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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
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