I M A G E B Y A L I C I A K U B I S T A / A N D R I J B O R Y S A S S O C I A T E S

with Ruin at the Virtual Casino,” The New York Times, Feb. 5, 2017).

˲ People who believe that elections based on Internet voting and proprietary unauditable voting machines are inherently “fair” can be easily misled. People who continue to believe that Russians had no influence on the November 2016 election in the U.S. or in the April preliminary elections in France are oblivious to real evidence in both cases. Furthermore, The Netherlands recently abandoned electronic voting systems, returning to paper ballots—wary of further ongoing Russian interference.

Risks of Believing in Human
Truthfulness and Integrity

Human creativity can have its down-sides. For example, opportunities for ransomware, cyberfraud, cybercrime, and even spam all seem to be not only increasing, but becoming much more sophisticated.

Social engineering is still a simple
and effective way to break into other-
wise secure facilities or computer sys-
tems. It takes advantage of normal hu-
man decency, helpfulness, politeness,
bara Simons, Broken Ballots, University
of Chicago Press, 2012.

Systems can be untrustworthy because of false assumptions by the programmers and designers. For example, sensors measure whatever they are designed to measure, which may not include the variables that should be of greatest concern. Thus, a system assessing the slipperiness of the road for a vehicle might rely upon a sensor that determines whether the road is wet. Sometimes that is done by checking whether the windshield wipers are on—which is a rather indirect measure of slipperiness and can lead to false or imprecise recommendations or actions. At least one commercial aviation accident resulted from an indirect and imprecise determination of runway slipperiness.

Risks of Believing in
Computer Trustworthiness

Many people believe computers are in-
fallible and cannot lie. However, com-
puters are created by people who are
not infallible. Therefore, logically we
might conclude that computers can-
not be infallible. Indeed, they cannot
always perform exactly as expected,
given the presence of hardware errors,
power outages, malware, hacking at-
tacks, and other adversities.
Indeed, computers can be made
to lie, cheat, or steal. In such cases, of
course, the faults may originate with or
be amplified by people who commis-
sion systems, or design them, or pro-
gram them, or even just use them, but
not with the computers themselves.
However, even supposedly ‘neutral’
learning algorithms and statistics can
be biased and untrustworthy if they
are presented with a biased or untrust-
worthy learning set. Unfortunately, the
complexity of systems makes such be-
havior difficult to detect. Worse, many
statistical learning algorithms (for ex-
ample, deep learning) and artificial in-
telligence cannot specify how they ac-
tually reached their decisions, making
it difficult to assess their validity.

˲People who believe that online gambling is “fair” are likely to be be easy victims. So can those who know it is not fair, but are nevertheless addicted (see Francis X. Clines, “Threatened