Companies increasingly deploy artificial intelligence technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty of AI (...) applications in recruiting practice, the subject is still an emerging topic in academic literature. To inform and strengthen the foundation for future research, this paper systematically reviews the extant literature on the ethicality of AI-enabled recruiting to date. We identify 51 articles dealing with the topic, which we synthesize by mapping the ethical opportunities, risks, and ambiguities, as well as the proposed ways to mitigate ethical risks in practice. Based on this review, we identify gaps in the extant literature and point out moral questions that call for deeper exploration in future research. (shrink)

Metrics shape our social worlds in many and more ways. Everyday quantifications of our preferences, our behaviors and our relationships, alter us and the institutions that we constitute. This essay takes a brief look at the metrics of business ethics through two analytic devices. Representation explains the notion that metrics can capture or demonstrate ethics and performativity explains the notion that metrics can shape or constitute ethics. The analytic distinction between representation and performativity is obscured in practice when metrics become (...) targets, indeed much of the social power of metrics comes from their use as targets. Hence, we should pay attention to the world of practice in which measuring and doing are entangled. However, we should not lose sight of the limitations of measurement and the possibility that there are areas of ethical life best left unmeasured. (shrink)

Difficult moral issues in economic life, such as evaluating the impact of hostile takeovers and plant relocations or determining the obligations of business to the environment, constitute the raison d'etre of business ethics. Yet, while the ultimate resolution of such issues clearly requires detailed, normative analysis, a shortcoming of business ethics is that to date it has failed to develop an adequate normative theory.1 The failing is especially acute when it results in an inability to provide a basis for fine-grained (...) analyses of issues. Both general moral theories and stakeholder theory seem incapable of expressing the moral complexity necessary to provide practical normative guidance for many business ethics contexts. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

Mind perception entails ascribing mental capacities to other entities, whereas moral judgment entails labeling entities as good or bad or actions as right or wrong. We suggest that mind perception is the essence of moral judgment. In particular, we suggest that moral judgment is rooted in a cognitive template of two perceived minds—a moral dyad of an intentional agent and a suffering moral patient. Diverse lines of research support dyadic morality. First, perceptions of mind are linked to moral judgments: dimensions (...) of mind perception (agency and experience) map onto moral types (agents and patients), and deficits of mind perception correspond to difficulties with moral judgment. Second, not only are moral judgments sensitive to perceived agency and experience, but all moral transgressions are fundamentally understood as agency plus experienced suffering—that is, interpersonal harm—even ostensibly harmless acts such as purity violations. Third, dyadic morality uniquely accounts for the phenomena of dyadic completion (seeing agents in response to patients, and vice versa), and moral typecasting (characterizing others as either moral agents or moral patients). Discussion also explores how mind perception can unify morality across explanatory levels, how a dyadic template of morality may be developmentally acquired, and future directions. (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)

Recent advances in machine learning methods have created opportunities to eliminate unfairness from algorithmic decision making. Multiple computational techniques (i.e., algorithmic fairness criteria) have arisen out of this work. Yet, urgent questions remain about the perceived fairness of these criteria and in which situations organizations should use them. In this paper, we seek to gain insight into these questions by exploring fairness perceptions of five algorithmic criteria. We focus on two key dimensions of fairness evaluations: distributive fairness and procedural fairness. (...) We shed light on variation in the potential for different algorithmic criteria to facilitate distributive fairness. Subsequently, we discuss procedural fairness and provide a framework for understanding how algorithmic criteria relate to essential aspects of this construct, which helps to identify when a specific criterion is suitable. From a practical standpoint, we encourage organizations to recognize that managing fairness in machine learning systems is complex, and that adopting a blind or one-size-fits-all mentality toward algorithmic criteria will surely damage people’s attitudes and trust in automated technology. Instead, firms should carefully consider the subtle yet significant differences between these technical solutions. (shrink)

Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms' actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algorithmic management, we conducted an online experiment using four managerial decisions that required either mechanical or human skills. We manipulated the decision-maker, and measured perceived fairness, trust, and emotional response. With the mechanical (...) tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers' fairness and trustworthiness were attributed to the manager's authority, whereas algorithms' fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms' perceived lack of intuition and subjective judgment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people's lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people's experiences with algorithmic technologies. (shrink)

Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize algorithms as value-laden, rather than neutral, in that algorithms (...) create moral consequences, reinforce or undercut ethical principles, and enable or diminish stakeholder rights and dignity. In addition, algorithms are an important actor in ethical decisions and influence the delegation of roles and responsibilities within these decisions. As such, firms should be responsible not only for the value-laden-ness of an algorithm but also for designing who-does-what within the algorithmic decision. As such, firms developing algorithms are accountable for designing how large a role individual will be permitted to take in the subsequent algorithmic decision. Counter to current arguments, I find that if an algorithm is designed to preclude individuals from taking responsibility within a decision, then the designer of the algorithm should be held accountable for the ethical implications of the algorithm in use. (shrink)

In this paper, I will argue that it is a moral obligation for companies, firstly, to accept their moral responsibility with respect to non-discrimination, and secondly, to address the issue with a full-fledged programme, including but not limited to the countering of microsocial discrimination processes through specific policies. On the basis of a broad sketch of how some discrimination mechanisms are actually influencing decisions, that is, causing intended as well as unintended bias in Human Resources Management, I will argue that (...) the well known tools of legislation and ethical codes are necessary although insufficient to cope with the problem. However, based on empirical evidence, we know which set of measures is likely to diminish discrimination. Taking non-discrimination seriously implies complex and longitudinal policies which include assigning responsibility for a non-discrimination policy within the firm, making managers conscious of implicit stereotypes and helping them to cope with prejudices that no one can totally overcome. Insofar as corporate responsibility with respect to non-discrimination is accepted and strategies that are not prohibitively expensive are known, companies are bound to implement them. Not implementing the best set of measures may be considered at least as a moral shortcoming or, depending on the size of the company, mere lip service to the non-discrimination principle. Although the paper refers to empirical material of diverse backgrounds, its intent is clearly normative. It wishes to spell out what companies ought to do if they are committed to responsible behaviour. The discussion of effective remedies against discrimination is based on a case study of a French company. The retailer Auchan was recently surprised to learn that it was discriminating against ethnic minorities despite strong ethical standards, an ethics committee and ethical leadership. The company dropped its naïve beliefs and set up an ambitious policy cope with the issue. The case illustrates what recent empirical research has revealed about the effectiveness of diversity policies: establishing responsibility for diversity results, firm ethical commitment and support from top management make diversity programs effective. (shrink)

Businesses increasingly rely on algorithms that are data-trained sets of decision rules (i.e., the output of the processes often called “machine learning”) and implement decisions with little or no human intermediation. In this article, we provide a philosophical foundation for the claim that algorithmic decision-making gives rise to a “right to explanation.” It is often said that, in the digital era, informed consent is dead. This negative view originates from a rigid understanding that presumes informed consent is a static and (...) complete transaction. Such a view is insufficient, especially when data are used in a secondary, noncontextual, and unpredictable manner—which is the inescapable nature of advanced artificial intelligence systems. We submit that an alternative view of informed consent—as an assurance of trust for incomplete transactions—allows for an understanding of why the rationale of informed consent already entails a right to ex post explanation. (shrink)

This paper reviews and develops the ethical analysis of human resource management (HRM). Initially, the ethical perspective of HRM is differentiated from the "mainstrea" and critical perspectives of HRM. To date, the ethical analysis of HRM has taken one of two forms: the application Kantian and utilitarian ethical theories to the gestalt of HRM, and the application of theories of justice and fairness to specific HRM practices. This paper is concerned with the former, the ethical analysis of HRM in its (...) entirety. It shows that numerous theoretical shortcomings exist, least of which is the disregard of stakeholder theory. These deficiencies are explored and, as such, the analysis is advanced. It is argued that such ethical analysis is outside the scope of the modern corporation. A third way in which ethics may be applied to HRM is suggested. Ethical concerns are used as a basis to develop minimum standards against which HRM, in its various guises and practice, may be evaluated. Yet, even when judged by these standards, HRM is seriously lacking. This begs the question, not of whether HRM is ethical, but of whether HRM can be ethical. (shrink)

Participants compared the mental capacities of various human and nonhuman characters via online surveys. Factor analysis revealed two dimensions of mind perception, Experience and Agency. The dimensions predicted different moral judgments but were both related to valuing of mind.

Difficult moral issues in economic life, such as evaluating the impact of hostile takeovers and plant relocations or determining the obligations of business to the environment, constitute the raison d'etre of business ethics. Yet, while the ultimate resolution of such issues clearly requires detailed, normative analysis, a shortcoming of business ethics is that to date it has failed to develop an adequate normative theory.1 The failing is especially acute when it results in an inability to provide a basis for fine-grained (...) analyses of issues. Both general moral theories and stakeholder theory seem incapable of expressing the moral complexity necessary to provide practical normative guidance for many business ethics contexts. (shrink)

