用统计方法让黑箱AI在无线系统中更可靠，无需重训练。
Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems
- 用不确定性量化和超参选择做部署前校准
- 实时监控发现并应对AI故障，保障运行稳定
- 适合关心AI可靠性与可解释性的网络运营商
人工智能有望通过提升效率、自动化与决策能力，彻底变革通信网络。然而，大多数AI模型的黑箱特性带来了显著风险，可能阻碍运营商采纳。当前主流的训练即部署策略无法解决这些风险。本文综述了符合性校准（Conformal Calibration）这一通用框架，它采用计算轻量的先进统计工具，在不需额外训练或微调的前提下，提供形式化的可靠性保障。该框架涵盖部署前的不确定性量化或超参数选择校准、运行时的实时故障检测与缓解，以及部署后的反事实性能分析，以回答“如果……会怎样”的诊断问题。将符合性校准融入AI全生命周期，有助于网络运营商建立对黑箱AI模型的信任，使其成为无线系统中可靠的使能技术。
原文摘要 · Abstract (English)
AI is poised to revolutionize telecommunication networks by boosting efficiency, automation, and decision-making. However, the black-box nature of most AI models introduces substantial risk, possibly deterring adoption by network operators. These risks are not addressed by the current prevailing deployment strategy, which typically follows a best-effort train-and-deploy paradigm. This paper reviews conformal calibration, a general framework that moves beyond the state of the art by adopting computationally lightweight, advanced statistical tools that offer formal reliability guarantees without requiring further training or fine-tuning. Conformal calibration encompasses pre-deployment calibration via uncertainty quantification or hyperparameter selection; online monitoring to detect and mitigate failures in real time; and counterfactual post-deployment performance analysis to address "what if" diagnostic questions after deployment. By weaving conformal calibration into the AI model lifecycle, network operators can establish confidence in black-box AI models as a dependable enabling technology for wireless systems.
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