IEEE Technology and Society Magazine - June 2021 - 10

Editorial
high school. While the outcry was around " the algorithm
got it wrong, " it is almost certain that it didn't:
It was doing exactly what it was told to do with the
data it had. People are responsible for this situation.
People specified the algorithm and its behavior. And
yet, the algorithm or the machine is blamed. In this
situation, there is no accountability because, as
Bryson correctly maintains, the people who made
it happen can deftly divert responsibility to the
machine. The result is that the people who brought
this situation upon the students of the U.K. remain in
their positions, the programmers get paid, and everyone
blames the " AI. "
Real life should not work this way. In real life, we
should learn from the mistakes we make and be held
accountable, not be doomed to repeat them because
we can divert attention away from us. Accountability
allows us to " fail forward " -and improve on our
behaviors and misconceptions so that the mistake
isn't repeated. Interestingly, whilst the situation has
not been repeated in exactly the same way in the
U.K. in 2021, responsibility is once again shifted from
the people who should be responsible to others in
a system-in this case teachers-so that if things go
wrong again it's their fault and not that of the people
who directed them in the first place. Algorithms
need not be followed just by computers.
And if we are correct, where does this leave us?
With an extremely fragile but carefully constructed
house of cards. Questions like, for instance, MIT's
Moral Machine examination of the " Trolley Problem "
become exposed as little more than tricks
with words, used to blind us to the actuality that the
machines we believe are making decisions about
life or death are no less than frauds: the " decisions "
they make are the responsibility of the people who
made them, nothing more or less.
Winfield et al. [12] note that one way of viewing
trolley problems and their ilk is to provide a set of
subjective moral judgments which the ethicist uses
as data to formulate general principles. Suppose
most people choose to flip the switch to save five
people instead of one, so the naive ethicist formulates
the principle of " the greater good of the greater
number. " But then what happens when the problem
is reformulated slightly: for example, consider five
people requiring an organ transplant or they will die,
and one healthy person. The application of that principle
would result in killing the one to save the five,
but we find that conflicts with the majority choice.
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This highlights a more informative way of viewing
trolley problems: exposing the flaws in formulating
(and worse, asserting) universal laws from limited
datasets, demonstrating that abstract philosophical
conundrums can illuminate, but do not necessarily
eliminate, the difficulties of moral or ethical decision
making (leaving aside the problem that, as Aristotle
observed, the majority might be wrong anyway [13]).
To put it another way, naive principles sound good
(and are good soundbites), but there is something
more subtle going on, even in real honest-to-goodness
people. Our problems start when we want to
let a machine (or the machine's designers) derive
ethical principles from just the data from a single (or
even multiple) scenarios, absent real understanding.
As one example of why The Trolley Problem is
insufficient as a way to think about how to make
policy for autonomous cars, consider the possible
consequences of implementing them based on its
outcomes. Suppose we arrived at the conclusion that
a car ought to avoid driving into young children at all
costs, even if that meant killing a middle-aged person.
That is certainly one reasonable-sounding policy.
Now suppose that we implemented this rule into
every car that was on the road. And further consider
what happens when said children realize this fact.
We can now conjure up the mental image of gangs
of children running amok in car parks or streets
with impunity, causing cars to swerve dangerously,
increasing the risk of them crashing into each other
or other people. This is perhaps fun for the kids, but
is clearly not helpful. Instead, consider that instead
today we have " rules of the road, " zoning, norms
about crossing roads, and behaving predictably, in
part driven by an appreciation for the dangers associated
with motor cars (autonomous or otherwise).
But it gets worse, if one were to formulate policy
on the machine's derived principles. This presents
a similar risk to what Ostrom [14] observed about
collective action situations involving provision and
appropriation of common pool resources (i.e., how
to avoid the supposedly inevitable tragedy of the
commons). Someone sets up an experiment (for
example, using a Prisoner's Dilemma) in the lab,
observe people are selfish, claim that people are
innately selfish because it suits some neoliberal
Randian world-view, and formulate policy, organize
society, base an economic model, align the educational
curriculum, on the assumption that people
are selfish. But all the experiment demonstrates is
IEEE Technology and Society Magazine
IEEE Technology and Society Magazine - June 2021

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