NC Feb-Mar 2023

OPINION: MACHINE LEARNING
within tightly
defined boundaries,
as any usage of resources
also means an opportunity cost.
Preferably, then, we would opt to solve all
problems with rule-based approaches.
However, that runs into other complicated
issues, such as not all problems having
defined boundaries that can be solved
through rules.
Machine learning is great at solving
two types of challenges. Any problem
that requires a probabilistic answer is
likely much better done by a model
rather than anything rule-based. Another
area where machine learning is
immensely valuable is when the rules are
not clear.
In business, we might sometimes not be
sure on how to answer specific questions.
For example, what rules should govern a
self-checkout process? There are nearly
infinite possibilities for structuring such a
feature, but we're always looking to
maximise the outcome. In other words,
we'd prefer that a self-checkout would
lead to the most conversions.
INFERENCES FROM MACHINE
LEARNING MODELS
A common objection might be that some
machine learning models, such as Deep
Neural Networks, are essentially black
boxes. We're never quite sure what's
going on under the hood, so extracting
rules from them is as much guesswork as
without them. Fortunately, in business
applications, we don't need to be as
exact as logicians or scientists who
attempt to uncover the foundational
blocks of minds, language, or the
universe. Insights that point us in the
right direction are enough to create a
case for doing things one way or
another.
In other words, when building a model
that predicts the best outcome for a selfservice
customer system, we're not trying
to define some immutable laws of human
behavior. We're simply looking at an
admittedly ever-changing set of
circumstances and attempting to wrestle
out the best way to go about them.
So, going back to the same example, a
Random Forest algorithm, fed with
enough data from event sessions and
user activities, could outline the most
predictive outputs. These would indicate
what users are most influenced by during
the self-service process. These outputs
might not be ground-breaking or even
wide-ranging as they only work in a fairly
confined space of circumstances. But
they're more than enough for the
engineers, designers, and content writers
to perform optimisation that would lead
to better conversions.
These insights can then be turned into
rule-based algorithms. As such, machine
learning models can give us a way to
discover circumstantial rules that we can
implement in our business practices.
CONCLUSION
Hopes that machine learning will
replace rule-based systems are illfounded.
The latter is often much more
efficient and cheaper to build and
maintain than complicated machine
learning models. As businesses are
always turning one eye to efficiency,
rule-based systems are here to stay.
Machine learning, unlike commonly
thought, can be used to supplement
rule-based systems. While there are
possible ways of combining one into a
single system, the former can also be
used to garner insights that can then be
implemented into the latter.
In the end, machine learning shouldn't
be thought of as the cure-all for technical
problems. It's one of the many possibilities
that should be used thoughtfully. One of
those is to ensure we make better
decisions in other systems. NC
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