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

• URL: http://arxiv.org/abs/2504.09310v3
• Date: Sun, 27 Apr 2025 11:36:45 GMT
• Title: Conformal Calibration: Ensuring the Reliability of Black-Box AI in Wireless Systems
• Authors: Osvaldo Simeone, Sangwoo Park, Matteo Zecchin,
• Abstract summary: The paper reviews conformal calibration, a general framework that moves beyond the state of the art by adopting computationally lightweight, advanced statistical tools.<n>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.
• Score: 36.407171992845456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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