diff --git a/trustworthy-ai.tex b/trustworthy-ai.tex
index 83695c6..08a226b 100644
--- a/trustworthy-ai.tex
+++ b/trustworthy-ai.tex
@@ -93,20 +93,20 @@ when we need medical advice. Trusting in these contexts means to cede control
 over a particular aspect of our lives to someone else. We do so in expectation
 that the trustee does not violate our \emph{social agreement} by acting against
 our interests. Often times we are not able to confirm that the trustee has
-indeed done his/her job. Sometimes we will only find out later that what was
-in fact done did not happen in line with our own interests. Trust is therefore
-also always a function of time. Previously entrusted people can—depending on
-their track record—either continue to be trusted or lose trust.
+indeed done his/her job. Sometimes we will only find out later that what did
+happen was not in line with our own interests. Trust is therefore also always a
+function of time. Previously entrusted people can—depending on their track
+record—either continue to be trusted or lose trust.
 
 We do not only trust certain people to act on our behalf, we can also place
 trust in things rather than people. Every technical device or gadget receives
 our trust to some extent, because we expect it to do the things we expect it to
 do. This relationship encompasses \emph{dumb} devices such as vacuum cleaners
-and refrigerators, as well as seemingly \emph{intelligent} systems such as
-algorithms performing medical diagnoses. Artificial intelligence systems belong
-to the latter category when they are functioning well, but can easily slip into
-the former in the case of a poorly trained machine learning algorithm that
-simply classifies pictures of dogs and cats always as dogs, for example.
+and refrigerators, as well as \emph{intelligent} systems such as algorithms
+performing medical diagnoses. Artificial intelligence systems belong to the
+latter category when they are functioning well, but can easily slip into the
+former in the case of a poorly trained machine learning algorithm that simply
+classifies pictures of dogs and cats always as dogs, for example.
 
 Scholars usually divide trust either into \emph{cognitive} or
 \emph{non-cognitive} forms. While cognitive trust involves some sort of rational
@@ -114,7 +114,7 @@ and objective evaluation of the trustee's capabilities, non-cognitive trust
 lacks such an evaluation. For instance, if a patient comes to a doctor with a
 health problem which resides in the doctor's domain, the patient will place
 trust in the doctor because of the doctor's experience, track record and
-education. The patient thus consciously decides that he/she would rather trust
+education. The patient, thus consciously, decides that he/she would rather trust
 the doctor to solve the problem and not a friend who does not have any
 expertise. Conversely, non-cognitive trust allows humans to place trust in
 people they know well, without a need for rational justification, but just
@@ -298,7 +298,7 @@ made by the model architects, productive bias quickly turns into \emph{erroneous
 bias}. The last category of bias is \emph{discriminatory bias} and is of
 particular relevance when designing artificial intelligence systems.
 
-Fairness, on the other hand, is \enquote{…the absence of any prejudice or
+Fairness, on the other hand, is \enquote{the absence of any prejudice or
 favoritism towards an individual or a group based on their inherent or acquired
 characteristics} \cite[p.~2]{mehrabiSurveyBiasFairness2021}. Fairness in the
 context of artificial intelligence thus means that the system treats groups or