In the field of Multi-fidelity Expensive Black-Box (Mf-EBB), a problem instance consists of an expensive yet accurate source of information, and one or more cheap yet less accurate sources of information. The field aims to provide techniques either to accurately explain how decisions affect design outcome, or to find the best decisions to optimise design outcomes. Despite the existence of many techniques which provide solutions to both aims, only in recent years have researchers begun to explore the conditions under which the information sources of lesser accuracy can be exploited reliably. This talk will present recent work which approaches the characterisation of harmful low-fidelity sources as an algorithm selection problem. We employ recently developed benchmark filtering techniques to conduct a bias-free assessment, generating and analysing an objectively varied benchmark suite with the technique known as Instance Space Analysis, which provides an intuitive visualisation of when a low-fidelity source should be used. By performing this analysis using only the limited data available to train a surrogate model, we are also able to provide guidelines that can be directly used in an applied industrial setting.