FORUM SOCIAL MEDIA
are similar to ourselves. Effectively
identifying and reflecting similarity
is one key aspect of the sociopathic AI
challenge that would allow us to build
the right types of ASAs.
We identified a critical need
for research in an area we termed
disinformation engineering. The
research challenge is focused on
creating information messages that
have optimum influence across specific
constituencies in a social computing
system. This should not be thought of as
just propaganda or simply marketing.
Disinformation engineering is an
engineering problem with the goal of
having an optimal disinformational
impact and parameterizing how to
achieve that goal for a set of participants
within some specific tolerances.
Disinformation engineering has
specific theory upon which to build.
Claude Shannon is well known
for characterizing the theoretic
propagation of information to
ensure robustness. The challenge for
disinformation engineering is to be able
to inject distortion or error to reliably
generate a disinformation message with
that crosses the ties that make up
the connections among individuals.
Further, the sucker network needs to
be robust, which yields an interesting
challenge in the area of structural holes.
Most social network analyses that focus
on structural holes work to identify
them for the purposes of filling them so
that the network itself is robust to flaws
in information transmission. Instead,
what we would need is to be able to
create just the right structural holes so
that disconfirming evidence could not
be injected into our sucker network.
Establishing the right structural
holes is an area where our social
network research challenge relates to
our sociopathic AI research challenge.
One piece of work that the ASA might
need to do is isolate participants to
create the right type of structural hole
and therefore make the information
we need to inject into the sucker
network more robust. The sociopathic
AI research agenda needs to be able
to effectively model differing tastes,
personalities, and interaction styles
across a network. One thing we know
is that we tend to like people who I M A
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