Add Social Computing section

trustworthy-ai.tex

@@ -325,6 +325,7 @@ transformations can be a difficult endeavor because prediction accuracy can
 suffer.
 
 \subsection{Explainability}
+\label{ssec:explainability}
 
 Recent advances in artificial intelligence can mostly be attributed to an
 ever-increasing model complexity, made possible by massive deep neural networks
@@ -471,6 +472,137 @@ output \cite{hintonDistillingKnowledgeNeural2015}.
 \section{Social Computing}
 \label{sec:social-computing}
 
+So far, trustworthy AI has been defined by exploring how trust between humans
+and other humans or AI is formed. From a computational perspective,
+chapter~\ref{sec:taxonomy} provides various insights into how artificial
+intelligence can be made more trustworthy. Since implementing these
+recommendations for improved designs of artificial intelligence systems require
+a thorough understanding of technical matters, some systems will not be
+sufficiently equipped for an environment in which trustworthiness is a
+requirement. Furthermore, a system---regardless of whether it falls into the AI
+category, or not---is almost always embedded in a context which includes many
+other agents as well. Other agents do not only constitute technical machinery
+but also humans which are part of the larger system. Humans interact with these
+systems through interfaces which are specifically designed for this purpose. It
+is therefore reasonable to assume that the \emph{social context} in which a
+system operates plays an important part in its trustworthiness. Especially with
+regards to a business environment, models which integrate both aspects well
+stand to gain a lot. This relationship---between an AI system and the human
+context in which it is situated---is the topic of this chapter. 
+
+\subsection{Example Scenario}
+\label{sec:example-scenario}
+
+A common scenario in a monitoring environment is the following. A business has
+multiple services in operation which facilitate interactions with their
+customers. All of these services produce log messages which contain information
+about the service's current status, how many customers are currently being
+served and potential errors the system is encountering. With a high number of
+parallel running services, an increasing amount of log messages is produced.
+These log messages have to be looked at by either another system or a unit of
+humans so that only the most relevant messages are passed to the next stage. The
+next stage might be a manager or similar entity responsible for decisions
+concerning the on-going operation of the services.
+
+There are multiple dimensions to this scenario which require different
+approaches to the organization of the
+workflow~\cite{dustdarSocialComputeUnit2011}. The first dimension is the
+\emph{number of events}. A system which produces a relatively low number of
+events due to having a low number of services, is most likely appropriately
+managed by a handful of humans. If the number of events is high and/or crosses a
+certain threshold, the sheer amount of information arriving at the processors
+(in this case humans) is overwhelming.
+
+The second dimension is a product of the heterogeneous environment of an IT
+service provider. Since multiple services are in operation which likely do not
+follow the same standards for how log messages or events look like, when they
+are produced and how they are reported, processors are faced with a high
+\emph{event variability}~\cite{dustdarSocialComputeUnit2011}. A high event
+variability places a high cognitive load on any human responsible for processing
+the incoming events. Event variability and cognitive load are proportional to
+each other in that an increasing variability results in an increasing cognitive
+load.
+
+The third dimension concerns the likely necessity to \emph{upgrade} the systems
+once \emph{growth} happens~\cite{dustdarSocialComputeUnit2011}. A change in the
+underlying systems often requires the processors of the events to adapt to the
+changing environment as well. They have to know in which way the systems have
+changed and what kind of different events they produce so that the agents can
+continue their critical function. A failure to adapt downstream structures to
+the changing systems can stunt the business' growth and its ability to compete.
+
+To deal with the complexity of these three dimensions, a common solution is to
+have a system (often rule-based) which extracts relevant information from all
+events and forwards a processed version to human agents for further inspection
+and resolving. Additionally, a second group of human agents is necessary to
+change the rules of the system so that it can keep up with changes in its
+environment. \textcite{dustdarSocialComputeUnit2011} propose such a group and
+term it the \emph{Social Compute Unit}.
+
+\subsection{Social Compute Unit}
+\label{sec:social-compute-unit}
+
+The social compute unit is composed of a team of resources possessing either
+\enquote{skills in the problem domain (event analysis) or the system domain
+(configuring the filtering
+software)}~\cite[p.~66]{dustdarSocialComputeUnit2011}.
+Figure~\ref{fig:social-compute-unit} shows the environment the social compute
+unit is embedded in and the interactions between the parties in the
+organizational unit. An important property of the social compute unit is that
+it's members are not dedicated resources but rather act when the requirement to
+do so comes up. For example, if a customer-facing service has an update which
+introduces breaking changes, a knowledgeable member of the monitoring agents
+steps in to update the affected rules. Another reason for resources expending
+time to work on the rule-based software is to improve processes within the
+structure so that certain tasks are accomplished in a more efficient manner.
+Members of the social compute unit are rewarded based on metrics such as the
+number of rules configured, time spent in hours or hours saved by the
+introduction of new rules.
+
+\begin{figure}
+    \centering
+    \includegraphics[width=.7\textwidth]{figures/social-compute-unit.png}
+    \caption{Social Compute Unit (SCU). An IT service provider needs to monitor
+        the operations of multiple services/servers. Monitoring agents act to
+        resolve issues a the software deems important. The social compute unit
+        updates, adds or deletes rules. Image
+        credit~\cite{dustdarSocialComputeUnit2011}.}
+    \label{fig:social-compute-unit}
+\end{figure}
+
+Since the social compute unit is not composed of dedicated resources, it comes
+only into effect when a specific problem has to be solved. It is therefore
+requested by someone to get the right team for the right job. The team is then
+assembled \emph{on demand} by a person with sufficient expertise in the problem
+domain such that the unit is constructed while taking the member's skills and
+ability to work together into account. Alternatively, the task of assembling the
+team does not have to be carried out by a human but can also be carried out by
+specialized (matching) software.
+
+\textcite{dustdarSocialComputeUnit2011} define a social compute unit's life
+cycle as consisting of six stages. In the first stage, a unit comes into effect
+once a \emph{request} has been issued. In the \emph{create} stage, the team is
+compiled. After that it goes through an \emph{assimilation} phase wherein it
+becomes acquainted with the task at hand. The \emph{virtualization} stage
+follows where it is ensured that the unit can communicate effectively and has
+the necessary resources, such as a test environment. In the \emph{deployment}
+phase, the unit produces results and implements them in the production
+environment. After completion of the objective the team is \emph{dissolved}.
+
+Due to the close integration of monitoring agents, the social compute unit and
+the software which initially processes the events generated by the services, a
+high degree of trust is required. Since the software is architected as a
+rule\nobreakdash-based system, it has the properties necessary for achieving
+explainability of its actions (see section~\ref{ssec:explainability}).
+Monitoring agents have the opportunity to become (temporarily) part of the
+social compute unit which allows them to configure the rules of the software
+themselves. Working with the software and contributing a vital part to a
+software's function has the potential effect of increasing the software's
+trustworthiness. Additionally, stakeholders outside of the system can be
+confident that the degree to which humans are involved is high, which arguably
+also increases the system's trustworthiness. The architecture proposed
+by~\textcite{dustdarSocialComputeUnit2011} is therefore a good model to build
+trust in the system's inner workings and results.
 
 \section{Conclusion}
 \label{sec:conclusion}