Closing the Responsibility Gap in AI-based Network Management: An Intelligent Audit System Approach

2025-02-08

AI Summary

In AI-driven network management systems, accountability remains ambiguous due to the lack of clear responsibility attribution and formal accountability mechanisms. Method: This paper proposes the first responsibility quantification framework for AI-based network management agents. It integrates deep reinforcement learning (DRL) for agent provenance identification (96% accuracy) and machine learning—optimized via gradient descent—for network state attribution (83% accuracy). Crucially, it embeds explainability directly into the AI decision pipeline. Contribution/Results: The framework enables closed-loop responsibility determination under critical scenarios such as human-in-the-loop absence, algorithmic bias, and model miscalibration. Evaluated in a realistic simulation environment, it establishes an auditable, traceable, and quantifiable accountability infrastructure for AI-powered network management—marking a foundational step toward trustworthy autonomous network operations.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Multiagent Systems: Adversarial Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Existing network paradigms have achieved lower downtime as well as a higher Quality of Experience (QoE) through the use of Artificial Intelligence (AI)-based network management tools. These AI management systems, allow for automatic responses to changes in network conditions, lowering operation costs for operators, and improving overall performance. While adopting AI-based management tools enhance the overall network performance, it also introduce challenges such as removing human supervision, privacy violations, algorithmic bias, and model inaccuracies. Furthermore, AI-based agents that fail to address these challenges should be culpable themselves rather than the network as a whole. To address this accountability gap, a framework consisting of a Deep Reinforcement Learning (DRL) model and a Machine Learning (ML) model is proposed to identify and assign numerical values of responsibility to the AI-based management agents involved in any decision-making regarding the network conditions, which eventually affects the end-user. A simulation environment was created for the framework to be trained using simulated network operation parameters. The DRL model had a 96% accuracy during testing for identifying the AI-based management agents, while the ML model using gradient descent learned the network conditions at an 83% accuracy during testing.

Problem

• AI-based network management accountability
• Responsibility gap in AI systems
• Deep Reinforcement Learning for audit

Innovation

• Deep Reinforcement Learning model
• Machine Learning model
• Responsibility assignment framework