Studies of micro-level price datasets find more frequent small price increases than decreases, which can be explained by consumer inattention because time-constrained shoppers might ignore small price changes. Recent empirical studies of the link between shopping behavior and price attention over the business cycle find that consumers are more attentive to prices during economic downturns, and less attentive during economic booms. These two sets of findings have a testable implication. The asymmetry in small price changes should vary over the business cycle. It should diminish during recessions and strengthen during expansions. We test this prediction using a large US store-level dataset with more than 98 million weekly price observations for the years 1989-1997, which includes an 8-month recession period, as defined by the NBER. We compare price adjustments between periods of recession - high unemployment, and expansion - low unemployment. Focusing on small price changes, we find, consistent with our hypothesis, that there is a greater asymmetry in small price changes during periods of low unemployment compared to the periods of high unemployment, implying that firms price-setting behavior varies over the business cycle.
James M. Berzuk, Lauren Corcoran, Brannen McKenzie-Lefurgey
et al.
Contemporary robots are increasingly mimicking human social behaviours to facilitate interaction, such as smiling to signal approachability, or hesitating before taking an action to allow people time to react. Such techniques can activate a person's entrenched social instincts, triggering emotional responses as though they are interacting with a fellow human, and can prompt them to treat a robot as if it truly possesses the underlying life-like processes it outwardly presents, raising significant ethical questions. We engage these issues through the lens of informed consent: drawing upon prevailing legal principles and ethics, we examine how social robots can influence user behaviour in novel ways, and whether under those circumstances users can be appropriately informed to consent to these heightened interactions. We explore the complex circumstances of human-robot interaction and highlight how it differs from more familiar interaction contexts, and we apply legal principles relating to informed consent to social robots in order to reconceptualize the current ethical debates surrounding the field. From this investigation, we synthesize design goals for robot developers to achieve more ethical and informed human-robot interaction.
Jeroen Middelhuis, Zaharah Bukhsh, Ivo Adan
et al.
Resource allocation plays a critical role in minimizing cycle time and improving the efficiency of business processes. Recently, Deep Reinforcement Learning (DRL) has emerged as a powerful technique to optimize resource allocation policies in business processes. In the DRL framework, an agent learns a policy through interaction with the environment, guided solely by reward signals that indicate the quality of its decisions. However, existing algorithms are not suitable for dynamic environments such as business processes. Furthermore, existing DRL-based methods rely on engineered reward functions that approximate the desired objective, but a misalignment between reward and objective can lead to undesired decisions or suboptimal policies. To address these issues, we propose a rollout-based DRL algorithm and a reward function to optimize the objective directly. Our algorithm iteratively improves the policy by evaluating execution trajectories following different actions. Our reward function directly decomposes the objective function of minimizing the cycle time, such that trial-and-error reward engineering becomes unnecessary. We evaluated our method in six scenarios, for which the optimal policy can be computed, and on a set of increasingly complex, realistically sized process models. The results show that our algorithm can learn the optimal policy for the scenarios and outperform or match the best heuristics on the realistically sized business processes.
Healthcare stands at a critical crossroads. Artificial Intelligence and modern computing are unlocking opportunities, yet their value lies in the data that fuels them. The value of healthcare data is no longer limited to individual patients. However, data stewardship and governance has not kept pace, and privacy-centric policies are hindering both innovation and patient protections. As healthcare moves toward a data-driven future, we must define reformed data stewardship that prioritizes patients' interests by proactively managing modern risks and opportunities while addressing key challenges in cost, efficacy, and accessibility. Current healthcare data policies are rooted in 20th-century legislation shaped by outdated understandings of data-prioritizing perceived privacy over innovation and inclusion. While other industries thrive in a data-driven era, the evolution of medicine remains constrained by regulations that impose social rather than scientific boundaries. Large-scale aggregation is happening, but within opaque, closed systems. As we continue to uphold foundational ethical principles - autonomy, beneficence, nonmaleficence, and justice - there is a growing imperative to acknowledge they exist in evolving technological, social, and cultural realities. Ethical principles should facilitate, rather than obstruct, dialogue on adapting to meet opportunities and address constraints in medical practice and healthcare delivery. The new ethics of data stewardship places patients first by defining governance that adapts to changing landscapes. It also rejects the legacy of treating perceived privacy as an unquestionable, guiding principle. By proactively redefining data stewardship norms, we can drive an era of medicine that promotes innovation, protects patients, and advances equity - ensuring future generations advance medical discovery and care.
