Organizations are making massive investments in artificial intelligence, and recent demonstrations and achievements highlight the immense potential for AI to improve organizational and human welfare. Yet realizing the potential of AI necessitates a better understanding of the various ethical issues involved with deciding to use AI, training and maintaining it, and allowing it to make decisions that have moral consequences. People want organizations using AI and the AI systems themselves to behave ethically, but ethical behavior means different things to different (...) people, and many ethical dilemmas require trade-offs such that no course of action is universally considered ethical. How should organizations using AI—and the AI itself—process ethical dilemmas where humans disagree on the morally right course of action? Though a variety of ethical AI frameworks have been suggested, these approaches do not adequately address how people make ethical evaluations of AI systems or how to incorporate the fundamental disagreements people have regarding what is and is not ethical behavior. Drawing on moral foundations theory, we theorize that a person will perceive an organization’s use of AI, its data procedures, and the resulting AI decisions as ethical to the extent that those decisions resonate with the person’s moral foundations. Since people hold diverse moral foundations, this highlights the crucial need to consider individual moral differences at multiple levels of AI. We discuss several unresolved issues and suggest potential approaches for thinking about conflicts in moral judgments concerning AI. (shrink)

Some economists now predict that technology will eliminate many millions of jobs and lead to a future without work. Much debate focuses on the accuracy of such a prediction—whether, or at what rate, jobs will disappear. But there is a wider question raised by this prediction, namely the merits or otherwise of automating work. Beyond estimating future job losses via automation, there is the normative issue of whether the quality of life would be enhanced in a world where machines replace (...) humans in work. Economics makes particular assumptions about the value of work and the nature of well-being that can address this normative issue. But a deeper enquiry into the scope for living well in a possible automated future requires us to think beyond the limits of standard economic theory and to engage in matters of relevance to business ethicists. This paper shows how automation raises crucial concerns about work—its meaning and contribution to well-being—and how the ability to envisage a better future of work depends on bridging the gap between economics and business ethics. Overall, the paper aims to further understanding of automation as a possible mechanism to raise well-being within work and beyond it. (shrink)

Health research ethics training is highly variable, with some researchers receiving little to none, which is why ethical frameworks represent critical tools for ethical deliberation and guiding responsible practice. However, these documents' voluntary and abstract nature can leave health researchers seeking more operationalised guidance, such as in the form of checklists, even though this approach does not support reflection on the meaning of principles nor their implications. In search of more reflective and participatory practices in a pandemic context with distance (...) learning, this study explored whether co-design could support engagement and reflection on ethical principles amongst early-stage health researchers. In a pilot test using the Swiss Personalized Health Network's ethical framework for Responsible Usage of Personal Data in Health Research as a case study, we engaged health researchers to design visuals of four ethical principles. In two online workshops, participants (N = 10, N = 8) completed activities such as individual reflection, collaborative ideation, sketching, prototyping, discussion, and feedback. Our analysis shows that the co-design process helped foster in-depth engagement and reflection on the meaning and relevance of the SPHN ethical principles. Participants reported enjoying the experience, and most felt that visuals could motivate engagement with ethical frameworks. This study demonstrates the feasibility of a participatory, design-oriented approach to promote engagement with research ethics among early-career health researchers and highlights key challenges and lessons learned. In doing so, it lays the foundation for future research to investigate the impact of design-oriented, participatory learning to foster reflection and deliberation in ethics education. (shrink)

