Francesco Franda

Proponents of technical monism argue that artistic functions are technical functions, and that therefore artworks and technical artifacts may be unified under the same theoretical framework. We hold that this view fails because it does not account for the phenomenon of technical malfunction: if artistic functions were technical functions, then artistic malfunctions would be technical malfunctions too. We argue that artistic malfunctions are not technical malfunctions because they cannot meet all the following four criteria: (1) malfunctioning attribution to a technical artifact entails negative evaluation and a reason to avoid use; (2) technical malfunction is caused by a failure of the artifact itself rather than failing circumstances; (3) a malfunctioning technical artifact remains a member of its artifactual kind; (4) malfunctioning technical artifacts are sometimes repaired. Since artistic malfunction cannot meet all these conditions, we claim that artworks and technical artifacts do not share the same type of function and, as such, unification theories based on technical function are untenable. We conclude by suggesting that our account raises broader challenges not only for technical-function-based unification theories, but also for other theories that seek to unify artworks and technical artifacts through a shared notion of function, as well as for accounts of artifact function more broadly.

The explosion of interest in large language models (LLMs) has been accompanied by concerns over the extent to which generated outputs can be trusted, owing to the prevalence of bias, hallucinations, and so forth. Accordingly, there is a growing interest in the use of ontologies and knowledge graphs to make LLMs more trustworthy. This rests on the long history of ontologies and knowledge graphs in constructing human-comprehensible justification for model outputs as well as traceability concerning the impact of evidence on other evidence. Understanding the nature of arguments and argumentation is critical to each, especially when LLM output conflicts with what is expected by users. The central contribution of this article is to extend the Arguments Ontology (ARGO) - an ontology specific to the domain of argumentation and evidence broadly construed - into the space of LLM fact-checking in the interest of promoting justification and traceability research through the use of ARGO-based ‘blueprints’.