A computational ethics framework is essential for AI and autonomous systems operating in complex, real-world environments. Existing approaches often lack the adaptability needed to integrate ethical principles into dynamic and ambiguous contexts, limiting their effectiveness across diverse scenarios. To address these challenges, we outline the necessary ingredients for building a holistic, meta-level framework that combines intermediate representations, probabilistic reasoning, and knowledge representation. The specifications therein emphasize scalability, supporting ethical reasoning at both individual decision-making levels and within the collective dynamics of multi-agent systems. By integrating theoretical principles with contextual factors, it facilitates structured and context-aware decision-making, ensuring alignment with overarching ethical standards. We further explore proposed theorems outlining how ethical reasoners should operate, offering a foundation for practical implementation. These constructs aim to support the development of robust and ethically reliable AI systems capable of navigating the complexities of real-world moral decision-making scenarios.
Abstract This paper investigates the role of business ethics consultants in translating business ethics into practical solutions within organizations. Despite the wealth of research on business ethics, practitioners often report difficulty in applying academic insights in their organizational context. To bridge this gap, organizations often engage external ethics consultants to help translate theory into practical solutions for navigating challenging ethical situations. Through 17 semi-structured interviews with ethics consultants who work in digital work environments, we developed the Assessment-Calibration-Synchronization framework, demonstrating how their translation work renders business ethics knowledge relevant and adoptable for their clients. This framework encompasses three phases: assessing ethical context, calibrating strategies to organizational goals, and synchronizing ethical practices. Our study contributes to the business ethics literature by offering insights into how ethics consultants translate business ethics into practice and promote ethical change. This research also offers practical guidance for organizations seeking to improve their ethical practices and for ethics consultants aiming to enhance their impact in corporate settings.
The endeavor of interstellar exploration is a convergence of technical innovation and profound ethical inquiry, challenging humanity to extend its reach beyond the confines of our solar system while contemplating the moral implications of such a leap. This paper explores the multifaceted aspects of interstellar travel, exploring advancements in propulsion systems, habitat construction, and life support alongside the ethical, sociopolitical, and philosophical questions that arise as we consider colonizing extraterrestrial worlds. We underscore the imperative for an integrative framework harmonizing scientific achievements with a deep ethical commitment to responsible exploration, environmental stewardship, and respect for potential extraterrestrial life. Our analysis highlights the dual nature of interstellar exploration as both a technical endeavor and a philosophical journey, advocating for a future in which humanity's expansion into the cosmos is guided by foresight, equity, and the collective well-being of all sentient beings. This synthesis of science and ethics offers a blueprint for navigating the unknowns of space with wisdom and integrity, ensuring that our interstellar aspirations reflect the best of human values.
Event log records all events that occur during the execution of business processes, so detecting and correcting anomalies in event log can provide reliable guarantee for subsequent process analysis. The previous works mainly include next event prediction based methods and autoencoder-based methods. These methods cannot accurately and efficiently detect anomalies and correct anomalies at the same time, and they all rely on the set threshold to detect anomalies. To solve these problems, we propose a business process anomaly correction method based on Transformer autoencoder. By using self-attention mechanism and autoencoder structure, it can efficiently process event sequences of arbitrary length, and can directly output corrected business process instances, so that it can adapt to various scenarios. At the same time, the anomaly detection is transformed into a classification problem by means of selfsupervised learning, so that there is no need to set a specific threshold in anomaly detection. The experimental results on several real-life event logs show that the proposed method is superior to the previous methods in terms of anomaly detection accuracy and anomaly correction results while ensuring high running efficiency.
Data visualizations are inherently rhetorical, and therefore bias-laden visual artifacts that contain both explicit and implicit arguments. The implicit arguments depicted in data visualizations are the net result of many seemingly minor decisions about data and design from inception of a research project through to final publication of the visualization. Data workflow, selected visualization formats, and individual design decisions made within those formats all frame and direct the possible range of interpretation, and the potential for harm of any data visualization. Considering this, it is imperative that we take an ethical approach to the creation and use of data visualizations. Therefore, we have suggested an ethical data visualization workflow with the dual aim of minimizing harm to the subjects of our study and the audiences viewing our visualization, while also maximizing the explanatory capacity and effectiveness of the visualization itself. To explain this ethical data visualization workflow, we examine two recent digital mapping projects, Racial Terror Lynchings and Map of White Supremacy Mob Violence.
