Hasil untuk "Business ethics"

Nelly Elsayed

The rapid evolution of artificial intelligence (AI) systems, tools, and technologies has opened up novel, unprecedented opportunities for businesses to innovate, differentiate, and compete. However, growing concerns have emerged about the use of AI in businesses, particularly AI washing, in which firms exaggerate, misrepresent, or superficially signal their AI capabilities to gain financial and reputational advantages. This paper aims to establish a conceptual foundation for understanding AI washing. In this paper, we draw on analogies from greenwashing and insights from Information Systems (IS) research on ethics, trust, signaling, and digital innovation. This paper proposes a typology of AI washing practices across four primary domains: marketing and branding, technical capability inflation, strategic signaling, and governance-based washing. In addition, we examine their organizational, industry, and societal impacts. Our investigation and analysis reveal how AI washing can lead to short-term gains; however, it also proposes severe long-term consequences, including reputational damage, erosion of trust, and misallocation of resources. Moreover, this paper examines current research directions and open questions aimed at mitigating AI washing practices and enhancing the trust and reliability of legitimate AI systems and technologies.

Mukucha Paul, Dube Thulani, Jaravaza Divaries Cosmas

Ethics has become a trending phenomenon in most disciplines as a result of the sustainability wave enshrined in the United Nations Sustainability Goals (SDGs). In business circles procurement ethics have become topical as a result of the rising malpractices whose ripple effects have far reaching economic, social and environmental consequences. In the agricultural supply chains procurement ethics have been called to question due to outcries from the tobacco contract farmers whose livelihoods are affected by supplier development washing in the form of exorbitantly priced contract farming inputs. The unethical practice of supplier development washing in the form of exorbitantly priced tobacco contract farming inputs has some ripple effects which this study sought to determine. Twenty-five contracted tobacco farmers were interviewed and the results indicated that farmers subjected to tobacco merchants’ unethical conduct tend to practice contractor switching, side marketing, become insolvent, and indulge in unorthodox cost cutting measures. The study recommended that the regulatory authorities should intervene to bring sanity to the tobacco supply chains through enforcing contract farming mechanisms that reflect genuine supplier development.

Kumpol Saengtabtim, Natt Leelawat, Rujapa Aumnoysombat et al.

In the consistently evolving artificial intelligence (AI) and large language models (LLMs), many organizations adopt these technologies’ capabilities to solve and assist core operations in many industries. In disaster areas, well-known organizations in disaster management try to shift their focus to apply the potential capabilities of AI and LLM to support disaster management. As AI and LLM continue to develop, this research aims to perform a structured summarization process to identify their current trend that can assist the disaster management process using a systematic review approach. The study follows the guidelines of PRISMA to ensure transparency in the review results. The findings highlighted the outstanding benefits of AI and LLM and the introduction of integrated technologies to facilitate disaster management, which can eventually mitigate disaster impacts and casualties. The refined results also proposed the technologies’ benefits in assisting the decision support process, creating a business continuity plan, and detecting early warnings. However, ethics and transparency remain the main concerns in fully implementing AI and LLM in disaster management operations. Moreover, the SWOT analysis, represented by the TOWS matrix, was also performed to identify core strategies based on internal and external factors for assisting the disaster management operations.

Dylan Waldner

This paper grounds ethics in evolutionary biology, viewing moral norms as adaptive mechanisms that render cooperation fitness-viable under selection pressure. Current alignment approaches add ethics post hoc, treating it as an external constraint rather than embedding it as an evolutionary strategy for cooperation. The central question is whether normative architectures can be embedded directly into AI systems to sustain human--AI cooperation (symbiosis) as capabilities scale. To address this, I propose a governance--embedding--representation pipeline linking moral representation learning to system-level design and institutional governance, treating alignment as a multi-level problem spanning cognition, optimization, and oversight. I formalize moral norm representation through the moral problem space, a learnable subspace in neural representations where cooperative norms can be encoded and causally manipulated. Using sparse autoencoders, activation steering, and causal interventions, I outline a research program for engineering moral representations and embedding them into the full semantic space -- treating competing theories of morality as empirical hypotheses about representation geometry rather than philosophical positions. Governance principles leverage these learned moral representations to regulate how cooperative behaviors evolve within the AI ecosystem. Through replicator dynamics and multi-agent game theory, I model how internal representational features can shape population-level incentives by motivating the design of sanctions and subsidies structured to yield decentralized normative institutions.

