Large Language Models (LLMs) are powerful in-context learners, achieving strong performance with just a few high-quality demonstrations. However, fairness concerns arise in many in-context classification tasks, especially when predictions involve sensitive attributes. To address this, we propose JUDGE—a simple yet effective framework for selecting fair and representative demonstrations that improve group fairness in In-Context Learning. JUDGE constructs the demonstration set iteratively using a greedy approach, guided by a small, carefully selected jury set. Our method remains robust across varying LLM architectures and datasets, ensuring consistent fairness improvements. We evaluate JUDGE on four datasets using four LLMs, comparing it against seven baselines. Results show that JUDGE consistently improves fairness metrics without compromising accuracy.