Sebastian Stein

This chapter discusses the epistemological status of the knowledge claims Hegel makes in the _Philosophy of Right_. It distinguishes between empirical knowledge (EK), potentially conditioned knowledge (PCPK), and philosophical knowledge (PK), and argues that PK is immune to criticism based on EK and PCPK because it is ontological prior to them. From ‘our’, PCPK-style perspective, Hegel might have failed truthfully to express PK so for ‘us’, his claims as well as our own are open for revision by the always already present standard of PK. Insofar as Hegel failed to express PK and we succeed, true thought is manifest and recognizes itself in ‘our’ thinking. When this happens, PK replaces PCPK and the contradiction between PK and PCPK is avoided.

The first section analyzes Hegel’s notion of philosophy as an ideal, self-referential unity with its internal differentiation into Geist and nature, compares Hegel’s account of the relationship between truth and thinkers to those of his essentialist- naturalist predecessors Aristotle and Spinoza and identifies Hegel’s system’s unified but differentiated categorial determinations as an expression of the one concept’s self-developing singularity. This leads into the second section’s discussion of Hegel’s reasons for philosophically deducing the notion of philosophy and motivates the enquiry into how he argues that universal Geist’s unconditioned truth can be comprehended by particular, historically situated thinkers. The third and final section returns to the comparison between Hegel and his essentialist predecessors but now draws on the analysis of ‘the concept’ to describe absolute idealism in an attempt to answer to the chapter’s motivating question.

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.