diff --git a/trustworthy-ai.tex b/trustworthy-ai.tex
index 08a226b..57d2f6d 100644
--- a/trustworthy-ai.tex
+++ b/trustworthy-ai.tex
@@ -50,14 +50,14 @@ environment~\cite{russellArtificialIntelligenceModern2021}.
 While the possibilities of AI are seemingly endless, the public is slowly but
 steadily learning about its limitations. These limitations manifest themselves
 in areas such as autonomous driving and medicine, for example. These are fields
-where AI can have a direct—potentially life-changing—impact on people's lives. A
-self-driving car operates on roads where accidents can happen at any time.
-Decisions made by the car before, during and after the accident can result in
-severe consequences for all participants. In medicine, AIs are increasingly used
-to drive human decision-making. The more critical the proper use and functioning
-of AI is, the more trust in its architecture and results is required. Trust,
-however, is not easily defined, especially in relation to artificial
-intelligence. 
+where AI can have a direct---potentially life-changing---impact on people's
+lives. A self-driving car operates on roads where accidents can happen at any
+time. Decisions made by the car before, during and after the accident can result
+in severe consequences for all participants. In medicine, AIs are increasingly
+used to drive human decision-making. The more critical the proper use and
+functioning of AI is, the more trust in its architecture and results is
+required. Trust, however, is not easily defined, especially in relation to
+artificial intelligence. 
 
 This work will explore the following question: \emph{Can artificial intelligence
 be trustworthy, and if so, how?} To be able to discuss this question, trust has
@@ -95,8 +95,8 @@ 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 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.
+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
@@ -122,8 +122,8 @@ because of their existing relationship.
 
 Due to the different dimensions of trust and its inherent complexity in
 different contexts, frameworks for trust are an active field of research. One
-such framework—proposed by \textcite{ferrarioAIWeTrust2020}—will be discussed in
-the following sections.
+such framework---proposed by \textcite{ferrarioAIWeTrust2020}---will be
+discussed in the following sections.
 
 \subsection{Incremental Model of Trust}
 
@@ -243,8 +243,8 @@ the model receives from its beneficiaries. One such example may be Netflix'
 movie recommendation system that receives which type of movies certain users are
 interested in. A malicious user could therefore attack the recommendation engine
 by supplying wrong inputs. \emph{Evasion attacks} consist of alterations which
-are made to the training samples in such a way that these alternations—while
-generally invisible to the human eye—mislead the algorithm.
+are made to the training samples in such a way that these alternations---while
+generally invisible to the human eye---mislead the algorithm.
 
 \emph{White-box attacks} allow an attacker to clearly see all parameters and all
 functions of a model. \emph{Black-box attackers}, on the other hand, can only
@@ -271,11 +271,11 @@ trade-offs is therefore critical for real-world applications.
 Non-discrimination and fairness are two important properties of any artificial
 intelligence system. If one or both of them are violated, trust in the system
 erodes quickly. Often researchers only find out about a system's discriminatory
-behavior when the system has been in place for a long time. In other cases—such
-as with the chat bot Tay from Microsoft Research, for example—the problems
-become immediately apparent once the algorithm is live. Countless other models
-have been shown to be biased on multiple fronts: the US' recidivism prediction
-software \textsc{COMPAS} is biased against black people
+behavior when the system has been in place for a long time. In other
+cases---such as with the chat bot Tay from Microsoft Research, for example---the
+problems become immediately apparent once the algorithm is live. Countless other
+models have been shown to be biased on multiple fronts: the US' recidivism
+prediction software \textsc{COMPAS} is biased against black people
 \cite{angwinMachineBias2016}, camera software for blink detection is biased
 against Asian eyes \cite{roseFaceDetectionCamerasGlitches2010} and gender-based
 discrimination in the placement of career advertisements