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

• URL: http://arxiv.org/abs/2502.05608v1
• Date: Sat, 08 Feb 2025 15:30:25 GMT
• Title: Closing the Responsibility Gap in AI-based Network Management: An Intelligent Audit System Approach
• Authors: Emanuel Figetakis, Ahmed Refaey Hussein,
• Abstract summary: 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 tools, 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.
• Score: 0.0
• License:
• 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.

Related papers

• Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning [55.641299901038316]
  AI-generated content can organize collaborative Mobile AIGC Service Providers (MASPs) at network edges to provide ubiquitous and customized content for resource-constrained users. Such a paradigm faces two significant challenges: 1) raw prompts often lead to poor generation quality due to users' lack of experience with specific AIGC models, and 2) static service provisioning fails to efficiently utilize computational and communication resources. We develop an interactive prompt engineering mechanism that leverages a Large Language Model (LLM) to generate customized prompt corpora and employs Inverse Reinforcement Learning (IRL) for policy imitation.
  arXiv  Detail & Related papers  (2025-02-17T03:05:20Z)
• AI-in-the-Loop Sensing and Communication Joint Design for Edge Intelligence [65.29835430845893]
  We propose a framework that enhances edge intelligence through AI-in-the-loop joint sensing and communication. A key contribution of our work is establishing an explicit relationship between validation loss and the system's tunable parameters. We show that our framework reduces communication energy consumption by up to 77 percent and sensing costs measured by the number of samples by up to 52 percent.
  arXiv  Detail & Related papers  (2025-02-14T14:56:58Z)
• Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services [55.0337199834612]
  Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics.
  arXiv  Detail & Related papers  (2024-11-03T07:01:13Z)
• Reservoir computing for system identification and predictive control with limited data [3.1484174280822845]
  We assess the ability of RNN variants to both learn the dynamics of benchmark control systems and serve as surrogate models for model predictive control (MPC) We find that echo state networks (ESNs) have a variety of benefits over competing architectures, namely reductions in computational complexity, longer valid prediction times, and reductions in cost of the MPC objective function.
  arXiv  Detail & Related papers  (2024-10-23T21:59:07Z)
• Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection [21.003217781832923]
  This paper proposes an Automated Machine Learning (AutoML)-based autonomous IDS framework towards achieving autonomous cybersecurity for next-generation networks. The proposed AutoML-based IDS was evaluated on two public benchmark network security datasets, CICIDS 2017 and 5G-NIDD. This research marks a significant step towards fully autonomous cybersecurity in next-generation networks, potentially revolutionizing network security applications.
  arXiv  Detail & Related papers  (2024-09-05T00:36:23Z)
• Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
  Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC. We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
  arXiv  Detail & Related papers  (2023-07-14T04:13:11Z)
• Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks [68.00382171900975]
  In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. We present the AIGC-as-a-service concept and discuss the challenges in deploying A at the edge networks. We propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
  arXiv  Detail & Related papers  (2023-01-09T09:30:23Z)
• Adversarial training with informed data selection [53.19381941131439]
  Adrial training is the most efficient solution to defend the network against these malicious attacks. This work proposes a data selection strategy to be applied in the mini-batch training. The simulation results show that a good compromise can be obtained regarding robustness and standard accuracy.
  arXiv  Detail & Related papers  (2023-01-07T12:09:50Z)
• Enabling AI Quality Control via Feature Hierarchical Edge Inference [6.490724361345847]
  This work proposes a feature hierarchical EI (FHEI) comprising feature network and inference network deployed at an edge server and corresponding mobile. A higher scale feature requires more computation and communication loads while it provides a better AI quality. It is verified by extensive simulations that the proposed joint communication-and-computation control on FHEI architecture always outperforms several benchmarks.
  arXiv  Detail & Related papers  (2022-11-15T02:54:23Z)
• Detection of Insider Attacks in Distributed Projected Subgradient Algorithms [11.096339082411882]
  We show that a general neural network is particularly suitable for detecting and localizing malicious agents. We propose to adopt one of the state-of-art approaches in federated learning, i.e., a collaborative peer-to-peer machine learning protocol. In our simulations, a least-squared problem is considered to verify the feasibility and effectiveness of AI-based methods.
  arXiv  Detail & Related papers  (2021-01-18T08:01:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.