Part of my existing
research plan includes research about the ethics of information
transmission. What does this trivial sounding description mean? In order for information - according to most contemporary competing descriptions of the nature of information* - to be deployed or effective (either in accordance with teleological content or on the basis of cause-effect complexes), it needs to be encoded and transmitted some distance. Even reading directly from a source requires this. (*There are dozens of conceptions of the nature of information, but most require transmission capability, including statistical, algorithmic (Kolmogorovian and Solomonovorian), semiotic-significatory (Peircian), and teleosemantic.) Where semantic information (I have argued [1] [2] that all information is
inherently and intrinsically semantic on a causal and indicative basis) is
transmitted, there is an ethical dimension whenever any human agent is, or is
part of, the source of that information. Any information generated by, encoded
from, or originating at a human agent source is ethical because it
intrinsically indicates something about that agent/person.
Artificial intelligence algorithms, and
especially those that involve recursively
self-modifying code, will be able to manipulate this information
and make powerful predictions based upon it. Our ability to comprehend or even detect how this is achieved may in some cases be very limited or non-existent, even with computer and AI-aided analyses. This is probably going to be especially
true of AI algorithms running on quantum computing platforms. Such AI (especially
strong AI if, and when, it eventuates) will be able to use and manipulate both the most basic, and the most complex, of signals and messages emitted or transmitted from a
human-agent source, and do so in ways that human agents are not even capable of
tracking or analysing (not without computer aided analysis involving other AI
or deep learning algorithms.)
There will be a high-risk epistemic and processing/cognitive-power
asymmetry between AI algorithms of this kind and the human sources whose information
they process. If we apply any one of a number of metrics to the disparity or
distance between the processing power of AI algorithms and their human target
sources, this asymmetry is likely to increase at non-linear rates, probably far
outstripping the non-linearity of the well known Moore’s law for the increase
in computing power over time.
[1] https://ses.library.usyd.edu.au/handle/2123/19601
[2] https://link.springer.com/article/10.1007/s11229-014-0457-7
[1] https://ses.library.usyd.edu.au/handle/2123/19601
[2] https://link.springer.com/article/10.1007/s11229-014-0457-7