Fuzzy Representation of Norms

2026-01-06

arXiv.org

AI Summary

This work addresses the critical challenge in AI governance of effectively modeling and embedding ethical, legal, and social norms into autonomous systems to navigate complex moral dilemmas. It proposes SLEEC—a formal framework integrating fuzzy logic with test-score semantics to represent Social, Legal, Ethical, Empathic, and Cultural norms. By introducing fuzzy reasoning into multidimensional norm modeling for the first time, the approach conceptualizes ethics as a space of possibilities, enabling flexible handling of uncertainty and normative conflicts. Case studies demonstrate that this method empowers autonomous systems to make norm-compliant ethical decisions in complex scenarios, offering a novel formal pathway toward trustworthy AI.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• ethical norms
• autonomous systems
• SLEEC rules
• fuzzy representation
• normative reasoning

Innovation

• fuzzy logic
• SLEEC rules
• ethical reasoning
• test-score semantics
• autonomous systems