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)

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)

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)

Proper linear models are those in which predictor variables are given weights such that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in P. Meehl's book on clinical vs statistical prediction and research stimulated in part by that book indicate that when a numerical criterion variable is to be predicted from numerical predictor variables, proper linear models outperform clinical intuition. Improper (...) linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method. The present article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit weighting is quite robust for making such predictions. The application of unit weights to decide what bullet the Denver Police Department should use is described; some technical, psychological, and ethical resistances to using linear models in making social decisions are considered; and arguments that could weaken these resistances are presented. (shrink)

The notion of dignity as that which has intrinsic value has arguably been neglected in economics and management despite its societal importance and eminent relevance in other social sciences. While management theory gained parsimony, this paper argues that the inclusion of dignity in the theoretical precepts of management theory will: improve management theory in general, align it more directly with the public interest, and strengthen its connection to social welfare creation. The paper outlines the notion of dignity, discusses its historical (...) understanding, and explains its relevance in the context of management theory. Furthermore, it proposes a framework of paradigmatic assumptions along two dimensions: understanding human dignity as unconditional or conditional and understanding social welfare as wealth creation or well-being creation. I propose alternative management theory archetypes and discuss these archetypes’ theoretical implications for management research. I also suggest how management theory can be shifted to contribute toward social welfare creation more directly. (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)

Firms use algorithms to make important business decisions. To date, the algorithmic accountability literature has elided a fundamentally empirical question important to business ethics and management: Under what circumstances, if any, are algorithmic decision-making systems considered legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the impact of decision importance, governance, outcomes, and data inputs on perceptions of the legitimacy of algorithmic decisions made by firms. We find that many of the procedural governance (...) mechanisms in practice today, such as notices and impact statements, do not lead to algorithmic decisions being perceived as more legitimate in general, and, consistent with legitimacy theory, that algorithmic decisions with good outcomes are perceived as more legitimate than bad outcomes. Yet, robust governance, such as offering an appeal process, can create a legitimacy dividend for decisions with bad outcomes. However, when arbitrary or morally dubious factors are used to make decisions, most legitimacy dividends are erased. In other words, companies cannot overcome the legitimacy penalty of using arbitrary or morally dubious factors, such as race or the day of the week, with a good outcome or an appeal process for individuals. These findings add new perspectives to both the literature on legitimacy and policy discussions on algorithmic decision-making in firms. (shrink)

This paper focuses on the potential of “equitech”—AI technology that improves equity. Recently, interventions have been developed to reduce the harm of implicit bias, the automatic form of stereotype or prejudice that contributes to injustice. However, these interventions—some of which are assisted by AI-related technology—have significant limitations, including unintended negative consequences and general inefficacy. To overcome these limitations, we propose a two-dimensional framework to assess current AI-assisted interventions and explore promising new ones. We begin by using the case of human (...) resource recruitment as a focal point to show that existing approaches have exploited only a subset of the available solution space. We then demonstrate how our framework facilitates the discovery of new approaches. The first dimension of this framework helps us systematically consider the analytic information, intervention implementation, and modes of human-machine interaction made available by advancements in AI-related technology. The second dimension enables the identification and incorporation of insights from recent research on implicit bias intervention. We argue that a design strategy that combines complementary interventions can further enhance the effectiveness of interventions by targeting the various interacting cognitive systems that underlie implicit bias. We end with a discussion of how our cognitive interventions framework can have positive downstream effects for structural problems. (shrink)

Decades of empirical and theoretical research has produced an extensive literature on the ethical judgments construct. Given its importance to understanding people’s ethical choices, future research should explore the psychological processes that produce ethical judgments. In this paper, the authors discuss two steps needed to advance this effort. First, they note that the business ethics literature lacks a single, generally accepted definition of ethical judgments. After reviewing several extant definitions, the authors offer a definition of the construct and discuss its (...) advantages. Second, future ethical judgment research would benefit from greater integration between theories of ethical decision making and theories of social cognition. Drawing upon the Hunt–Vitell ( Journal of Macromarketing 6 (Spring), 5–15, 1986 ; In: N. C. Smith and J. A. Quelch (eds.), Ethics in Marketing . Irwin, Homewood, IL, pp. 775–784, 1992 ) model and the heuristic-systematic model (Chaiken, Journal of Personality and Social Psychology 39 (November), 752–766, 1980 ), the authors present a brief research agenda intended to stimulate research on the psychological processes behind ethical judgments. (shrink)

In “Sweatshops and Respect for Persons” we argued on Kantian grounds that managers of multinational enterprises (MNEs) have the following duties: to adhere to local labor laws, to refrain from coercion, to meet minimum health and safety standards, and to pay workers a living wage. In their commentary on our paper Sollars and Englander challenge some of our conclusions. We argue here that several of their criticisms are based on an inaccurate reading of our paper, and that none of the (...) remaining criticisms successfully challenge our main arguments. By highlighting the shortcomings of their arguments we hope to advance discussion of the ethical treatment of workers in global supply chains. (shrink)

