ABSTRACT:Scholars studying organizational justice have been slow to incorporate insights from behavioral ethics research, despite the fields’ conceptual affinities. We maintain that this stems from differences in the paradigmatic approaches taken by scholars in each area. First, justice research historically has assumed that individuals are motivated by a desire for instrumental control of worthwhile outcomes or by a concern with social status, while behavioral ethics has paid more attention to the role of internalized moral convictions and duties. Second, organizational justice (...) researchers have investigated one set of individual differences, behavioral researchers have examined another. Third, justice scholars focus on social identities while behavioral ethics scholars also investigate moral identities. As an impetus to future inquiry, our review attends to contemporary organizational justice research that takes into account concepts derived from behavioral ethics. In so doing we hope to highlight an avenue for integrative scholarship that will further our understanding of organizational justice. (shrink)

Ensuring the trustworthiness of autonomous systems and artificial intelligence is an important interdisciplinary endeavour. In this position paper, we argue that this endeavour will benefit from technical advancements in capturing various forms of responsibility, and we present a comprehensive research agenda to achieve this. In particular, we argue that ensuring the reliability of autonomous system can take advantage of technical approaches for quantifying degrees of responsibility and for coordinating tasks based on that. Moreover, we deem that, in certifying the legality (...) of an AI system, formal and computationally implementable notions of _responsibility_, _blame_, _accountability_, and _liability_ are applicable for addressing potential responsibility gaps (i.e. situations in which a group is responsible, but individuals’ responsibility may be unclear). This is a call to enable AI systems themselves, as well as those involved in the design, monitoring, and governance of AI systems, to represent and reason about who can be seen as responsible in prospect (e.g. for completing a task in future) and who can be seen as responsible retrospectively (e.g. for a failure that has already occurred). To that end, in this work, we show that across all stages of the design, development, and deployment of trustworthy autonomous systems (TAS), responsibility reasoning should play a key role. This position paper is the first step towards establishing a road map and research agenda on how the notion of responsibility can provide novel solution concepts for ensuring the _reliability_ and _legality_ of TAS and, as a result, enables an effective embedding of AI technologies into society. (shrink)

La biologie expérimentale et causale a pour tâche d’apprendre à connaître le genre de rapports qui existent entre toutes les parties de l'organisme. A côté de la reproductibilité des phénomenes de détail dépendant de causes, il у a la reproduction naturelle de l’événement de la vie dans sa totalité. La reproductibilité du résultat de causes nombreuses suppose, comme donné, l’ordre ou la structure de ces causes. La biologie est, en première ligne, la science de ces structures causales. L’on peut montrer (...) diverses sortes de formes de ces structures. Il n’y a pas d’explication causale de la vie ; eile est seulement décrite comme ensemble de rapports. La biologie n’est pas science des effets de causes ordonnées ; elle est science de la manière d’être ou de l’origine de ces causes. (shrink)