Abstract This investigation is based on a quantitative method and approach from inferential statistics. This study addresses urgent need for social equality and the desire for a sustainable business environment following emerging realities, climatic changes, and environmental issues based on a framework built on corporate social responsibility (CSR). The primary data were acquired from respondents via questionnaire administration and interviews from a random poll performed in Rome. The results of the hypothesis connecting brand image and social responsibility showed a high value of p = 1.000, exceeding the set critical limit of 0.05; thus, companies and organizations that support socially responsible practices are drivers and vanguards for promoting and entrenching social equality, trust, and mutual engagements with the stakeholders and societies from which they draw resources for their activities. Finally, relevant and novel models have been presented that unravel and unveil the templates and working frame for achieving social equality and sustainability while addressing environmental issues associated with business activities, emphasizing value -based creation, social equality, and sustainable marketing on a precept and foundational framework of social responsibility and corporate identity, or ‘CSR’. This led to key recommendations crucial to the business environment, policymakers, stakeholders, and decision-makers in politics.
Social responsibility of business, Business ethics
The axis from which we choose to build our argument is that of “Artificial intelligence and democracy”. The objective of the proposal is to define rules that keep the human being at the center of the advancement of artificial intelligence. It is in this context of the advance of new technologies that the need to protect the values of democracy and individual freedoms, currently threatened in several countries, such as Brazil, clearly appears. The rise of the “surveillance state” is a global reality that directly interferes with this challenge. Contemporary digital social and political participation (e-Democracy) is a product of the digitization of the state and its apparatuses, characterized by the production of new rights made possible by communication technologies. The digitization of the State apparatus thanks to new technologies based on intelligent algorithms and the rules of the information and communication society, have triggered the production of so-called "new rights" whose applicability expands the concept of democracy by differentiating between traditional business governance, and the growing demands of communities increasingly linked to the digital communication system. The rights of access to the Internet and the network, to electronic voting, to communicate thanks to new technologies, to receive digital public services are parallel to the duties of the State, characterized by the satisfaction of new rights. At the same time, there is a growing risk that forms of digital participation produce intolerable levels of exclusion that undermine democracy. Based on suggestions made by the latest frontiers of AI research, the next artificial intelligences could benefit from the power of Qubit, the ability to learn through biological neural networks beyond the usual level of Machine Learning to achieve realiser the utopia or the nightmare of many: to have computerized machines capable of deciding for themselves. What impact will these innovations have on the shape and resilience of democracies? In the presence of an evolving ecosystem of non-moral artificial intelligences, is there an obligation to think of a new ethics? Using an analytical-critical method, we are going to call upon three elements that shape the "triple revolution", which led to this transformation: the emergence of social networks, the ability of the Internet to reach individuals and connectivity.
Technology has advanced dramatically in the previous several years. There are also cyber assaults. Cyberattacks pose a possible danger to information security and the general public. Since data practice and internet consumption rates continue to upswing, cyber awareness has become progressively important. Furthermore, as businesses pace their digital transformation with mobile devices, cloud services, communal media, and Internet of Things services, cybersecurity has appeared as a critical issue in corporate risk management. This research focuses on the relations between cybersecurity awareness, cyber knowledge, computer ethics, cyber ethics, and cyber behavior, as well as protective tools, across university students in general. The findings express that while internet users are alert of cyber threats, they only take the most elementary and easy-to-implement precautions. Several knowledge and awareness have been proposed to knob the issue of cyber security. It also grants the principles of cybersecurity in terms of its structure, workforces, and evidence pertaining to the shield of personal information in the cyber world. The first step is for people to educate themselves about the negative aspects of the internet and to learn more about cyber threats so that they can notice when an attack is taking place. To validate the efficiency of the suggested analysis between CS and non-CS university students, case study along with several comparisons are provided.
