  The trustworthiness of Machine Learning (ML) models can be difficult to
assess, but is critical in high-risk or ethically sensitive applications. Many
models are treated as a `black-box' where the reasoning or criteria for a final
decision is opaque to the user. To address this, some existing Explainable AI
(XAI) approaches approximate model behaviour using perturbed data. However,
such methods have been criticised for ignoring feature dependencies, with
explanations being based on potentially unrealistic data. We propose a novel
framework, CHILLI, for incorporating data context into XAI by generating
contextually aware perturbations, which are faithful to the training data of
the base model being explained. This is shown to improve both the soundness and
accuracy of the explanations.
