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Hybrid Responsible AI-Stochastic Approach for SLA Compliance in Multivendor 6G Networks

Emanuel Figetakis, Ahmed Refaey Hussein

TL;DR

The paper addresses accountability gaps in SLA enforcement for autonomous, multivendor 6G networks under closed-loop AI. It proposes a hybrid responsible AI–stochastic learning framework that couples RAI games with dynamic adversarial reweighting and probabilistic exploration, embedded in a Responsibility-Aware Audit Plane (RAAP) for end-to-end traceability. The method achieves improved worst-group performance ($WGAcc$ of $60.5\%$) while maintaining strong average accuracy ($AvgAcc$ of $72.7\%$) and enables $99\%$ traceability of SLA violations to specific agents or vendors, demonstrating robust fairness, auditability, and SLA assurance in zero-touch networks. This work advances auditable, fair, and robust SLA management in multivendor 6G environments, with practical deployment potential through policy feedback loops and audit-grade logging.

Abstract

The convergence of AI and 6G network automation introduces new challenges in maintaining transparency, fairness, and accountability across multivendor management systems. Although closed-loop AI orchestration improves adaptability and self-optimization, it also creates a responsibility gap, where violations of SLAs cannot be causally attributed to specific agents or vendors. This paper presents a hybrid responsible AI-stochastic learning framework that embeds fairness, robustness, and auditability directly into the network control loop. The framework integrates RAI games with stochastic optimization, enabling dynamic adversarial reweighting and probabilistic exploration across heterogeneous vendor domains. An RAAP continuously records AI-driven decision trajectories and produces dual accountability reports: user-level SLA summaries and operator-level responsibility analytics. Experimental evaluations on synthetic two-class multigroup datasets demonstrate that the proposed hybrid model improves the accuracy of the worst group by up to 10.5\%. Specifically, hybrid RAI achieved a WGAcc of 60.5\% and an AvgAcc of 72.7\%, outperforming traditional RAI-GA (50.0\%) and ERM (21.5\%). The audit mechanism successfully traced 99\% simulated SLA violations to the AI entities responsible, producing both vendor and agent-level accountability indices. These results confirm that the proposed hybrid approach enhances fairness and robustness as well as establishes a concrete accountability framework for autonomous SLA assurance in multivendor 6G networks.

Hybrid Responsible AI-Stochastic Approach for SLA Compliance in Multivendor 6G Networks

TL;DR

The paper addresses accountability gaps in SLA enforcement for autonomous, multivendor 6G networks under closed-loop AI. It proposes a hybrid responsible AI–stochastic learning framework that couples RAI games with dynamic adversarial reweighting and probabilistic exploration, embedded in a Responsibility-Aware Audit Plane (RAAP) for end-to-end traceability. The method achieves improved worst-group performance ( of ) while maintaining strong average accuracy ( of ) and enables traceability of SLA violations to specific agents or vendors, demonstrating robust fairness, auditability, and SLA assurance in zero-touch networks. This work advances auditable, fair, and robust SLA management in multivendor 6G environments, with practical deployment potential through policy feedback loops and audit-grade logging.

Abstract

The convergence of AI and 6G network automation introduces new challenges in maintaining transparency, fairness, and accountability across multivendor management systems. Although closed-loop AI orchestration improves adaptability and self-optimization, it also creates a responsibility gap, where violations of SLAs cannot be causally attributed to specific agents or vendors. This paper presents a hybrid responsible AI-stochastic learning framework that embeds fairness, robustness, and auditability directly into the network control loop. The framework integrates RAI games with stochastic optimization, enabling dynamic adversarial reweighting and probabilistic exploration across heterogeneous vendor domains. An RAAP continuously records AI-driven decision trajectories and produces dual accountability reports: user-level SLA summaries and operator-level responsibility analytics. Experimental evaluations on synthetic two-class multigroup datasets demonstrate that the proposed hybrid model improves the accuracy of the worst group by up to 10.5\%. Specifically, hybrid RAI achieved a WGAcc of 60.5\% and an AvgAcc of 72.7\%, outperforming traditional RAI-GA (50.0\%) and ERM (21.5\%). The audit mechanism successfully traced 99\% simulated SLA violations to the AI entities responsible, producing both vendor and agent-level accountability indices. These results confirm that the proposed hybrid approach enhances fairness and robustness as well as establishes a concrete accountability framework for autonomous SLA assurance in multivendor 6G networks.
Paper Structure (8 sections, 6 equations, 5 figures, 1 algorithm)

This paper contains 8 sections, 6 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Architecture and Accountability Model
  3. Hybrid RAI–Stochastic Learning Framework
  4. RAI Games
  5. Hybrid RAI–Stochastic Formulation
  6. Experimentation
  7. Results and Analysis
  8. Conclusion

Figures (5)

  • Figure 1: Responsibility-Aware Audit Plane (RAAP): The RAAP continuously monitors AI-driven control loops within the multi-vendor 6G management stack. It records decision traces $\{(x_t,a_t,s_t,z_t)\}$ and generates two complementary audit views: (i) a user-level SLA dashboard reporting compliance status and remediation actions, and (ii) an operator-level accountability report summarizing average accuracy (AvgAcc), worst-group accuracy (WGAcc), and responsibility attribution per vendor/agent. This design operationalizes the hybrid RAI–stochastic model, ensuring explainable, fair, and auditable SLA compliance.
  • Figure 2: Decision boundaries of the proposed hybrid RAI model versus baseline algorithms on a synthetic two-class mixture dataset.
  • Figure 3: Worst-group and average accuracy across models and random seeds.
  • Figure 4: Comparative end-user showing accuracy, fairness gap, and SLA compliance across Standard ERM, RAI-FW, Adaboost, RAI-GA, Online GDRO, and Hybrid RAI.
  • Figure 5: Comparative end-user showing accuracy, fairness gap, group accuracies, group sizes, and group margins across Standard ERM, RAI-FW, Adaboost, RAI-GA, Online GDRO, and Hybrid RAI.