Specification, Application, and Operationalization of a Metamodel of Fairness

2025-11-14

AI Summary

This paper addresses the lack of a unified formal framework for fairness concepts—particularly equality and equity—by proposing the AR fairness metamodel: an abstract structural model that uniformly characterizes fairness scenarios and rigorously distinguishes, while enabling computable comparison of, core notions such as equal treatment/outcome versus equitable resource allocation. Methodologically, the metamodel is operationalized via the modular Tiles framework, supporting flexible definition, composition, and evaluation of fairness criteria across diverse application domains. Key contributions include: (1) the first formal metamodel explicitly capturing semantic distinctions among fairness concepts; (2) an open-source toolchain enabling reusable, extensible fairness modeling and empirical analysis; and (3) systematic instantiation and conceptual validation across multiple canonical scenarios, demonstrating both expressiveness and discriminative power in contrasting equality- and equity-oriented fairness specifications.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Game Theory and Economic Paradigms: Fair Division

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

This paper presents the AR fairness metamodel, aimed at formally representing, analyzing, and comparing fairness scenarios. The metamodel provides an abstract representation of fairness, enabling the formal definition of fairness notions. We instantiate the metamodel through several examples, with a particular focus on comparing the notions of equity and equality. We use the Tiles framework, which offers modular components that can be interconnected to represent various definitions of fairness. Its primary objective is to support the operationalization of AR-based fairness definitions in a range of scenarios, providing a robust method for defining, comparing, and evaluating fairness. Tiles has an open-source implementation for fairness modeling and evaluation.

Problem

• Formally representing and comparing diverse fairness scenarios
• Providing abstract framework to define fairness notions operationally
• Differentiating equity versus equality concepts through metamodel instantiation

Innovation

• AR metamodel formally represents fairness scenarios
• Tiles framework enables modular fairness definitions
• Open-source implementation supports fairness evaluation