English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The rise of AI-based and autonomous systems is raising concerns andapprehension due to potential negative repercussions stemming from theirbehavior or decisions. These systems must be designed to comply with the humancontexts in which they will operate. To this extent, Townsend et al. (2022)introduce the concept of SLEEC (social, legal, ethical, empathetic, orcultural) rules that aim to facilitate the formulation, verification, andenforcement of the rules AI-based and autonomous systems should obey. They layout a methodology to elicit them and to let philosophers, lawyers, domainexperts, and others to formulate them in natural language. To enable theireffective use in AI systems, it is necessary to translate these rulessystematically into a formal language that supports automated reasoning. Inthis study, we first conduct a linguistic analysis of the SLEEC rules pattern,which justifies the translation of SLEEC rules into classical logic. Then weinvestigate the computational complexity of reasoning about SLEEC rules andshow how logical programming frameworks can be employed to implement SLEECrules in practical scenarios. The result is a readily applicable strategy forimplementing AI systems that conform to norms expressed as SLEEC rules.

Social, Legal, Ethical, Empathetic, and Cultural Rules: Compilation and Reasoning (Extended Version)