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Machine learning systems have become popular in fields such as marketing,financing, or data mining. While they are highly accurate, complex machinelearning systems pose challenges for engineers and users. Their inherentcomplexity makes it impossible to easily judge their fairness and thecorrectness of statistically learned relations between variables and classes.Explainable AI aims to solve this challenge by modelling explanations alongsidewith the classifiers, potentially improving user trust and acceptance. However,users should not be fooled by persuasive, yet untruthful explanations. Wetherefore conduct a user study in which we investigate the effects of modelaccuracy and explanation fidelity, i.e. how truthfully the explanationrepresents the underlying model, on user trust. Our findings show that accuracyis more important for user trust than explainability. Adding an explanation fora classification result can potentially harm trust, e.g. when addingnonsensical explanations. We also found that users cannot be tricked byhigh-fidelity explanations into having trust for a bad classifier. Furthermore,we found a mismatch between observed (implicit) and self-reported (explicit)trust.

How model accuracy and explanation fidelity influence user trust