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Tobias Eidelpes 2022-01-05 14:24:43 +01:00
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trustworthy-ai.tex
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@ -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 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 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 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 indeed done his/her job. Sometimes we will only find out later that what did
in fact done did not happen in line with our own interests. Trust is therefore happen was not in line with our own interests. Trust is therefore also always a
also always a function of time. Previously entrusted people can—depending on function of time. Previously entrusted people can—depending on their track
their track record—either continue to be trusted or lose trust. 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 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 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 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 do. This relationship encompasses \emph{dumb} devices such as vacuum cleaners
and refrigerators, as well as seemingly \emph{intelligent} systems such as and refrigerators, as well as \emph{intelligent} systems such as algorithms
algorithms performing medical diagnoses. Artificial intelligence systems belong performing medical diagnoses. Artificial intelligence systems belong to the
to the latter category when they are functioning well, but can easily slip into latter category when they are functioning well, but can easily slip into the
the former in the case of a poorly trained machine learning algorithm that former in the case of a poorly trained machine learning algorithm that simply
simply classifies pictures of dogs and cats always as dogs, for example. classifies pictures of dogs and cats always as dogs, for example.
Scholars usually divide trust either into \emph{cognitive} or Scholars usually divide trust either into \emph{cognitive} or
\emph{non-cognitive} forms. While cognitive trust involves some sort of rational \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 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 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 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 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 expertise. Conversely, non-cognitive trust allows humans to place trust in
people they know well, without a need for rational justification, but just 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 bias}. The last category of bias is \emph{discriminatory bias} and is of
particular relevance when designing artificial intelligence systems. 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 favoritism towards an individual or a group based on their inherent or acquired
characteristics} \cite[p.~2]{mehrabiSurveyBiasFairness2021}. Fairness in the characteristics} \cite[p.~2]{mehrabiSurveyBiasFairness2021}. Fairness in the
context of artificial intelligence thus means that the system treats groups or context of artificial intelligence thus means that the system treats groups or