In 2022, the American Statistical Association revised its Ethical Guidelines for Statistical Practice. Originally issued in 1982, these Guidelines describe responsibilities of the 'ethical statistical practitioner' to their profession, to their research subjects, as well as to their community of practice. These guidelines are intended as a framework to assist decision-making by statisticians working across academic, research, and government environments. For the first time, the 2022 Guidelines describe the ethical obligations of organizations and institutions that use statistical practice. This paper examines alignment between the ASA Ethical Guidelines and other long-established normative guidelines for US official statistics: the OMB Statistical Policy Directives 1, 2, and 2a NASEM Principles and Practices, and the OMB Data Ethics Tenets. Our analyses ask how the recently updated ASA Ethical Guidelines can support these guidelines for federal statistics and data science. The analysis uses a form of qualitative content analysis, the alignment model, to identify patterns of alignment, and potential for tensions, within and across guidelines. The paper concludes with recommendations to policy makers when using ethical guidance to establish parameters for policy change and the administrative and technical controls that necessarily follow.
Sunder Ali Khowaja, Parus Khuwaja, Kapal Dev
et al.
ChatGPT is another large language model (LLM) vastly available for the consumers on their devices but due to its performance and ability to converse effectively, it has gained a huge popularity amongst research as well as industrial community. Recently, many studies have been published to show the effectiveness, efficiency, integration, and sentiments of chatGPT and other LLMs. In contrast, this study focuses on the important aspects that are mostly overlooked, i.e. sustainability, privacy, digital divide, and ethics and suggests that not only chatGPT but every subsequent entry in the category of conversational bots should undergo Sustainability, PrivAcy, Digital divide, and Ethics (SPADE) evaluation. This paper discusses in detail the issues and concerns raised over chatGPT in line with aforementioned characteristics. We also discuss the recent EU AI Act briefly in accordance with the SPADE evaluation. We support our hypothesis by some preliminary data collection and visualizations along with hypothesized facts. We also suggest mitigations and recommendations for each of the concerns. Furthermore, we also suggest some policies and recommendations for EU AI policy act concerning ethics, digital divide, and sustainability
Josef Valvoda, Alec Thompson, Ryan Cotterell
et al.
The introduction of large public legal datasets has brought about a renaissance in legal NLP. Many of these datasets are comprised of legal judgements - the product of judges deciding cases. This fact, together with the way machine learning works, means that several legal NLP models are models of judges. While some have argued for the automation of judges, in this position piece, we argue that automating the role of the judge raises difficult ethical challenges, in particular for common law legal systems. Our argument follows from the social role of the judge in actively shaping the law, rather than merely applying it. Since current NLP models come nowhere close to having the facilities necessary for this task, they should not be used to automate judges. Furthermore, even in the case the models could achieve human-level capabilities, there would still be remaining ethical concerns inherent in the automation of the legal process.
Job satisfaction is the result of an employee's perception of their work and the impact of their actions, attitudes and values on personal and professional life. Potential consequences of job discontentment may include high staff turnover, increased absenteeism, alcohol or drug addiction, and accidents at the workplace. The article aims to develop a methodology for assessing employee job satisfaction to substantiate the cause-and-effect relationship between job satisfaction and the number of workplace accidents. The subject of the study is the Algerian Electricity and Gas Company in Hassi Messaoud, which employs more than 82 thousand employees and has more than 11 million electricity consumers and more than 7 million gas consumers. The study is based on a survey of employees of the operation and human resources departments, for which a random sample was formed, and 84.16% of the questionnaires were valid for analysis. The Cronbach's Alpha coefficient was used to assess the internal consistency of the questionnaire, which professional experts reviewed. The SPSS statistical package was used to process the data. The study showed that among all dimensions of job satisfaction, the most important for the surveyed respondents were satisfaction with the leadership style and job stability, while less important were workload, satisfaction with the work environment, and remuneration. Future research is needed to examine the factors that will contribute to the reduction of workplace accidents; further efforts are needed to understand the many aspects that affect other sectors of the economy and different industries.
Corporate scandals, the deepening global financial and environmental crisis as well as other societal ills have compelled leaders to rethink leadership styles. Recently benevolent leadership has emerged as a contemporary leadership style with promise to advance business ethics, corporate social responsibility, positive organizational building and workplace spirituality. Guided by quantitative research methodology, with a cross-sectional survey research design, 314 leaders were recruited across South Africa, to investigate the characteristics of benevolent leaders and how their leadership style influenced organizational performance. The study found a high level of benevolent leadership qualities and characteristics, amongst the sample, which consequently influenced their organizational performance in the areas of employee morale, productivity and corporate social responsibility.