Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems

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. A. Motivation Next-generation wireless networks are expected to leverage AI for tasks ranging from physical-layer processing to resource management. Initiatives like O-RAN exemplify this trend by defining open network architectures that enable data-driven control at different time scales via modular AI applications [1]. While AI promises improved efficiency and flexibility, most AI apps function as black boxes, raising significant reliability concerns. These reliability concerns may make operators hesitant to cede network functionalities to black-box systems without additional safeguards.