Communications of the ACM

challenge this bald determinism. Let’s scrutinize those briefly.

Deductive closure includes propositions not immediately obvious.

But even where the programmers are not sure what exactly will happen, because of obscure compound conditions, the algorithm does not “make a decision.” What happens is an implication of the assertions in force (written into the code if the programmer bothered to formulate assertions), that is, an implication of the deductive closure. The question whether programmers can be held responsible for the distant eventualities is significant, noting that what we view as algorithmic bias does not often seem deliberate. In any case, the deciding agent is certainly not the machine.

Timing of interactions may result in unanticipated outcomes, as in passive investment through computerized stock trading.

But unexpected states do not demonstrate demonic agency. Someone has decided in advance that it makes sense to sell a stock when it loses n% of its value. That’s not what we would call a real-time decision on the spot, because it ignores ( 1) the real time and ( 2) the spot. We would correctly call that a decision made earlier and elsewhere by system designers, which played out into unforeseen results.

The pattern-matching of deep learning precludes the identification of symbolic variables and conditions.

With no semantics available,
no agent prominent, and no
execution through a condi-
tional structure traceable, the
computer looks like the proxi-
mate decider. But no. If there
are training cases, some com-
plex combination of numeric
variables has developed from
given initial values which
were adjusted over time to
match a set of inputs with a
set of outputs, where those
matches were selected by the
systems designers. In unsu-
pervised learning, some sort
of regularities are uncovered,
regularities that were already
there in the data. Although it
may be tempting to say that
no one is deciding anything,
certainly no computer is mak-
ing anything that could be
called a decision. Someone
has planned antecedently to
seek those regularities.

Selection, recommender, and classification systems use the criteria implemented in their decision structure. We in the trade all know that whatever the algorithmic technique, the computer is not deciding. To explain to the public that computers are dumb may baffle and frustrate, rather than educate. The malapropisms that grant agency to algorithms confuse the determination of responsibility and liability, but also the public grasp of Tech overall. People may attempt to “persuade” the computer, or to try to fix, enhance, or “tame” the programs, rather than just rejecting their inappropriate deployment. At the extreme, people feel helpless and fearful when danger comes from beings like us— willful, arbitrary, capricious—except more powerful. Worse yet would be apathy: Society may ignore the difficulties and become resigned to the results, as if such programmed assessments were factive.

What would be the correct locu-
tion, the correct way to say it, passive
toward machine and active toward
programmer (or designer or developer
or specification writer or whomever)?
How should we note that “the deduc-
tive closure of home mortgage quali-
fication criteria entails red-lining of
certain neighborhoods”—other than
to say those exact words, which are not
compelling? How should we say that
“The repeated adjustment of weighting
criteria applied to a multi-dimensional
function of anonymous variables, close-
ly approximating an unknown function
for which some correct outcomes have
been identified by past users, associates
this individual record to your own dis-
crete declared criteria for a date”—with-
out saying “the dating app has chosen
this match for you”?

We have no other way of expressing such outcomes easily. We lack the verbs for computing that denote reaching states that look like decisions, and taking actions that look like choices. We need a substitute for “decides” in “the algorithm decides that X,” something to fill in the blank in “the program _______ X.” Perhaps “the program fulfills X.” Perhaps “the program derives that X.” Well ... this seems lame. The trouble really is that we have to avoid any verb that implies active mental function. This is new. This is unique to computing, as far as I can tell. The Industrial Revolution brought us many machines that seemed to have human capacities, but they also had material descriptions. For mechanical devices, verbs are available that describe physical functionality without the implication of cognition: “The wheel wobbles.” “The fuel line clogged.” We may say, jokingly or naively, that “the car chooses not to start today,” but we are not forced into it by lack of vocabulary.

For this new technological require-
ment, the best locution I can come up
with is, “the result of the programmed
assumptions is that X.” I have not
heard anyone seriously appeal to “com-
puter error” as a final explanation for
some time; that seems like progress in
understanding Tech. If we can forgo
that locution, maybe we can forgo
“biased algorithms.”
Any other ideas?

References

1. Angwin, J., et al. Machine Bias. ProPublica. May 23,

2016, http://bit.ly/2sGcEbH.

2. Banino, A., et al. 2018. Nature 557; pages

429–433. Vector-based navigation using grid-like representations in artificial agents. doi: 10.1038/s41586-018-0102-6.

3. Caporael, L.R. 1986. Anthropomorphism and Mechanomorphism: Two Faces of the Human Machine.

Computers in Human Behavior 2; pages 215-234. https://doi.org/10.1016/0747-5632( 86)90004-X.

4. Grothaus, M. Google’s AI is learning to navigate like humans, Fast Company, May 15, 2018, http://bit.ly/2JuyYzI.

5. Raymond, E. Anthropomorphization. From The Jargon File, http://bit.ly/2kPmF2Z.

6. Williams, S. (TMFUltraLong). The Evolution of Stock Market Volatility, The Motley Fool, Apr 3, 2018, http://bit.ly/2xKrROY.

Robin K. Hill is adjunct professor in the Department of Philosophy, and in the Wyoming Institute for Humanities Research, of the University of Wyoming. She has been a member of ACM since 1978.