Since the passage of Title VII of the Civil Rights Act of 1964 and more recent Federal legislation, managers, regulators, and attorneys have been busy in sorting out the legal meaning of fairness in employment. While ethical managers must follow the law in their hiring practices, they cannot be satisfied with legal compliance. In this article, we first briefly summarize what the law requires in terms of fair hiring practices. We subsequently rely on multiple perspectives to explore the ethical meaning (...) of fairness in hiring. Ethical fairness underlies the law and regulations in this area, but goes beyond them as well. We conclude by demonstrating that ethical hiring practices enable managers to make better hiring decisions. (shrink)

Fairness of Artificial Intelligence decisions has become a big challenge for governments, companies, and societies. We offer a theoretical contribution to consider AI ethics outside of high-level and top-down approaches, based on the distinction between “reality” and “world” from Luc Boltanski. To do so, we provide a new perspective on the debate on AI fairness and show that criticism of ML unfairness is “realist”, in other words, grounded in an already instituted reality based on demographic categories produced by institutions. Second, (...) we show that the limits of “realist” fairness corrections lead to the elaboration of “radical responses” to fairness, that is, responses that radically change the format of data. Third, we show that fairness correction is shifting to a “domination regime” that absorbs criticism, and we provide some theoretical and practical avenues for further development in AI ethics. Using an ad hoc critical space stabilized by reality tests alongside the algorithm, we build a shared responsibility model which is compatible with the radical response to fairness issues. Finally, this paper shows the fundamental contribution of pragmatic sociology theories, insofar as they afford a social and political perspective on AI ethics by giving an active role to material actors such as database formats on ethical debates. In a context where data are increasingly numerous, granular, and behavioral, it is essential to renew our conception of AI ethics on algorithms in order to establish new models of responsibility for companies that take into account changes in the computing paradigm. (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)

Perceived supervisor support is widely studied in terms of its positive outcomes. This paper, in contrast, investigates employees’ unethical pro-supervisor behavior as a negative consequence of perceived supervisor support. Drawing upon the multifoci approach of social exchange theory and the reciprocity principle, we hypothesized that perceived supervisor support can engender unethical pro-supervisor behavior via employees’ feelings of reciprocity towards the supervisor. Building on the instrumental reasons that underlie social exchanges, we further hypothesized that this mediation relationship is stronger for employees (...) high in Machiavellianism. We collected data for three experimental studies from full-time MBA students of a European business school and from U.S. working professionals, and the results provided consistent support for our proposed model. Taken together, the current study highlights the “dark side” of perceived supervisor support, in that it can lead to unethical behavior and that this effect can be accentuated by employees’ Machiavellianism. (shrink)

Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use of participatory (...) design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility. (shrink)

This conceptual paper explores the relationship between an organization’s exclusive talent management practices, employees’ perceptions of the fairness of exclusive TM practices, and the corresponding impact on employee engagement. We propose that in organizations pursuing exclusive TM programs, employee perceptions of organizational justice of the exclusive TM practices may affect their employee engagement, which may influence both organizational and employee outcomes. Building on extant research, we present a conceptual framework depicting the relationship between exclusive TM practices, organizational justice and employee (...) engagement, with social exchange theory and equity theory as the framework’s foundation. The propositions in the framework are each supported by the respective literature. The perceived organizational justice and potential ramifications of exclusive TM practices for employees who are not included in corporate talent pools is an under-researched topic. The paper considers the perspectives of employees not included in corporate talent pools and explores how exclusive TM practices, as inputs, could lead to negative employee engagement outputs. In unpacking how exclusive TM practices could impact on employee engagement, the implications for organizations are underlined. The ethics and perceived fairness of exclusive TM practices, which have the potential to marginalize employees and lead to their disengagement, are considered. (shrink)

The very idea of human resource management raises ethical considerations: What does it mean to us as humans for human beings to be managed as resources? Intriguingly, the field of ethics and HRM remains underdeveloped. Current approaches to HRM fail to place ethical considerations as their central warrant. This article, building on Greenwood (J Bus Ethics 36(3):261–279, 2002), argues for a deeper analysis of ethical issues in HRM, indeed for a differentiated ethical perspective of HRM that sets normative deliberations as (...) its prime task. By identifying a distinct ethical approach to HRM that is unashamedly normative and socio-politically embedded, two objectives can be achieved. First, mainstream and critical approaches will be challenged to take ethical issues in HRM more seriously. Second, a dedicated forward-looking research agenda for the ethical analysis of HRM will be advanced. (shrink)

