fEDM+: A Risk-Based Fuzzy Ethical Decision Making Framework with Principle-Level Explainability and Pluralistic Validation

2026-02-25

AI Summary

This work addresses critical gaps in existing AI ethics decision-making frameworks, particularly their limited interpretability of ethical principles and insufficient robustness in validating decisions across diverse value systems. To overcome these limitations, the paper proposes a novel risk-driven ethical decision-making framework that integrates fuzzy logic with fuzzy Petri nets. It introduces a pioneering method to quantitatively measure the contribution of moral principles to decision rules, while enabling interpretability through traceable reasoning paths and a multi-stakeholder semantic validation mechanism. This approach supports formal expression and verification across heterogeneous ethical systems, significantly enhancing explainability, context sensitivity, and ethical robustness without compromising formal verifiability. The framework is thus well-suited for transparent governance and auditing of high-stakes AI systems.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

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: Fairness, accountability, transparency and ethics of web technologies
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

In a previous work, we introduced the fuzzy Ethical Decision-Making framework (fEDM), a risk-based ethical reasoning architecture grounded in fuzzy logic. The original model combined a fuzzy Ethical Risk Assessment module (fERA) with ethical decision rules, enabled formal structural verification through Fuzzy Petri Nets (FPNs), and validated outputs against a single normative referent. Although this approach ensured formal soundness and decision consistency, it did not fully address two critical challenges: principled explainability of decisions and robustness under ethical pluralism. In this paper, we extend fEDM in two major directions. First, we introduce an Explainability and Traceability Module (ETM) that explicitly links each ethical decision rule to the underlying moral principles and computes a weighted principle-contribution profile for every recommended action. This enables transparent, auditable explanations that expose not only what decision was made but why, and on the basis of which principles. Second, we replace single-referent validation with a pluralistic semantic validation framework that evaluates decisions against multiple stakeholder referents, each encoding distinct principle priorities and risk tolerances. This shift allows principled disagreement to be formally represented rather than suppressed, thus increasing robustness and contextual sensitivity. The resulting extended fEDM, called fEDM+, preserves formal verifiability while achieving enhanced interpretability and stakeholder-aware validation, making it suitable as an oversight and governance layer for ethically sensitive AI systems.

Problem

• principled explainability
• ethical pluralism
• ethical decision-making
• risk-based reasoning
• stakeholder validation

Innovation

• Explainable AI
• Ethical Decision-Making
• Fuzzy Logic
• Pluralistic Validation
• Principle-Level Explainability