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

TL;DR

This paper explores how social, legal, ethical, empathetic, and cultural rules can be systematically formulated, translated into logic, and implemented in AI systems to ensure normative compliance.

Contribution

It provides a linguistic analysis of SLEEC rules, justifies their translation into classical logic, and demonstrates how logical programming can enable their practical application in AI.

Findings

SLEEC rules can be effectively translated into classical logic.

Logical programming frameworks support automated reasoning about SLEEC rules.

A practical strategy for implementing norm-compliant AI systems is proposed.

Abstract

The rise of AI-based and autonomous systems is raising concerns and apprehension due to potential negative repercussions stemming from their behavior or decisions. These systems must be designed to comply with the human contexts in which they will operate. To this extent, Townsend et al. (2022) introduce the concept of SLEEC (social, legal, ethical, empathetic, or cultural) rules that aim to facilitate the formulation, verification, and enforcement of the rules AI-based and autonomous systems should obey. They lay out a methodology to elicit them and to let philosophers, lawyers, domain experts, and others to formulate them in natural language. To enable their effective use in AI systems, it is necessary to translate these rules systematically into a formal language that supports automated reasoning. In this study, we first conduct a linguistic analysis of the SLEEC rules pattern, which justifies the translation of SLEEC rules into classical logic. Then we investigate the computational complexity of reasoning about SLEEC rules and show how logical programming frameworks can be employed to implement SLEEC rules in practical scenarios. The result is a readily applicable strategy for implementing AI systems that conform to norms expressed as SLEEC rules.