Fuzzy Representation of Norms
Ziba Assadi, Paola Inverardi
TL;DR
The paper addresses encoding Social, Legal, Ethical, Empathetic, and Cultural (SLEEC) norms into autonomous systems under uncertainty. It proposes a fuzzy representation based on possibility theory with $Poss$ and test‑score semantics, replacing binary logic with explicit IF–THEN–ELSE rules and a center‑of‑gravity defuzzification pipeline that yields a numeric distress score $D^*$. Key contributions include a formalized SLEEC rule representation, a three‑stage fuzziness methodology (Preliminaries, Fuzzification, Defuzzification), and a case study applying the framework to a healthcare robotics scenario to resolve ethical dilemmas. The approach enables graded, runtime ethical decision‑making in autonomous systems and supports trustworthy human–robot interactions by handling imprecision and context dependence in normative reasoning.
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.