Malvina Nissim, Viviana Patti, Beatrice Savoldi

As Natural Language Processing (NLP) systems become more pervasive, integrating ethical considerations into NLP education has become essential. However, this presents inherent challenges in curriculum development: the field's rapid evolution from both academia and industry, and the need to foster critical thinking beyond traditional technical training. We introduce our course on Ethical Aspects in NLP and our pedagogical approach, grounded in active learning through interactive sessions, hands-on activities, and "learning by teaching" methods. Over four years, the course has been refined and adapted across different institutions, educational levels, and interdisciplinary backgrounds; it has also yielded many reusable products, both in the form of teaching materials and in the form of actual educational products aimed at diverse audiences, made by the students themselves. By sharing our approach and experience, we hope to provide inspiration for educators seeking to incorporate social impact considerations into their curricula.

Meng Wang, Philipp Görz, Joschua Schilling et al.

Detecting business logic vulnerabilities is a critical challenge in software security. These flaws come from mistakes in an application's design or implementation and allow attackers to trigger unintended application behavior. Traditional fuzzing sanitizers for dynamic analysis excel at finding vulnerabilities related to memory safety violations but largely fail to detect business logic vulnerabilities, as these flaws require understanding application-specific semantic context. Recent attempts to infer this context, due to their reliance on heuristics and non-portable language features, are inherently brittle and incomplete. As business logic vulnerabilities constitute a majority (27/40) of the most dangerous software weaknesses in practice, this is a worrying blind spot of existing tools. In this paper, we tackle this challenge with ANOTA, a novel human-in-the-loop sanitizer framework. ANOTA introduces a lightweight, user-friendly annotation system that enables users to directly encode their domain-specific knowledge as lightweight annotations that define an application's intended behavior. A runtime execution monitor then observes program behavior, comparing it against the policies defined by the annotations, thereby identifying deviations that indicate vulnerabilities. To evaluate the effectiveness of ANOTA, we combine ANOTA with a state-of-the-art fuzzer and compare it against other popular bug finding methods compatible with the same targets. The results show that ANOTA+FUZZER outperforms them in terms of effectiveness. More specifically, ANOTA+FUZZER can successfully reproduce 43 known vulnerabilities, and discovered 22 previously unknown vulnerabilities (17 CVEs assigned) during the evaluation. These results demonstrate that ANOTA provides a practical and effective approach for uncovering complex business logic flaws often missed by traditional security testing techniques.

Sai Kartheek Reddy Kasu

The deployment of large language models (LLMs) in mental health and other sensitive domains raises urgent questions about ethical reasoning, fairness, and responsible alignment. Yet, existing benchmarks for moral and clinical decision-making do not adequately capture the unique ethical dilemmas encountered in mental health practice, where confidentiality, autonomy, beneficence, and bias frequently intersect. To address this gap, we introduce Ethical Reasoning in Mental Health (EthicsMH), a pilot dataset of 125 scenarios designed to evaluate how AI systems navigate ethically charged situations in therapeutic and psychiatric contexts. Each scenario is enriched with structured fields, including multiple decision options, expert-aligned reasoning, expected model behavior, real-world impact, and multi-stakeholder viewpoints. This structure enables evaluation not only of decision accuracy but also of explanation quality and alignment with professional norms. Although modest in scale and developed with model-assisted generation, EthicsMH establishes a task framework that bridges AI ethics and mental health decision-making. By releasing this dataset, we aim to provide a seed resource that can be expanded through community and expert contributions, fostering the development of AI systems capable of responsibly handling some of society's most delicate decisions.

