Scientific American - 09.2019

(Darren Dugan) #1
Graphic by Jen Christiansen September 2019, ScientificAmerican.com 77

SOURCE: “INDIVIDUALS WITH GREATER SCIENCE LITERACY AND EDUCATION HAVE MORE POLARIZED BELIEFS ON CONTROVERSIAL SCIENCE TOPICS,” BY CAITLIN DRUMMOND AND BARUCH FISCHHOFF, IN


PROCEEDINGS OF THE NATIONAL

ACADEMY OF SCIENCES USA,

VOL. 114, NO. 36; SEPTEMBER 5, 2017

thought the technology had a greater potential to
cause widespread catastrophes. That pattern held
true for other technologies as well.
To find out whether knowing more about a tech-
nology changed this pattern, we asked technical ex-
perts the same questions. The experts generally
agreed with laypeople about nuclear power’s death
toll for a typical year: low. But when they defined risk
themselves, on a broader time frame, they saw less po-
tential for problems. The general public, unlike the ex-
perts, emphasized what could happen in a very bad
year. The public and the experts were talking past
each other and focusing on different parts of reality.

UNDERSTANDING RISK
DID EXPERTS always have an accurate understanding of
the probabilities for disaster? Experts analyze risks by
breaking complex problems into more knowable
parts. With nuclear power, the parts might include
the performance of valves, control panels, evacuation
schemes and cybersecurity defenses. With GMO crops,
the parts might include effects on human health, soil
chemistry and insect species.
The quality and accuracy of a risk analysis depend
on the strength of the science used to assess each part.
Science is fairly strong for nuclear power and GMOs.
For new technologies such as self-driving vehicles, it is
a different story. The components of risk could be the
probability of the vehicle laser-light sensors “seeing” a
pedestrian, the likelihood of a pedestrian acting pre-
dictably, and the chances of a driver taking control at
the exact moment when a pedestrian is unseen or un-
predictable. The physics of pulsed laser-light sen sors
is well understood, but how they perform in snow and
gloom is not. Research on how pedestrians interact
with autonomous vehicles barely exists. And stu dies of
drivers predict that they cannot stay vigilant enough
to handle infrequent emergencies.
When scientific understanding is incomplete, risk
analysis shifts from reliance on established facts to ex-
pert judgment. Studies of those judgments find that
they are often quite good—but only when experts get
good feedback. For example, meteorologists routinely
compare their probability-of-precipitation forecasts
with the rain gauge at their station. Given that clear,
prompt feedback, when forecasters say that there is a
70  percent chance of rain, it rains about 70  percent of
the time. With new technologies such as the self-driv-
ing car or gene editing, however, feedback will be a long
time coming. Until it does, we will be unsure—and the
experts themselves will not know—how accurate their
risk estimates really are.

THE SCIENCE OF CLIMATE SCIENCE
EXPERT JUDGMENT , which is dependent on good feed-
back, comes into play when one is predicting the costs
and benefits of attempts to slow climate change or to
adapt to it. Climate analyses combine the judgments
of experts from many research areas, including obvi-

ous ones, such as atmospheric chemistry and ocean-
ography, and less obvious ones, such as botany, ar-
chaeology and glaciology. In complex climate anal-
yses, these expert judgments reflect great knowedge
driven by evidence-based feedback. But some as pects
still re main uncertain.
My first encounter with these analyses was in 1979,
as part of a project planning the next 20 years of cli-

When the Public Disagrees


about Science


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