SLIPMAP: Fast and Robust Manifold Visualisation for Explainable AI

We propose a new supervised manifold visualisation method, slipmap, that finds local explanations for complex black-box supervised learning methods and creates a two-dimensional embedding of the data items such that data items with similar local explanations are embedded nearby. This work extends and improves our earlier algorithm and addresses its shortcomings: poor scalability, inability to make predictions, and a tendency to find patterns in noise. We present our visualisation problem and provide an efficient GPU-optimised library to solve it. We experimentally verify that slipmap is fast and robust to noise, provides explanations that are on the level or better than the other local explanation methods, and are usable in practice.