april2022 - 33

MANAGEMENT
Photo: 195430633 by Anatoly Stojko | Dreamstime
Is our faith in
algorithms
misplaced?
AI has great benefits, but trusting
machines and sensors could
lead to bad decision making.
Recently, my doctor gave me a stress test,
which basically amounts to running on a treadmill
while attached to an electrocardiogram
machine. I noticed the pulse rate on my Fitbit
watch varied from the EKG reading. The variance
changed with time but did not seem to
follow any pattern. No big deal (I'm fine), but I
was fascinated by how this could be.
The answer may be a flaw in how software
engineers think. They like to measure things
that are easy to measure. And when measuring
is difficult or expensive-such as measuring
the heart's electrical activity-they come up
with a new solution. Here, the light in my wrist
was being measured to calculate my bpm. The
engineer makes some assumptions, tests, and
develops an algorithm to turn that data into
the displayed pulse.
The problem is that the reading was wrong. I
have been living as if my pulse rate is 75 when,
in fact, it isn't. Again, no big deal, but what if
it was a big deal? We are so used to relying on
algorithms that we will make the wrong decision
every day of the week.
This is increasingly relevant as commercial
vehicles increase their level of automation.
Someday, these commercial vehicles will likely
be driving themselves, and we need to have
a healthy fear of where this might lead-and
maybe a hesitation with algorithms in general.
Algorithms are concise instructions or
recipes that computers can follow to do their
assigned work. Who writes these algorithms?
Programmers do. They may be experts in the
systems they write for, or have no expertise
in them at all. They might have expertise in a
completely different field. That disconnect has
always been this way in the computer software
field. Today, data scientists do the programming,
and these subject-matter experts (SMEs) do hold
expertise in the application and what the readings
mean for the sophisticated technology.
But it can get complicated. The CAT 799
By Joel Levitt
PRESIDENT, SPRINGFIELD RESOURCES
Joel Levitt is the president of Springfield Resources, a
management consulting firm that services a variety of
clients on a wide range of maintenance issues. Levitt
has trained more than 17,000 maintenance leaders from
more than 3,000 organizations in 38 countries. He is also
the creator of Laser-Focused Training, a flexible training
program that provides specific, targeted training on your
schedule, online to one to 250 people.
truck for mining has about 150 sensors generating
data every second, which are transmitted
locally or even to the cloud. The sensors can
measure loading, weather, fuel, and GPS, and
the data combine to become big data. Now
hundreds or even thousands of algorithms
might use those fields to figure out what is
happening inside the asset, what to do about
it, and how to improve performance.
The goal of the SME may be to predict component
failure and test hundreds of algorithms to
find a few that work. What happens if the algorithm
doesn't work? How do we act if an algorithm
gives us bad results? Mistakes and biases
with artificial intelligence rank among the greatest
fears of AI scientists, technologists, business
leaders, and policy makers alike. I think that
blindly following the computer's results without
thought should be our fear as well.
In some cases, the company will lose money;
in others, quality might suffer a bit. But in a few
instances, lives are in the balance.
This worst-case scenario happened with
Boeing's 737 Max, where bad software caused
two fatal passenger jet crashes. Here's the
short version: Boeing installed new flight
control software called the Maneuvering
Characteristics Augmentation System (MCAS)
to overcome aerodynamic issues that came up
when the Max got a bigger engine. Pilots were
told that flying this new 737 was just like the
classic version, only this system may take
control and push the nose down if externally
mounted sensors determine the plane's pitch is
too high and might cause the plane to stall. The
pilot loses control, and if the sensor and software
are wrong, there is no recourse, because
the programmers didn't code for that. And as
a result, 346 people died.
What happens
if the algorithm
doesn't work?
In IEEE Spectrum, pilot and engineer Gregory
Travis wrote: " It is astounding that no one who
wrote the MCAS software for the 737 Max
seems even to have raised the possibility of
using multiple inputs, including the opposite
angle-of-attack sensor, in the computer's determination
of an impending stall. As a lifetime
member of the software development fraternity,
I don't know what toxic combination of
inexperience, hubris, or lack of cultural understanding
led to this mistake. "
This is one of many reasons why I fear that
we will defer to the machine and not think for
ourselves. Likely, this is already the norm.
April 2022 | FleetMaintenance.com
33
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