Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. First, it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object (ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and description of relevant (...) works is presented. Third, applying the new taxonomy to the selected works, dominant research patterns, and lessons for the field are identified, and future directions for research are suggested. (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)

Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, (...) I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (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)

The combination of increased availability of large amounts of fine-grained human behavioral data and advances in machine learning is presiding over a growing reliance on algorithms to address complex societal problems. Algorithmic decision-making processes might lead to more objective and thus potentially fairer decisions than those made by humans who may be influenced by greed, prejudice, fatigue, or hunger. However, algorithmic decision-making has been criticized for its potential to enhance discrimination, information and power asymmetry, and opacity. In this paper, we (...) provide an overview of available technical solutions to enhance fairness, accountability, and transparency in algorithmic decision-making. We also highlight the criticality and urgency to engage multi-disciplinary teams of researchers, practitioners, policy-makers, and citizens to co-develop, deploy, and evaluate in the real-world algorithmic decision-making processes designed to maximize fairness and transparency. In doing so, we describe the Open Algortihms project as a step towards realizing the vision of a world where data and algorithms are used as lenses and levers in support of democracy and development. (shrink)

Recent scholarship in philosophy, law, and information systems suggests that respecting privacy entails understanding the implicit privacy norms about what, why, and to whom information is shared within specific relationships. These social contracts are important to understand if firms are to adequately manage the privacy expectations of stakeholders. This paper explores a social contract approach to developing, acknowledging, and protecting privacy norms within specific contexts. While privacy as a social contract—a mutually beneficial agreement within a community about sharing and using (...) information—has been introduced theoretically and empirically, the full impact on firms of an alternative framework to respecting the privacy expectations of stakeholders has not been examined. The goal of this paper is to examine how privacy norms develop through social contract’s narrative, to redescribe privacy violations given the social contract approach, and to critically examine the role of business as a contractor in developing privacy norms. A social contract narrative dealing specifically with issues of privacy is an important next step in exploring a social contract approach to privacy. Here, the narrative is used to explain to analyze the dynamic process of privacy norm generation within particular communities. Based on this narrative, individuals within a given community discriminately share information with a particular set of obligations in mind as to who has access to the information and how it will be used. Rather than giving away privacy, individuals discriminately share information within a particular community and with norms governing the use of their information. Similar to contractual business ethics’ impact on global commerce in explaining how and why norms vary across global contexts, the social contract approach to privacy explains how and why norms vary across communities of actors. Focusing on agreements around privacy expectations shifts the responsibility of firms from adequate notification to the responsibility of firms as contractors to maintain a mutually beneficial and sustainable solution. (shrink)

Firms increasingly deploy algorithmic pricing approaches to determine what to charge for their goods and services. Algorithmic pricing can discriminate prices both dynamically over time and personally depending on individual consumer information. Although legal, the ethicality of such approaches needs to be examined as often they trigger moral concerns and sometimes outrage. In this research paper, we provide an overview and discussion of the ethical challenges germane to algorithmic pricing. As a basis for our discussion, we perform a systematic interpretative (...) review of 315 related articles on dynamic and personalized pricing as well as pricing algorithms in general. We then use this review to define the term algorithmic pricing and map its key elements at the micro-, meso-, and macro levels from a business and marketing ethics perspective. Thus, we can identify morally ambivalent topics that call for deeper exploration by future research. (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)

Biometric technology was once the purview of security, with face recognition and fingerprint scans used for identification and law enforcement. This is no longer the case; biometrics is increasingly used for commercial and civil applications. Due to the widespread diffusion of biometrics, it is important to address the ethical issues inherent to the development and deployment of the technology. This article explores the burgeoning research on biometrics for non-security purposes and the ethical implications for organizations. This will be achieved by (...) reviewing the literature on biometrics and business ethics and drawing from disciplines such as computer ethics to inform a more robust discussion of key themes. Although there are many ethical concerns, privacy is the key issue, with associated themes. These include definitions of privacy, the privacy paradox, informed consent, regulatory frameworks and guidelines, and discrimination. Despite the proliferation of biometric technology, there is little empirical research on applied biometrics and business ethics. As such, there are several avenues for research to improve understanding of the ethical implications of using this technology. (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)

Codes of ethics are being increasingly adopted in organizations worldwide, yet their effects on employee perceptions and behavior have not been thoroughly addressed. This study used a sample of 613 management accountants drawn from the United States to study the relationship between corporate and professional codes of ethics and employee attitudes and behaviors. The presence of corporate codes of ethics was associated with less perceived wrongdoing in organizations, but not with an increased propensity to report observed unethical behavior. Further, organizations (...) that adopted formal codes of ethics exhibited value orientations that went beyond financial performance to include responsibility to the commonweal. In contrast to corporate codes of ethics, professional codes of ethical conduct had no influence on perceived wrongdoing in organization nor these codes affect the propensity to report observed unethical activities. (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)

The present study aimed to integrate findings from technology acceptance research with research on applicant reactions to new technology for the emerging selection procedure of asynchronous video interviewing. One hundred six volunteers experienced asynchronous video interviewing and filled out several questionnaires including one on the applicants’ personalities. In line with previous technology acceptance research, the data revealed that perceived usefulness and perceived ease of use predicted attitudes toward asynchronous video interviewing. Furthermore, openness revealed to moderate the relation between perceived usefulness (...) and attitudes toward this particular selection technology. No significant effects revealed for computer self-efficacy, job interview self-efficacy, extraversion, neuroticism, and conscientiousness. Theoretical and practical implications are discussed. (shrink)