We review and synthesize over two decades of research on ethical culture in organizations, examining eighty-nine relevant scholarly works. Our article discusses the conceptualization of ethical culture in a cross-disciplinary space and its critical role in ethical decision-making. With a view to advancing future research, we analyze the antecedents, outcomes, and mediator and moderator roles of ethical culture. To do so, we identify measures and theories used in past studies and make recommendations. We propose, inter alia, the use of validated (...) measures, application of a wider range of theories, adoption of longitudinal studies, and study of group-level data in organizations. We explore research possibilities in new and emergent forms of organizations, ways of organizing work, and technology in ethical decision-making, such as the role of artificial intelligence. We also recommend the study of a broad range of leadership styles and their influence in shaping ethical cultures in organizations. (shrink)

Contemporary ethical analysis of Artificial Intelligence is growing rapidly. One of its most recognizable outcomes is the publication of a number of ethics guidelines that, intended to guide governmental policy, address issues raised by AI design, development, and implementation and generally present a set of recommendations. Here we propose two things: first, regarding content, since some of the applied issues raised by AI are related to fundamental questions about topics like intelligence, consciousness, and the ontological and ethical status of humans, (...) among others, the treatment of these issues would benefit from interfacing with neuroethics that has been addressing those same issues in the context of brain research. Second, the identification and management of some of the practical ethical challenges raised by AI would be enriched by embracing the methodological resources used in neuroethics. In particular, we focus on the methodological distinction between conceptual and action-oriented neuroethical approaches. We argue that the normative discussion about AI will benefit from further integration of conceptual analysis, including analysis of some operative assumptions, their meaning in different contexts, and their mutual relevance in order to avoid misplaced or disproportionate concerns and achieve a more realistic and useful approach to identifying and managing the emerging ethical issues. (shrink)

Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of (...) automation. In this article, we consider the feasibility and efficacy of ethics-based auditing as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS. (shrink)

This article argues in defence of human–robot friendship. I begin by outlining the standard Aristotelian view of friendship, according to which there are certain necessary conditions which x must meet in order to ‘be a friend’. I explain how the current literature typically uses this Aristotelian view to object to human–robot friendships on theoretical and ethical grounds. Theoretically, a robot cannot be our friend because it cannot meet the requisite necessary conditions for friendship. Ethically, human–robot friendships are wrong because they (...) are deceptive, and could also make it more likely that we will favour ‘perfect’ robots, and disrespect, exploit, or exclude other human beings. To argue against the above position, I begin by outlining and assessing current attempts to reject the theoretical argument—that we cannot befriend robots. I argue that the current attempts are problematic, and do little to support the claim that we can be friends with robots now. I then use the standard Aristotelian view as a touchstone to develop a new degrees-of-friendship view. On my view, it is theoretically possible for humans to have some degree of friendship with social robots now. I explain how my view avoids ethical concerns about human–robot friendships being deceptive, and/or leading to the disrespect, exploitation, or exclusion of other human beings. (shrink)

AI research is growing rapidly raising various ethical issues related to safety, risks, and other effects widely discussed in the literature. We believe that in order to adequately address those issues and engage in a productive normative discussion it is necessary to examine key concepts and categories. One such category is anthropomorphism. It is a well-known fact that AI’s functionalities and innovations are often anthropomorphized (i.e., described and conceived as characterized by human traits). The general public’s anthropomorphic attitudes and some (...) of their ethical consequences (particularly in the context of social robots and their interaction with humans) have been widely discussed in the literature. However, how anthropomorphism permeates AI research itself (i.e., in the very language of computer scientists, designers, and programmers), and what the epistemological and ethical consequences of this might be have received less attention. In this paper we explore this issue. We first set the methodological/theoretical stage, making a distinction between a normative and a conceptual approach to the issues. Next, after a brief analysis of anthropomorphism and its manifestations in the public, we explore its presence within AI research with a particular focus on brain-inspired AI. Finally, on the basis of our analysis, we identify some potential epistemological and ethical consequences of the use of anthropomorphic language and discourse within the AI research community, thus reinforcing the need of complementing the practical with a conceptual analysis. (shrink)

Most philosophical accounts of human rights accept that all persons have human rights. Typically, ‘personhood’ is understood as unitary and binary. It is unitary because there is generally supposed to be a single threshold property required for personhood. It is binary because it is all-or-nothing: you are either a person or you are not. A difficulty with binary views is that there will typically be subjects, like children and those with dementia, who do not meet the threshold, and so who (...) are not persons with human rights, on these accounts. It is consequently unclear how we ought to treat these subjects. This is the problem of marginal cases. I argue that we cannot resolve the problem of marginal cases if we accept a unitary, binary view of personhood. Instead, I develop a new non-binary personhood account of human rights, and defend two main claims. First, there are many, scalar properties, the having of which are conducive to personhood. Second, different subjects have different human rights depending on which of these properties they have, and what threats apply to them. On my view, and contra most existing accounts, most marginal cases have some degree of personhood and are entitled to some human rights. (shrink)

This article defends two claims. First, humans could be in relationships characterised by hate with some robots. Second, it matters that humans could hate robots, as this hate could wrong the robots (by leaving them at risk of mistreatment, exploitation, etc.). In defending this second claim, I will thus be accepting that morally considerable robots either currently exist, or will exist in the near future, and so it can matter (morally speaking) how we treat these robots. The arguments presented in (...) this article make an important original contribution to the robo-philosophy literature, and particularly the literature on human–robot relationships (which typically only consider positive relationship types, e.g., love, friendship, etc.). Additionally, as explained at the end of the article, my discussions of robot hate could also have notable consequences for the emerging robot rights movement. Specifically, I argue that understanding human–robot relationships characterised by hate could actually help theorists argue for the rights of robots. (shrink)

Why do organizations conduct job interviews? The traditional view of interviewing holds that interviews are conducted, despite their steep costs, to predict a candidate’s future performance and fit. This view faces a twofold threat: the behavioral and algorithmic threats. Specifically, an overwhelming body of behavioral research suggests that we are bad at predicting performance and fit; furthermore, algorithms are already better than us at making these predictions in various domains. If the traditional view captures the whole story, then interviews seem (...) to be a costly, archaic human resources procedure sustained by managerial overconfidence. However, building on T. M. Scanlon’s work, we offer the value of choice theory of interviewing and argue that interviews can be vindicated once we recognize that they generate commonly overlooked kinds of noninstrumental value. On our view, interviews should thus not be entirely replaced by algorithms, however sophisticated algorithms ultimately become at predicting performance and fit. (shrink)