Sebastian Stein

Despite his commitment to universal explicability, a case can be made that Hegel is better labelled an idealist than a naturalist. As an analysis of his three syllogisms of philosophy reveals, he strictly differentiates between the domains of nature and Geist, suggesting in sequence that Geist replaces nature, Geist comprehends nature and that Geist and nature are comprehended as forms of the metaphysical idea and determine and mediate each other. Since Hegel grounds his accounts of the metaphysical idea and its forms nature and Geist in concept-metaphysics and these include a supernatural notion of undetermined, self-positing universality, Hegel’s entire ontological edifice is ultimately supernatural. This stands in stark contrast to the metaphysics of naturalism, which are based on categories Hegel associates with the logics of being and essence or which fail to sufficiently emancipate universality from its immediate connection to particularity.

Hegel's absolute idealism has proven to be one of the most controversial philosophical positions to characterize. The most abstract categories of essence are what Hegel calls the determinations of reflection, i.e. identity, difference and ground. Continuing his analysis of the determinations of essence, Hegel then discusses the notions of subsistence, relation and the whole and its parts and arrives at essence's determinations of “inner” and outer: the “inner” functions as ground of appearance and opposes the externality of reflection‐into‐other. In actuality, there is no difference between ground and existence, between existence and essence: actuality is what it is, its existence is its essence, its essence its existence. Hegel sets out to show that the ultimate description of truth ‐ i.e. the concept ‐ creates, undermines, incorporates, and improves on any other relevant definition of truth. To achieve this, Hegel divides the Logic's description of metaphysical truth into three parts: Being, Essence, and the Concept.

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.