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.

翻译：人工智能与6G网络自动化的融合为跨多供应商管理系统保持透明度、公平性和可问责性带来了新挑战。尽管闭环AI编排提升了适应性与自优化能力，但也产生了责任缺口，即服务等级协议违规行为无法因果归因于特定智能体或供应商。本文提出一种混合式负责任AI-随机学习框架，将公平性、鲁棒性和可审计性直接嵌入网络控制环路。该框架将RAI博弈与随机优化相结合，支持跨异构供应商领域的动态对抗性重加权与概率探索。RAAP持续记录AI驱动的决策轨迹，并生成双重问责报告：用户级SLA摘要与运营商级责任分析。在合成的二类多组数据集上的实验评估表明，所提出的混合模型将最差组准确率最高提升10.5%。具体而言，混合RAI实现了60.5%的WGAcc与72.7%的AvgAcc，优于传统RAI-GA（50.0%）与ERM（21.5%）。审计机制成功将99%的模拟SLA违规追溯至应负责的AI实体，同时生成供应商级与智能体级问责指标。这些结果证实，所提出的混合方法不仅增强了公平性与鲁棒性，还为多供应商6G网络中的自主SLA保障建立了具体的问责框架。