Quality is commonly applied to products and processes, but we can also define human quality in dealing with people. This requires first establishing what treatment is appropriate to the human condition. Through an inquiry into the characteristics that define the human being and what ethical requirements constitute a good treatment, we define “Human Quality Treatment” as dealing with persons in a way appropriate to the human condition, which entails acting with respect for their human dignity and rights, caring for their (...) problems and legitimate interests, and fostering their personal development. With this in mind, we can distinguish between five different levels or degrees of HQT in organizations which can be characterized by the following terms: maltreatment, indifference, justice, care, and development. (shrink)

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)

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)

Literature on consumers’ ethical decision making is rooted in a rationalist perspective that emphasizes the role of moral reasoning. However, the view of ethical consumption as a thorough rational and conscious process fails to capture important elements of human cognition, such as emotions and intuitions. Based on moral psychology and microsociology, this paper proposes a holistic and integrated framework showing how emotive and intuitive information processing may foster ethical consumption at individual and social levels. The model builds on social intuitionism (...) to show how consumers’ a priori affect-laden intuitive moral judgments impact their post hoc reflective moral reasoning. Symbolic interactionism is used to interpret consumers as interdependent and socially embedded agents that self-construct their social identity through interactions with other consumers. The proposed social intuitionist framework of consumers’ ethical decision making shows that other-oriented moral emotions—such as elevation, gratitude, and empathy—interact with persuasion and social influence in ethical consumption. Consequently, moral emotions and intuition drive interpersonal persuasion among ethical consumers. Theoretical propositions and implications for consumer ethics theory and practice are discussed. (shrink)

This paper explores how consumers perceive retailer ethics. Based on a review of the marketing and consumer research literature, we conceptualize consumer perceptions of the ethics of retailers as a multidimensional construct and propose that its effects on consumer purchase behavior and word-of-mouth communication are more salient when consumers have strong rather than weak ethical beliefs. The model was validated using a random sample of 399 respondents in a collectivist society. The results of structural equation modeling confirmed that CPER is (...) a second-order construct comprising product fairness, price fairness, non-deception, fair trade, and green products. CPER positively predicted consumer purchase behavior and word-of-mouth communication. Moreover, ethical beliefs moderated the positive relationship between CPER and the word-of-mouth communication of consumers with strong ethical beliefs but did not moderate the relationship between CPER and purchase behavior. The implications of the findings are discussed. (shrink)

This research examines the impact of an online platform’s revenue model on consumers’ ethical inferences of the company. We demonstrate that consumers perceive online platforms that employ the advertising-based revenue model to be less ethical than platforms that employ the service-fee-based revenue model because platforms that adopt the advertising-based revenue model are thought to be less consumer-serving motivated. The unfavorable ethical inferences induced by the advertising-based revenue model further lower consumers’ intention to disclose personal information and the intention to generate (...) positive word of mouth for the company. We further find that allowing consumers to skip advertisements does not improve the less favorable ethical inferences while using resources for public-service advertisements does. These findings advance the understanding of consumers’ ethical inferences regarding business models and provide insights for managers to develop effective strategies to alleviate less favorable ethical inferences. (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)

Necro-advertising refers to the use of deceased celebrities in advertising. This practice offers unique advantages to brands that seek to benefit from positive associations with timeless celebrities at a more affordable cost than celebrity endorsement. Nevertheless, how consumers actually respond to the use of deceased celebrities in advertising remains under-theorized. This research is the first to empirically examine consumers’ ethical judgments about necro-advertising practices. In particular, drawing from the signaling theory, it demonstrates the impact of consumer inferences about the existence (...) of a legal agreement for using deceased celebrities’ images on brand ethicality. The results of two experimental studies show that a low-equity brand is more likely to be perceived as unethical when using necro-advertising since consumers have limited knowledge about these brands. Conversely, our findings confirm how a high level of equity prevents from the aforementioned adverse effects since these brands’ assets send a credible signal about their capability to get approval from a deceased celebrity’s estate for the use of its image. While deepening current knowledge on the perceived ethicality of necro-advertising practices, this research uncovers the moderating effect of equity on consumer inferences about brand ethicality. The results also suggest managerial caveats and guidelines for low-equity brands. In particular, while necro-advertising may have a negative impact on the perceived ethicality of low-equity brands, disclosing a statement about a contractual engagement with the deceased celebrity’s estate can mitigate this negative effect by providing an unequivocal signal that the brand is acting ethically. (shrink)

Based on three empirical studies, this research sets out to conceptualise and subsequently operationalise the construct of consumer perceived ethicality (CPE) of a company or brand. Study 1 investigates consumer meanings of the term ethical and reveals that, contrary to philosophical scholars' exclusively consequentialist or nonconsequentialist positions, consumers' ethical judgments are a function of both these evaluation principles, illustrating that not any one scholarly definition of ethics alone is capable of capturing the content domain. The resulting conceptualisation identifies six key (...) themes explicating the construct. Building upon these findings, studies 2 and 3 were conducted to operationalise CPE. Such operationalisation is an essential prerequisite for future explorations and theory development given the absence of a suitable tool to capture and quantify the strength and direction of CPE. The key focus was on developing a valid and reliable multi-item measurement tool that is practical, parsimonious and easy to administer. The scale's general applicability allows deployment in academic and business contexts as well as different research areas and doing thus facilitates the much-needed theory building in this new research area. (shrink)