While the ethics of technology is analyzed across disciplines from science and technology studies, engineering, computer science, critical management studies, and law, less attention is paid to the role that firms and managers play in the design, development, and dissemination of technology across communities and within their firm. Although firms play an important role in the development of technology, and make associated value judgments around its use, it remains open how we should understand the contours of what firms owe society (...) as the rate of technological development accelerates. We focus here on digital technologies: devices that rely on rapidly accelerating digital sensing, storage, and transmission capabilities to intervene in human processes. This symposium focuses on how firms should engage ethical choices in developing and deploying these technologies. In this introduction, we, first, identify themes the symposium articles share and discuss how the set of articles illuminate diverse facets of the intersection of technology and business ethics. Second, we use these themes to explore what business ethics offers to the study of technology and, third, what technology studies offers to the field of business ethics. Each field brings expertise that, together, improves our understanding of the ethical implications of technology. Finally we introduce each of the five papers, suggest future research directions, and interpret their implications for business ethics. (shrink)

Algorithmic decision-making increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along (...) four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of algorithmic decision-making. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible algorithmic decision-making. (shrink)

In this paper, I will argue that it is a moral obligation for companies, firstly, to accept their moral responsibility with respect to non-discrimination, and secondly, to address the issue with a full-fledged programme, including but not limited to the countering of microsocial discrimination processes through specific policies. On the basis of a broad sketch of how some discrimination mechanisms are actually influencing decisions, that is, causing intended as well as unintended bias in Human Resources Management (HRM), I will argue (...) that the well known tools of legislation and ethical codes are necessary although insufficient to cope with the problem. However, based on empirical evidence, we know which set of measures is likely to diminish discrimination. Taking non-discrimination seriously implies complex and longitudinal policies which include assigning responsibility for a non-discrimination policy within the firm, making managers conscious of implicit stereotypes and helping them to cope with prejudices that no one can totally overcome. Insofar as corporate responsibility with respect to non-discrimination is accepted and strategies that are not prohibitively expensive are known, companies are bound to implement them. Not implementing the best set of measures may be considered at least as a moral shortcoming or, depending on the size of the company, mere lip service to the non-discrimination principle. Although the paper refers to empirical material of diverse backgrounds, its intent is clearly normative. It wishes to spell out what companies ought to do if they are committed to responsible behaviour. The discussion of effective remedies against discrimination is based on a case study of a French company. The retailer Auchan was recently surprised to learn that it was discriminating against ethnic minorities despite strong ethical standards, an ethics committee and ethical leadership. The company dropped its naïve beliefs and set up an ambitious policy cope with the issue. The case illustrates what recent empirical research has revealed about the effectiveness of diversity policies: establishing responsibility for diversity results, firm ethical commitment and support from top management make diversity programs effective. (shrink)

Sophisticated technology is increasingly replacing human minds to perform complicated tasks in domains ranging from medicine to education to transportation. We investigated an important theoretical determinant of people's willingness to trust such technology to perform competently—the extent to which a nonhuman agent is anthropomorphized with a humanlike mind—in a domain of practical importance, autonomous driving. Participants using a driving simulator drove either a normal car, an autonomous vehicle able to control steering and speed, or a comparable autonomous vehicle augmented with (...) additional anthropomorphic features—name, gender, and voice. Behavioral, physiological, and self-report measures revealed that participants trusted that the vehicle would perform more competently as it acquired more anthropomorphic features. Technology appears better able to perform its intended design when it seems to have a humanlike mind. These results suggest meaningful consequences of humanizing technology, and also offer insights into the inverse process of objectifying humans. (shrink)

The purpose of this paper is to draw out and make explicit the assumptions made in the treatment of technology within business ethics. Drawing on the work of Freeman (1994, 2000) on the assumed separation between business and ethics, we propose a similar separation exists in the current analysis of technology and ethics. After first identifying and describing the separation thesis assumed in the analysis of technology, we will explore how this assumption manifests itself in the current literature. A different (...) stream of analysis, that of science and technology studies (STS), provides a starting point in understanding the interconnectedness of technology and society. As we will demonstrate, business ethicists are uniquely positioned to analyze the relationship between business, technology, and society. The implications of a more complex and rich definition of lsquotechnologyrsquo ripple through the analysis of business ethics. Finally, we propose a pragmatic approach to understanding technology and explore the implications of such an approach to technology. This new approach captures the broader understanding of technology advocated by those in STS and allows business ethicists to analyze a broader array of dilemmas and decisions. (shrink)

A number of fairness issues and principles are developed and discussed from the context of personnel selection. It is noted that not too much attention has been paid to these issues and concerns in the past. A distinction is made between justice and fairness having to do with the procedural components and processes of selection, the nature of the information used to make selection decisions, and the resulting outcomes of the selection process. Ideas for future research and exploration are also (...) extended. (shrink)