics also emerged as important mediators of risk perception. For example, people with the lowest education, married, religious, and families with children fear the hazard of COVID-19 infection to the greatest extent, compared to people with the highest education, singles, atheists, and families without children, who do not perceive the virus as a high-risk factor.
Engineering (General). Civil engineering (General), Risk in industry. Risk management
With the advancement in the marketing channel, the use of e-commerce has increased tremendously therefore the basic objective of this study is to analyze the impact of business analytics and decision support systems on e-commerce in small and medium enterprises. Small and medium enterprises are becoming a priority for economies as by implementing some policies and regulations these businesses could encourage gain development on an international level. The objective of this study is to analyze the impact of business analytics and decision support systems on e-commerce in small and medium enterprises that investigate the relationship between business analytics and decision support systems in e-commerce businesses. To evaluate the impact of both on e-commerce the, descriptive analysis approach is adopted that reviews the research of different scholars who adopted different plans and strategies to predict the relationship between e-commerce and business analytics. The study contributes to the literature by examining the impact of business analytics in SMEs and provides a comprehensive understanding of its relationship with the decision support system. After analyzing the impact of business analytics and decision support system in SMEs, the research also highlights some limitations and provide future recommendations that are helpful to overcome these limitations.
We present the motivation, design, outline, and lessons learned from an online course in scientific integrity, research ethics, and information ethics provided to over 2000 doctoral and engineering students in STEM fields, first at the University Paris-Saclay, and now expanded to an online MOOC available to students across the world, in English. Unlike a course in scientific domains, meant to provide students with methods, tools, and concepts they can apply in their future career, the goal of such a training is not so much to equip them, but to make them aware of the impact of their work on society, care about the responsibilities that befall on them, and make them realize not all share the same opinions on how should technology imprint society. While we provide conceptual tools, this is more to sustain interest and engage students. We want them to debate on concrete ethical issues and realize the difficulty of reconciling positions on contemporary dilemma such as dematerialized intellectual property, freedom of expression online and its counterparts, the protection of our digital selves, the management of algorithmic decision, the control of autonomous systems, and the resolution of the digital divide. As a bold shortcut, our course is about introducing and motivating Hegelian dialectics in STEM curricula, usually more bent on an Aristotelian perspective.
Dr Kaizer Raseane Makole, Dr Bhekabantu Alson Ntshangase, Samson Adeoluwa Adewumi
Coalition governance in South Africa is adjudged as an opportunity for nation-building and social compact, which intends to jettison the apartheid legacy of racial and social separation. Coalition governance in South Africa quintessentially demonstrates a maturing nation-building project whereby political parties embrace a cohesive spirit for democratic governance and citizenship representation. However, the lack of consensus about a developmental posture to drive nation-building in South Africa put different political parties on antithetical dissimulations that undermine effective coalition governance in both national and local governmental spheres. An era of dominant party hegemony is approaching its final demise, which challenges political parties to build workable and collaborative consensus toward leading South Africa out of its current developmental stagnation. The study examines the challenges and opportunities of coalition governance in South Africa. The objective of the paper is to understand how coalition building can be a leverage for Nation building in the context of South African political structure. A qualitative study is undertaken by analyzing strategic and policy documents of political parties and journal articles to examine challenges and opportunities for coalition governance in South Africa. The thematic content analysis examines emerging patterns from primary and secondary documentary sources to generate relevant themes that influence and guide conceptualization, challenges, and opportunities for coalition governance in South Africa. Findings revealed that a major challenge for coalition governance in South Africa is political brinkmanship by political parties who negotiate in bad faith, demanding more concessions than votes garnered from election results, which demonstrates political immaturity. However, a positive aspect of coalition governance might be for parties to formalize binding coalition agreements that can serve as conflict management mechanisms that can compel parties to demonstrate reciprocal accountability during governance.
Purpose – The emergence of artificial intelligence (AI) is leading to a job transformation within the service ecosystem in which issues related to AI governance principles may hinder the social sustainability of the sector. The relevance of AI startups in driving innovation has been recognized; thus, this paper aims to investigate whether and how AI startups may influence the sustainable social development (SSD) of the service sector. Design/methodology/approach – An empirical study based on 24 in-depth interviews was conducted to qualitatively explore the perceptions of service sector facing AI policymakers, AI consultants and academics (n = 12), as well as AI startups (founders, AI developers; n = 12). An inductive coding approach was used to identify and analyze the data. Findings – As part of a complex system, AI startups influence the SSD of the service sector in relation to other stakeholders’ contributions for the ethical deployment of AI. Four key factors influencing AI startups’ ability to contribute to the SSD of the service sector were identified: awareness of socioeconomic issues; fostering decent work; systematically applying ethics; and business model innovation. Practical implications – This study proposes measures for service sector AI startups to promote collaborative efforts and implement managerial practices that adapt to their available resources. Originality/value – This study develops original guidelines for startups that seek ethical development of beneficial AI in the service sector, building upon Ethics as a Service approach.
AbstractThis paper provides, from a business ethics perspective, a basic clustering of the morally (a) favorable, (b) unfavorable, and (c) ambivalent dimensions of blockchain technology and its various emergent applications. Instead of proffering specific assessments on particular aspects of blockchain‐based business models, we aim to offer an initial overview that charters the territory so that future research can bring about such moral assessments in an informed and orderly fashion. The main contribution of this paper lies in identifying several morally ambivalent dimensions of blockchain technology, which we finally link to two strands of business ethics research: ethical and legal aspects of legislation as well as a link to Habermasian corporate social responsibility theory arguing for transparent data production and consumption on the blockchain. We conclude that future research is necessary for moral assessment of the ambivalent cases, since their ethical evaluation changes depending on whether one analyzes them through the lenses of utilitarianism, contractarianism, deontology, and virtue ethics, respectively.