Zihao Wang, Junming Zhang

Large Language Models (LLMs) have shown promising potential in business applications, particularly in enterprise decision support and strategic planning, yet current approaches often struggle to reconcile intricate operational analyses with overarching strategic goals across diverse market environments, leading to fragmented workflows and reduced collaboration across organizational levels. This paper introduces BusiAgent, a novel multi-agent framework leveraging LLMs for advanced decision-making in complex corporate environments. BusiAgent integrates three core innovations: an extended Continuous Time Markov Decision Process (CTMDP) for dynamic agent modeling, a generalized entropy measure to optimize collaborative efficiency, and a multi-level Stackelberg game to handle hierarchical decision processes. Additionally, contextual Thompson sampling is employed for prompt optimization, supported by a comprehensive quality assurance system to mitigate errors. Extensive empirical evaluations across diverse business scenarios validate BusiAgent's efficacy, demonstrating its capacity to generate coherent, client-focused solutions that smoothly integrate granular insights with high-level strategy, significantly outperforming established approaches in both solution quality and user satisfaction. By fusing cutting-edge AI technologies with deep business insights, BusiAgent marks a substantial step forward in AI-driven enterprise decision-making, empowering organizations to navigate complex business landscapes more effectively.

Zhou Ziheng, Haiqiang Dai, Bin Ling et al.

As AI systems become increasingly sophisticated, questions about machine consciousness and its ethical implications have moved from fringe speculation to mainstream academic debate. Current ethical frameworks in this domain often implicitly rely on contested functionalist assumptions, prioritize speculative AI welfare over concrete human interests, and lack coherent theoretical foundations. We address these limitations through a structured three-level framework grounded in philosophical uncertainty. At the foundational level, we establish five factual determinations about AI consciousness alongside human-centralism as our meta-ethical stance. These foundations logically entail three operational principles: presumption of no consciousness (placing the burden of proof on consciousness claims), risk prudence (prioritizing human welfare under uncertainty), and transparent reasoning (enabling systematic evaluation and adaptation). At the application level, the third component of our framework, we derive default positions on pressing ethical questions through a transparent logical process where each position can be explicitly traced back to our foundational commitments. Our approach balances philosophical rigor with practical guidance, distinguishes consciousness from anthropomorphism, and creates pathways for responsible evolution as scientific understanding advances, providing a human-centric foundation for navigating these profound ethical challenges.

Rajnović Ljiljana, Eremić-Đođić Jelica, Dimitrijević Ljiljana

In this paper, the authors present the specifics of the management of economic entities in the field of agricultural production in the Republic of Serbia. In addition to the goals that all business entities are faced with, which are economic goals that are reflected in the realization of profits for the sake of business security for a long period of time and safe survival in a constantly turbulent environment, and legal goals that oblige first-class entities to operate in accordance with legal regulations, limiting conditions of freedom of management, in the form of respect for high ethical principles, are set before economic entities engaged in agricultural production due to the use of agricultural land that represents a good of general interest and a natural resource. This activity has a significant impact on human health and the environment. The authors used a survey of the owners of 40 agricultural farms and small businesses in the territory of the municipality of Ruma and other stakeholders, first of all buyers of goods of the surveyed economic entities, then extensive literate review and the method of comparative analysis. The results of the research show that for those economic subjects whose business requires the trust of citizens and protection of the environment, respect for high ethical principles in business, could be the tip of the scales that will determine their survival on the market. It has been shown that compliance with the rules of business ethics, in the long term, brings the business entity more profit, so compliance with those rules is becoming more and more universal. That is why agricultural producers follow the ethical principles of responsible business. The role of the Government of the Republic of Serbia could be crucial here with certain types of assistance, which would further improve the sustainable development of agricultural entities.

David Mhlanga, Mufaro Dzingirai

Abstract In an increasingly volatile, uncertain, complex, and ambiguous (VUCA) world, the importance of organizational resilience has grown, yet the scholarly literature on this topic remains fragmented. To address this gap, our study conducted a bibliometric analysis of 469 articles from the Scopus database using VOSViewer software to systematically review and map trends, gaps, and significant contributions in the field. Our analysis revealed key themes such as resilience, crisis management, innovation, COVID-19, dynamic capabilities, sustainability, and change management, which are crucial to understanding organizational resilience. The findings highlight that the United Kingdom and the University of Oulu are significant contributors to this research area, with notable authors including Duchek E., Martinelli E., Santoro G., Williams T.A., and Youssef C.M. playing a pivotal role in advancing this field. By providing a comprehensive overview of institutional affiliations, countries, authors, journals, publications, and keyword co-occurrences, our study not only maps the landscape of organizational resilience research but also identifies critical areas for future inquiry. This contribution enhances both theoretical and practical understandings of organizational resilience, aiding practitioners in developing robust strategies to navigate the challenges of the VUCA world.

