magic bullet exists to solve this problem, but there are
many different approaches being pursued. However, there
is an important consensus that as of today, explainability
methods typically somewhat reduce the accuracy of the
models, in favor of supporting a greater level of explainabil-
ity. It is also necessary to understand that in these
approaches, the explainability achieved is not absolute, but
rather should be thought of as an increase in “confidence
level.” As a result, it makes sense to selectively apply XAI
approaches with the balance of accuracy vs. explainability
that is appropriate to each use case.
Takeaway: Ethics surrounding the use of AI will only
become more important to business over time. Any enter-
prise utilizing AI will need methods, processes and possibly
roles, dedicated to proactively assessing whether there are
ethical implications for AI-related projects, and addressing
them accordingly.
Adversarial AI
A fairly recent development attracting attention in the field
of AI is called “adversarial AI.” It turns out that in many
cases, a model that is very good at visual or audio recogni-
tion can be fooled by what humans would consider
extremely subtle alterations of the image or audio signal
being analyzed. While these changes are not perceptible to
a human, they can cause a model to decide that, for exam-
ple, an image of a turtle is instead an image of a gun. Alter-
ations intentionally introduced through sophisticated tech-
niques to trick a perceptual AI are called adversarial AI,
which, understandably, has generated significant concern.
One can imagine numerous scenarios where tricking a
model into producing specific errors could be disastrous,
such as in autonomous vehicles, weapons detection or
medical diagnostics.
A couple of points to note about the development of adver-
sarial AI. First, it is generally agreed that as of this point in
time, no usages of these techniques for malicious purposes
have been discovered in the wild. All currently known exam-
ples of adversarial AI have been created by researchers in
an effort to understand, anticipate and prepare defenses
against future attacks. Secondly, researchers are simultane-
ously working on defenses against adversarial AI, including
using another layer of AI specifically to detect adversarial
AI!
Takeaway: Adversarial AI may not yet be an active concern
for your company, but it is certain to be a growing focus
over time. Keep watch on this topic, and include it as an item
to consider when evaluating risks associated with potential
AI-related projects.
Conclusion
AI is a vast movement in industry and in our culture, so the
topics discussed above are certainly not exhaustive, nor is
the level of detail. But we do feel that an organization would
be wise to ensure it is familiar with these developments, as
it considers AI-based offerings and features. The benefits
of AI are simply too great to ignore, plus, if you do not take
advantage of those benefits, your competitors certainly
will!
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