My aim in this article is twofold. First, I will illuminate the triangular conceptual connections between responsibility, authority, and power as they are exposed in the organizational realm; second, I will show how the three concepts are distinct. Relying on the work of Peter Strawson and his followers on responsibility for my point of departure, I will show that the connection between the inner corporational authority and its inner matching responsibility is different from the connection between the outer corporational forces (...) and influences and the CSR that they develop in reaction to these. This will expose another important distinction between two kinds of responsibility: the organizational kind, as instantiated in organizations, and the social kind, which constitutes the outer aspect of the CSR. Though many thinkers address different kinds of responsibility, a comparative perspective of these different concepts is missing. I attempt to bring three of these separate discourses together to examine them alongside one another, evaluating them in the light of their differences and similarities. This will expose a new typology of ‘responsibility’ that penetrates and illuminates the relations between corporations and society and as such enhance our understanding of organizational responsibility. (shrink)

The development of machine learning and deep learning (DL) in the field of AI (artificial intelligence) is the direct result of the advancement of nano-electronics. Machine learning is a function that provides the system with the capacity to learn from data without being programmed explicitly. It is basically a mathematical and probabilistic model. DL is part of machine learning methods based on artificial neural networks, simply called neural networks (NNs), as they are inspired by the biological NNs that constitute organic (...) brains. Despite its similarity to biological organs such as human brains, major problems arise in trying to attribute moral responsibility to autonomic systems based on hardware including nano-electronic devices, which are sought to replace humans (moral agents) in the context of AI. It is suggested that the required emotional environment which enables actions according to reasons in humans is not witnessed at AI devices. Though AI technology raises an enticing resemblance to human actions, this resemblance is to be considered with a skeptical eye. It is because actions are associated with reasons while causes are connected to operations. Human agents are capable of acting upon their reasons, while AI devices are limited only to operations as they are conditioned by their programming, which is considered as an embodiment of some causes. As moral responsibility goes hand in hand with the capacity to act (according to reasons), attributing moral responsibility to AI devices is revealed to be but a misleading metaphor. (shrink)

ABSTRACT: Frege, and others who followed him, stressed the role of fallibility as a means to defining ‘objectivity.’ By defining objective judgments as fallible, these philosophers contributed to the consolidation of a theory of objectivity which suggested interpreting epistemological, as well as other judgements, as being objective. An important philosophical implication of this theory lies in its disclosure of the interrelations between truth and objectivity. In light of this insight, and based on an analysis of instances of false (epistemological and (...) other) judgments, I show that truth and objectivity go hand-in-hand, while falsity and objectivity do not. This finding alone indicates the necessity to revise the theory of objectivity. (shrink)