The Landscape of Fairness: An Axiomatic and Predictive Framework for Network QoE Sensitivity

2025-09-10

AI Summary

Network fairness assessment faces fundamental challenges due to its non-stationarity and high sensitivity to SLA parameters. To address this, we propose the first axiomatic framework for QoE fairness sensitivity analysis, grounded in a rigorously derived QoE-Imbalance metric and a covariance-based analytical model of fairness gradients. We introduce the first global fairness phase diagram—revealing critical topological structures including stability bands and danger wedges—and derive a topology-aware “threshold-first” optimization strategy. Integrating information theory, axiomatic modeling, closed-form covariance derivation, and curvature analysis, our framework enables interpretable mapping and navigable exploration of the fairness sensitivity landscape. This advances fairness from an empirical metric to a predictable, designable engineering discipline, significantly enhancing network resilience and fairness assurance in complex service environments.

Technology Category

• Game Theory and Economic Paradigms: Fair Division
• Search and Optimization: Evaluation and Analysis
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies

Abstract

Evaluating network-wide fairness is challenging because it is not a static property but one highly sensitive to Service Level Agreement (SLA) parameters. This paper introduces a complete analytical framework to transform fairness evaluation from a single-point measurement into a proactive engineering discipline centered on a predictable sensitivity landscape. Our framework is built upon a QoE-Imbalance metric whose form is not an ad-hoc choice, but is uniquely determined by a set of fundamental axioms of fairness, ensuring its theoretical soundness. To navigate the fairness landscape across the full spectrum of service demands, we first derive a closed-form covariance rule. This rule provides an interpretable, local compass, expressing the fairness gradient as the covariance between a path's information-theoretic importance and its parameter sensitivity. We then construct phase diagrams to map the global landscape, revealing critical topological features such as robust "stable belts" and high-risk "dangerous wedges". Finally, an analysis of the landscape's curvature yields actionable, topology-aware design rules, including an optimal "Threshold-First" tuning strategy. Ultimately, our framework provides the tools to map, interpret, and navigate the landscape of system sensitivity, enabling the design of more robust and resilient networks.

Problem

• Develops a predictive framework for network-wide fairness evaluation
• Proposes an axiomatic QoE-Imbalance metric for theoretical soundness
• Maps sensitivity landscape to identify robust and high-risk regions

Innovation

• Axiomatic QoE-Imbalance metric for fairness evaluation
• Closed-form covariance rule as interpretable gradient compass
• Phase diagrams mapping stable belts and dangerous wedges