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

2026-02-10

AI Summary

This work addresses the challenge of attributing SLA violations in multi-vendor 6G networks, where AI-driven closed-loop orchestration lacks transparency and auditability. To tackle this, the authors propose a hybrid framework integrating responsible AI (RAI) game-theoretic principles with stochastic optimization, embedding fairness, robustness, and auditability directly into the network control loop. The framework employs dynamic adversarial reweighting and probabilistic exploration to adaptively adjust decision weights across vendor domains while logging AI decision trajectories to establish a dual-layer accountability mechanism at both user and operator levels. Experimental results on synthetic datasets demonstrate that the approach improves worst-group accuracy by 10.5% (reaching 60.5%), achieves an average accuracy of 72.7%, and enables precise attribution of 99% of SLA violations to specific AI entities, significantly outperforming RAI-GA and ERM baselines.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• SLA compliance
• responsibility gap
• multivendor 6G networks
• accountability
• AI transparency

Innovation

• Responsible AI
• Stochastic Optimization
• SLA Compliance
• Multi-vendor 6G Networks
• Accountability Framework