Abstract: eXplainable Artifical Intelligence (XAI) is a new but fast-moving area in machine learning. Ultimately, the goal is to improve the experience of using black-box automated systems by allowing users to probe models for explanations of outcomes on three possible levels: explanation of the instance, explanation of the local behaviour, or explanations of the global behaviour. However, what constitutes a good and complete explanation is still an open problem. How do we extract these explanations? What information should be shown to the user, and what should be hidden? And how can we communicate this information effectively to give users true agency within the system? To this end, we have are investigating two aspects of XAI: Are the currently available tools in XAI fit for the layperson? Which types of people are susceptible to bad explanations that can initiate unwarranted trust in a system? https://arxiv.org/pdf/2303.00934.pdf Given that changing an outcome takes time and effort on the part of the user, how can we maximise the likelihood that a user can achieve a counterfactual given that a path defined at time t=0 may become infeasible or more challenging at t>0? https://arxiv.org/pdf/2306.02786.pdf We therefore introduce the concept of an explanatory multiverse that attempts to capture all possible paths to a desired (or all desired) outcomes. We introduce a framework in order to directly compare the geometry of these paths in order to generate addional paths that maximise user-agency under (potentially) imperfect information at t=0.