Human oversight of AI demands transparent models, especially in high-stakes contexts. While interpretable models like decision trees offer clarity, traditional axis-aligned trees struggle with complex features, and oblique trees, though more expressive, are hard to understand. This work introduces a human-centred framework using parallel coordinates (PCs) to visualise and construct oblique decision trees interactively. By enabling the visualisation of 3D hyperplanes and allowing users to manipulate them directly, the method enhances explainability without compromising accuracy. We present an efficient method for finding hyperplanes of up to 3 dimensions. We show experimentally by comparing several methods that our oblique trees do not sacrifice accuracy, but the explainability of the trees (due to their simplicity) is radically enhanced. This bridges the gap between model complexity and interpretability, supporting human oversight in AI decision-making.