Systematic conservation planning (SCP) is increasingly used to support protected area design in Antarctica, yet the complexity of optimization models can make conservation decisions difficult for stakeholders to understand. This creates challenges within the Antarctic Treaty System, where proposals must be scientifically robust, transparent, and defensible to multiple Parties. To address this issue, we propose the eXplainable Systematic Conservation Planning (XSCP) framework, which integrates explainable artificial intelligence (XAI) principles into SCP process. XSCP links stakeholder questions to six explanation layers: data quality, attribution, robustness, strategic outcomes, future dynamics, and implementation feasibility. These layers generate spatially explicit indicators that explain why certain areas are selected, how sensitive decisions are to uncertainty, and how priorities may change under future conditions. We demonstrate the framework using a worked example from Port Lockroy, Antarctica. The example shows how XAI-inspired approaches, including SHAP-based sensitivity analysis, can indicate the drivers of conservation priorities, quantify decision robustness, and communicate trade-offs in a transparent manner. By transforming optimization outputs into structured explanations, XSCP helps bridge the gap between analytical complexity and stakeholder understanding. The framework provides a practical pathway for improving transparency, trust, and evidence-based decision-making in Antarctic conservation planning and protected area governance.