Fuzzy Representation of Norms

TL;DR

This paper introduces a fuzzy logic-based method for representing and embedding ethical norms, specifically SLEEC rules, into autonomous systems to handle ethical dilemmas more effectively.

Contribution

It proposes a novel logical representation of SLEEC ethical rules using fuzzy logic and test-score semantics, enabling better ethical decision-making in AI systems.

Findings

Fuzzy logic effectively models ethical norms as domains of possibilities.

The methodology allows embedding ethical requirements into autonomous systems.

Case study demonstrates practical application of the approach.

Abstract

Autonomous systems (AS) powered by AI components are increasingly integrated into the fabric of our daily lives and society, raising concerns about their ethical and social impact. To be considered trustworthy, AS must adhere to ethical principles and values. This has led to significant research on the identification and incorporation of ethical requirements in AS system design. A recent development in this area is the introduction of SLEEC (Social, Legal, Ethical, Empathetic, and Cultural) rules, which provide a comprehensive framework for representing ethical and other normative considerations. This paper proposes a logical representation of SLEEC rules and presents a methodology to embed these ethical requirements using test-score semantics and fuzzy logic. The use of fuzzy logic is motivated by the view of ethics as a domain of possibilities, which allows the resolution of ethical dilemmas that AI systems may encounter. The proposed approach is illustrated through a case study.