Henrik Kirchmann, Stephan A. Fahrenkrog-Petersen, Felix Mannhardt et al.

Process models may be automatically generated from event logs that contain as-is data of a business process. While such models generalize over the control-flow of specific, recorded process executions, they are often also annotated with behavioural statistics, such as execution frequencies.Based thereon, once a model is published, certain insights about the original process executions may be reconstructed, so that an external party may extract confidential information about the business process. This work is the first to empirically investigate such reconstruction attempts based on process models. To this end, we propose different play-out strategies that reconstruct the control-flow from process trees, potentially exploiting frequency annotations. To assess the potential success of such reconstruction attacks on process models, and hence the risks imposed by publishing them, we compare the reconstructed process executions with those of the original log for several real-world datasets.

Emanuele Luzio, Moacir Antonelli Ponti, Christian Ramirez Arevalo et al.

Machine learning models typically focus on specific targets like creating classifiers, often based on known population feature distributions in a business context. However, models calculating individual features adapt over time to improve precision, introducing the concept of decoupling: shifting from point evaluation to data distribution. We use calibration strategies as strategy for decoupling machine learning (ML) classifiers from score-based actions within business logic frameworks. To evaluate these strategies, we perform a comparative analysis using a real-world business scenario and multiple ML models. Our findings highlight the trade-offs and performance implications of the approach, offering valuable insights for practitioners seeking to optimize their decoupling efforts. In particular, the Isotonic and Beta calibration methods stand out for scenarios in which there is shift between training and testing data.

Bora Caglayan, Mingxue Wang, John D. Kelleher et al.

NL2SQL (Natural Language to Structured Query Language) transformation has seen wide adoption in Business Intelligence (BI) applications in recent years. However, existing NL2SQL benchmarks are not suitable for production BI scenarios, as they are not designed for common business intelligence questions. To address this gap, we have developed a new benchmark focused on typical NL questions in industrial BI scenarios. We discuss the challenges of constructing a BI-focused benchmark and the shortcomings of existing benchmarks. Additionally, we introduce question categories in our benchmark that reflect common BI inquiries. Lastly, we propose two novel semantic similarity evaluation metrics for assessing NL2SQL capabilities in BI applications and services.

Astrid Rudyanto, Julisar Julisar, Debora Debora

Purpose – This research aims to examine the association between political connection and tax aggressiveness during the COVID-19 pandemic and the role of business ethics in the association between political connection and tax aggressiveness. Design/methodology/approach – This study employs a multiple regression method for 147 manufacturing firms listed on the Indonesia Stock Exchange during the pandemic era. Findings – Political connection has no association with tax aggressiveness. However, political connection has a negative (positive) association with tax aggressiveness in more (less) ethical firms. The results are robust after controlling for year-fixed effects, endogeneity issues and other tax aggressiveness measurements. Originality/value – Political connection is often cited as the driver of unethical business, including tax aggressiveness. However, this paper claims and finds that political connection is a double-edged sword. Ethical firms use political connection to reduce their tax aggressiveness, and vice versa. Previous research has paid little attention to this topic. This paper also uses COVID-19 as a natural experiment to highlight the importance of corporate social responsibility activities as business ethics.

Kardiyem Kardiyem, Bandi Bandi, Kristiani Kristiani et al.

This study aims to analyze strategies universities can use to develop appropriate digital competencies for economics, business, and accounting lecturers and also provides an overview of the framework. This study employed a narrative literature review method with a qualitative approach consisting of several steps: compilation, tabulation, research result comparison, and summarization. The literature sources were the related articles dated within the last 15 years (2009-2023) acquired from reputable international journal sites (Emerald, Elsevier, Springer, Routledge, and MDPI). Content analysis technique was used to analyze the data. The results of this study showed that some existing frameworks in the world rarely include digital ethics dimensions. The digital competency framework for accounting lecturers consists of several elements, including the capacity to use digital sources, assessments, teaching and learning processes, as well as empowering digital literacy for students, small and medium-sized enterprises' people (UMKM), and also now BUMDES' people as well. Strategies that can be carried out by universities in developing human capital for lecturers of economics, business, and accounting in digital competence are by approaching the human capital component through university policies, providing organizational infrastructure and culture (Organization Climate), strategic leadership, or currently being able to adopt digital leadership (leadership component), and various training according to the needs and areas of expertise of lecturers.

