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

TL;DR

This paper proposes an accountability framework using deep reinforcement learning and machine learning to assign responsibility to AI agents in network management, aiming to address ethical and operational challenges.

Contribution

It introduces a novel framework combining DRL and ML models to identify and assign responsibility to AI agents in network management decisions.

Findings

DRL model achieved 96% accuracy in identifying AI agents.

ML model learned network conditions with 83% accuracy.

Framework enhances accountability in AI-driven network management.

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.