This article proposes a conceptual mapping to outline salient properties and relations that allow for a knowledge transfer from the well-established greenwashing phenomenon to the more recent machinewashing. We account for relevant dissimilarities, indicating where conceptual boundaries may be drawn. Guided by a “reasoning by analogy” approach, the article addresses the structural analogy and machinewashing idiosyncrasies leading to a novel and theoretically informed model of machinewashing. Consequently, machinewashing is defined as a strategy that organizations adopt to engage in misleading behavior (...) about ethical Artificial Intelligence /algorithmic systems. Machinewashing involves misleading information about ethical AI communicated or omitted via words, visuals, or the underlying algorithm of AI itself. Furthermore, and going beyond greenwashing, machinewashing may be used for symbolic actions such as lobbying and prevention of stricter regulation. By outlining diverse theoretical foundations of the established greenwashing domain and their relation to specific research questions, the article proposes a machinewashing model and a set of theory-related research questions on the macro, meso, and micro-level for future machinewashing research. We conclude by stressing limitations and by outlining practical implications for organizations and policymakers. (shrink)

Artificial intelligence has dramatically changed the way organizations communicate, understand, and interact with their potential consumers. In the context of this trend, the ethical considerations of advertising when applying AI should be the core question for marketers. This paper discusses six dominant algorithmic purchase decision pathways that align with ethical philosophies for online customers when buying a product/goods. The six ethical positions include: ethical egoism, deontology, relativist, utilitarianism, virtue ethics, and ethics of care. Furthermore, this paper launches an “intelligent advertising” (...) AI theme by examining its present and future as well as identifying the key phases of intelligent advertising. Several research questions are offered to guide future research on intelligent advertising to benefit ethical AI decision-making. Finally, several areas that can be widely applied to ethical intelligent advertising are suggested for future research. (shrink)

The world of work over the past 3 years has been characterized by a great reset due to the COVID-19 pandemic, giving an even more central role to scholarly discussions of ethics and the future of work. Such discussions have the potential to inform whether, when, and which work is viewed and experienced as meaningful. Yet, thus far, debates concerning ethics, meaningful work, and the future of work have largely pursued separate trajectories. Not only is bridging these research spheres important (...) for the advancement of meaningful work as a field of study but doing so can potentially inform the organizations and societies of the future. In proposing this Special Issue, we were inspired to address these intersections, and we are grateful to have this platform for advancing an integrative conversation, together with the authors of the seven selected scholarly contributions. Each article in this issue takes a unique approach to addressing these topics, with some emphasizing ethics while others focus on the future aspects of meaningful work. Taken together, the papers indicate future research directions with regard to: (a) the meaning of meaningful work, (b) the future of meaningful work, and (c) how we can study the ethics of meaningful work in the future. We hope these insights will spark further relevant scholarly and practitioner conversations. (shrink)

Despite the rapid adoption of technology in human resource departments, there is little empirical work that examines the potential challenges of algorithmic decision-making in the recruitment process. In this paper, we take the perspective of job applicants and examine how they perceive the use of algorithms in selection and recruitment. Across four studies on Amazon Mechanical Turk, we show that people in the role of a job applicant perceive algorithm-driven recruitment processes as less fair compared to human only or algorithm-assisted (...) human processes. This effect persists regardless of whether the outcome is favorable to the applicant or not. A potential mechanism underlying algorithm resistance is the belief that algorithms will not be able to recognize their uniqueness as a candidate. Although the use of algorithms has several benefits for organizations such as improved efficiency and bias reduction, our results highlight a potential cost of using them to screen potential employees during recruitment. (shrink)

Empathy is a specific moral aspect of human behavior. The global workplace, and thereby a consideration of employee stakeholders, includes unique behavioral and ethical considerations, including a consideration of human empathy. Further, the human aspects of workplaces are within the domain of human resources and managerial oversight in business organizations. As such, human emotions and interactions are complicated by daily work related expectations, employee/employer interactions and work practices, and the outcomes of employees’ work routines. Business ethics, human resources, and risk (...) management practices are endemic aspects within workplaces. Increasingly, the understanding of models of AI-reliant business practices underscores the need for the consideration of the ethical aspects of AI impacts on employees in the workplace. This paper explores a systematic ethical lens of the opportunities and the risks of AI ideation, development, and deployment in business-employee relations practices beyond a compliance mindset, and that introduces a further set of workplace considerations. Empathy is concerned with human intentions. As such, attributive ethical indications of the role of AI in the workplace and its impacts on employees is necessary. Moreover, this paper uses a cognitive lens of empathy and focuses on artificial morality related to the ethical concerns, implications, and practices of AI development, deployment, and workplace practices that may impact employees in a variety of business aspects. (shrink)

