A lot of modern day applications use machine learning algorithms, which may produce computer models, that are hard to understand. These models are applicable in decision support systems, where they help users make better decisions. Here we investigate how can local post-hoc explanations affect user decisions and their overall utility. We have conducted experiments with people where we simulated three environments and monitored user behaviour in each of them, which we later analysed.
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