Add application of incremental trust model

trustworthy-ai.tex

@@ -160,15 +160,50 @@ model: trustworthiness. Trustworthiness can be defined as the cognitive belief
 of $X$ that $Y$ is trustworthy. Reflective trust involves a cognitive process
 which allows a trustor to obtain reasons for trusting a potential trustee. $X$
 believes in the trustworthiness of $Y$ because there are reasons for $Y$ being
-trustworthy. Similarly to simple trust, reflective trust is still missing the
-aspect of control. Reflective trust does not have to be expressed in binary
-form but can also be expressed by a subjective measure of confidence. The more
-likely a trustee $Y$ is to perform action $A$ towards a goal $G$, the higher
-$X$'s confidence in $Y$ is. Additionally, $X$ might have high reflective trust
-in $Y$ but still does not trust $Y$ to perform a given task because of other,
-potentially unconscious, reasons. 
+trustworthy. Contrary to simple trust, reflective trust includes the aspect of
+control. For an agent $X$ to \emph{reflectively} trust another agent $Y$, $X$
+has objective reasons to trust $Y$ but is not willing to do so without control.
+Reflective trust does not have to be expressed in binary form but can also be
+expressed by a subjective measure of confidence. The more likely a trustee $Y$
+is to perform action $A$ towards a goal $G$, the higher $X$'s confidence in $Y$
+is. Additionally, $X$ might have high reflective trust in $Y$ but still does not
+trust $Y$ to perform a given task because of other, potentially unconscious,
+reasons. 
 
-\subsubsection{Pragmatic Trust}
+\subsubsection{Pragmatic Trust} is the last form of trust in the incremental
+model proposed by \cite{ferrario_ai_2020}. In addition to having objective
+reasons to trust $Y$, $X$ is also willing to do so without control. It is thus a
+combination of simple trust and reflective trust. Simple trust provides the
+non-cognitive, non-controlling aspect of trust and reflective trust provides the
+cognitive aspect.
+
+\subsection{Application of the Model}
+
+Since the incremental model of trust can be applied to human-human as well as
+human-AI interactions, an example which draws from both domains will be
+presented. The setting is that of a company which ships tailor-made machine
+learning (ML) solutions to other firms. On the human-human interaction side
+there are multiple teams working on different aspects of the software. The
+hierarchical structure between bosses, their team leaders and their developers
+is composed of different forms of trust. A boss has worked with a specific team
+leader in the past and thus knows from experience that the team leader can be
+trusted without control (paradigmatic trust). The team leader has had this
+particular team for a number of projects already but has recently hired a new
+junior developer. The team leader has some objective proof that the new hire is
+capable of delivering good work on time due to impressive credentials but needs
+more time to be able to trust the new colleague without control (reflective
+trust).
+
+On the human-AI side, one developer is working on a machine learning algorithm
+to achieve a specific goal $G$. Taking the 5-tuple from the incremental model,
+$X$ is the developer, $Y$ is the machine learning algorithm and $A$ is the
+action the machine learning algorithm takes to achieve the goal $G$. In the
+beginning, $X$ does not yet trust $Y$ to do its job properly. This is due to an
+absence of any past performance metric of the algorithm to achieve $G$. While
+most, if not all, parameters of $Y$ have to be controlled by $X$ in the
+beginning, there is less and less control needed if $Y$ achieves $G$
+consistently. This also increases the cognitive trust in $Y$ as time goes on due
+to accurate performance metrics.
 
 \section{Taxonomy for Trustworthy AI}
 \label{sec:taxonomy}