This study examines employee perceptions on the effective adoption of artificial intelligence principles in their organizations. 49 interviews were conducted with employees of 24 organizations across 11 countries. Participants worked directly with AI across a range of positions, from junior data scientist to Chief Analytics Officer. The study found that there are eleven components that could impact the effective adoption of AI principles in organizations: communication, management support, training, an ethics office, a reporting mechanism, enforcement, measurement, accompanying technical processes, a (...) sufficient technical infrastructure, organizational structure, and an interdisciplinary approach. The components are discussed in the context of business code adoption theory. The findings offer a first step in understanding potential methods for the effective adoption of AI principles in organizations. (shrink)

Several technological developments, such as self-service technologies and artificial intelligence, are disrupting the retailing industry by changing consumption and purchase habits and the overall retail experience. Although AI represents extraordinary opportunities for businesses, companies must avoid the dangers and risks associated with the adoption of such systems. Integrating perspectives from emerging research on AI, morality of machines, and norm activation, we examine how individuals morally behave toward AI agents and self-service machines. Across three studies, we demonstrate that consumers’ moral concerns (...) and behaviors differ when interacting with technologies versus humans. We show that moral intention is less likely to emerge for AI checkout and self-checkout machines compared with human checkout. In addition, moral intention decreases as people consider the machine less humanlike. We further document that the decline in morality is caused by less guilt displayed toward new technologies. The non-human nature of the interaction evokes a decreased feeling of guilt and ultimately reduces moral behavior. These findings offer insights into how technological developments influence consumer behaviors and provide guidance for businesses and retailers in understanding moral intentions related to the different types of interactions in a shopping environment. (shrink)

Understanding the moral right and wrong in the context of business practice has long captivated the attention of researchers and business leaders (Brenkert, 2019). Fueled by ethical failures recoun...

The use of artificial intelligence (AI) in hiring entails vast ethical challenges. As such, using an ethical lens to study this phenomenon is to better understand whether and how AI matters in hiring. In this paper, we examine whether ethical perceptions of using AI in the hiring process influence individuals’ trust in the organizations that use it. Building on the organizational trust model and the unified theory of acceptance and use of technology, we explore whether ethical perceptions are shaped by (...) individual differences in performance expectancy and social influence and how they, in turn, impact organizational trust. We collected primary data from over 300 individuals who were either active job seekers or who had recent hiring experience to capture perceptions across the full range of hiring methods. Our findings indicate that performance expectancy, but not social influence, impacts the ethical perceptions of AI in hiring, which in turn influence organizational trust. Additional analyses indicate that these findings vary depending on the type of hiring methods AI is used for, as well as on whether participants are job seekers or individuals with hiring experience. Our study offers theoretical and practical implications for ethics in HRM and informs policy implementation about when and how to use AI in hiring methods, especially as it pertains to acting ethically and trustworthily. (shrink)

More organizations use AI in the hiring process than ever before, yet the perceived ethicality of such processes seems to be mixed. With such variation in our views of AI in hiring, we need to understand how these perceptions impact the organizations that use it. In two studies, we investigate how ethical perceptions of using AI in hiring are related to perceptions of organizational attractiveness and innovativeness. Our findings indicate that ethical perceptions of using AI in hiring are positively related (...) to perceptions of organizational attractiveness, both directly and indirectly via perceptions of organizational innovativeness, with variations depending on the type of hiring method used. For instance, we find that individuals who consider it ethical for organizations to use AI in ways often considered to be intrusive to privacy, such as analyzing social media content for traits and characteristics, view such organizations as both more innovative and attractive. Our findings trigger a timely discussion about the critical role of ethical perceptions of AI in hiring on organizational attractiveness and innovativeness. (shrink)