Michele van Eck, Samantha Huneberg

Big data is changing the way many companies conduct business on a day-to-day basis. Insurers are notorious for utilising data in the risk assessment of prospective and current policyholders. The use of such risk assessment mechanisms in insurance has resulted in some discrimination between various policyholders but this has been held to be justifiable due to the fact that it is based on actuarial science and is therefore viewed as fair. However, the advent of big data, data analytics, algorithms, and artificial intelligence is providing insurers with far more sophisticated data about potential policyholders. This may prove to be beneficial to insurers in many ways, but it also brings about additional possibilities of exclusion within the industry. This use of big data has the potential to increase social injustices within our country, which is something that needs to be avoided as much as possible. Discrimination, through the use of big data, is a reality that needs to be addressed by insurers and regulators alike. Achieving social justice as far as possible in the insurance industry is crucial and also requires considerations in the areas of morality and ethics. The reason for this is that the very nature of big data is integrally linked to the assessment of the policyholder’s moral risks and hazards for the benefit of the insurer is often linked to personal circumstances and, sometimes, the financial circumstances of the policyholder, and may even speak to the policyholder’s integrity. Although potentially beneficial for the insurers from a risk assessment perspective, the use of big data has ethical and moral considerations within the insurance context. After all, the insurance industry’s collection, storage, and use of big data raises ethical and moral concerns and casts a shadow on the manner it is used to assess policyholders. This discussion highlights the need for regulatory oversight of big data, an aspect that is notably missing in the South African legislative framework.

Luyao Zhang, Yutong Sun, Yutong Quan et al.

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.

Eduardo C. Garrido-Merchán, Sara Lumbreras-Sancho

This paper adds to the efforts of evolutionary ethics to naturalize morality by providing specific insights derived from a computational ethics view. We propose a stylized model of human decision-making, which is based on Reinforcement Learning, one of the most successful paradigms in Artificial Intelligence. After the main concepts related to Reinforcement Learning have been presented, some particularly useful parallels are drawn that can illuminate evolutionary accounts of ethics. Specifically, we investigate the existence of an optimal policy (or, as we will refer to, objective ethical principles) given the conditions of an agent. In addition, we will show how this policy is learnable by means of trial and error, supporting our hypotheses on two well-known theorems in the context of Reinforcement Learning. We conclude by discussing how the proposed framework can be enlarged to study other potentially interesting areas of human behavior from a formalizable perspective.

Mela Anggilia, Joko Hadi Purnomo, Niswatin Nurul Hidayati

The purpose of this study is to describe and analyze the implementation of Islamic business ethics in the management of HCG (Hikma Collection Group) at Miftahul Hikmah Parengan Tuban, as well as the obstacles faced. This study used qualitative research methods. The results of this study are the implementation of Islamic business ethics in the management of HCG (Hikma Collection Group) using eight principles that are in accordance with Islamic teachings, namely the first with the principle of honesty in business, the second with the principle of selling good quality goods. The third principle is not selling out promises to consumers who want to buy at HCG (Hikma Collection Group). The fourth principle applied in HCG (Hikma Collection Group) is to be responsible in doing business regarding dissatisfaction in front of consumers and being responsible before God. The fifth principle is non-binding and generous to customers and all employees by getting used to being polite, smiling, greeting and friendly to all consumers and each employee. The sixth principle is the balance of doing business by always prioritizing the unity of every employee in HCG (Hikma Collection Group). The seventh principle is discipline in administration that uses honesty and timeliness in completing administrative reports. The last principle is to complete the product according to the target desired by the consumer. In addition, there are also several supporting and inhibiting factors in the implementation of Islamic business ethics in the institution.