diff --git a/trustworthy-ai.tex b/trustworthy-ai.tex
index 860a2cc..813e416 100644
--- a/trustworthy-ai.tex
+++ b/trustworthy-ai.tex
@@ -367,7 +367,7 @@ outcome, the model is inherently explainable. Examples are decision trees,
 linear regression models, rule-based models and Bayesian networks. This approach
 is not possible for neural networks and thus \emph{model-agnostic explanations}
 have to be found. \textsc{LIME} \cite{ribeiroWhyShouldTrust2016} is a tool to
-find such model-agnostic explanations. \textsc{LIME} works \enquote{…by learning
+find such model-agnostic explanations. \textsc{LIME} works \enquote{by learning
 an interpretable model locally around the prediction}
 \cite[p.~1]{ribeiroWhyShouldTrust2016}. An advantage of this approach is that
 \textsc{LIME} is useful for any model, regardless of how it is constructed. Due