Opening the black box of zero-touch 6G: Sequential XAI in the control loop

Zero-touch Service Management (ZSM) delegates the control of 6G virtualised resources to AI/ML pipelines, increasingly built on time-series models whose opacity undermines operator trust and conflicts with regulatory requirements. Existing XAI-for-networking proposals largely (i) reuse tabular methods unsuited to networking time series and (ii) treat explanations as post-hoc artefacts for humans rather than as first-class signals inside the MAPE-K loop. This PhD aims to close this two-fold gap by designing XAI native to time-series predictors on networking data and by feeding their outputs — with Machine Reasoning (MR) over a knowledge base — into the Plan stage of MAPE-K. We present the problem context, a structured state of the art on MAPE-K-based ZSM and on XAI for sequential models, three research questions, an initial methodology, an evaluation plan grounded on injected ground truth, and a three-year roadmap.