The motivation of this industrial presentation is to provide the GLEBECOM community with recent research findings in explainable, causal and safe AI techniques that support trustworthiness in various network operations and use cases. Ericsson researchers are leading in the field of the aforementioned technologies and we believe this talk will provide with new insights in telecom domain, from an industrial perspective, and give the opportunity to research, industrial or other participants to exchange ideas and enhance collaboration opportunities. The presentation will include the four aforementioned speakers in on the following topics: 1. Explainable AI (XAI) and Explainable RL (XRL): Motivating the need of Explainable AI and XRL, the XAI framework, and with the examples form telecom industry implementing XAI and XRL. 2. Symbolic safe reinforcement learning for RAN control that provides novel formal techniques that assure safe learning in live deployment of RL agents on the network. 3. Causal AI: Causal ML and RL models have better sample efficiency and improved out-of distributional generalization compared to models built on observational data only. We will demonstrate impact of Causal AI for Telecom problems, such as Radio root cause analysis and fronthaul congestion control. 4. Trustworthiness measures. The aim is to provide means to measure and analyze the properties and performance of different explanation methods by exploration and implementation of metrics.