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

arXiv: https://arxiv.org/abs/2502.05608 · HTML: https://mlsanity.com/paper/2502.05608/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [Who Followed the Blueprint? Analyzing the Responses of U.S. Federal Agencies to the Blueprint for an AI Bill of Rights](https://mlsanity.com/paper/2404.19076/) (2024)
- [Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks](https://mlsanity.com/paper/2307.11468/) (2023)

## Built on by (most cited first, 1 of 1)

- [Hybrid Responsible AI-Stochastic Approach for SLA Compliance in Multivendor 6G Networks](https://mlsanity.com/paper/2602.09841/) (2026)

## Abstract (arXiv)

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.