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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