2020521 Artificial Intelligence Wont Save Us From Coronavir.docx

2020/5/21 Artificial Intelligence Won't Save Us From
Coronavirus | WIRED
https://www.wired.com/story/artificial-intelligence-wont-save-
us-from-coronavirus/ 1/6
ALEX ENGLER IDEAS 04.26.2020 08:00 AM
Artificial Intelligence Won't Save Us From
Coronavirus
The hype is real, but the potential is not: Approach claims
around AI and
Covid-19 with skepticism.
Fever detection is a plausible use case of AI, but it will take far
more time, effort, and money to build systems that are robust
enough
to trust. PHOTOGRAPH: ELIJAH
NOUVELAGE/BLOOMBERG/GETTY IMAGES
https://www.wired.com/contributor/alex-engler
https://www.wired.com/category/ideas
Coronavirus | WIRED
ARTIFICIAL INTELLIGENCE IS here to save us from

coronavirus. It spots new outbreaks,
identifies people with fevers, diagnoses cases, prioritizes the
patients most in need, reads the
scientific literature, and is on its way to creating a cure.
If only.
WIRED OPINION
ABOUT
Alex Engler is a David M. Rubenstein Fellow at the Brookings
Institution and an adjunct
professor and affiliated scholar at Georgetown University’s
McCourt School of Public Policy.
As the world confronts the outbreak of coronavirus, many have
lauded AI as our omniscient
secret weapon. Although corporate press releases and some
media coverage sing its praises, AI
will play only a marginal role in our fight against Covid-19.
While there are undoubtedly ways
in which it will be helpful—and even more so in future
pandemics—at the current moment,
technologies like data reporting, telemedicine, and conventional
diagnostic tools are far more
impactful. So how can you avoid falling for the AI hype? In a
recent Brookings Institution

report, I identified the necessary heuristics for a healthy
skepticism of AI claims around Covid-
19.
Let’s start with the most important rule: always look to the
subject matter experts. If they are
applying AI, fantastic! If not, be wary of AI applications from
software companies that don’t
employ those experts. Data is always dependent on its context,
which takes expertise to
understand. Does data from China apply to the United States?
How long might exponential
growth continue? By how much will our interventions reduce
transmission? All models, even
AI models, make assumptions about questions like these. If the
modelers don’t understand
those assumptions, their models are more likely to be harmful
than helpful.
Thankfully, in the case of Covid-19, epidemiologists know quite
a bit about the context of the
data. Even though the virus is new and there is much to be
learned, there is tremendous depth
https://brookingsinstitution-
my.sharepoint.com/:w:/r/personal/aengler_brookings_edu/_layo
uts/15/Doc.aspx?sourcedoc=%7B88105CD7-4510-43F4-B708-

64D18DFFA284%7D&file=OpEd-AISkepticism-COVID-
19_KS1.docx&action=default&mobileredirect=true&cid=141bd
4a0-267d-4d0e-a798-7eae3d945737
https://www.brookings.edu/research/a-guide-to-healthy-
skepticism-of-artificial-intelligence-and-coronavirus/
Coronavirus | WIRED
of expertise around what questions to ask and how they can be
answered. Modern statistical
epidemiology dates to the early 1900s, which means the field is
incorporating a century of
scientific research into its analyses. In contrast, machine
learning methods tend to assume that
everything can be learned directly from a data set, without
incorporating the broader scientific
context.
Consider, for example, the claim that AI was the first to detect
the coronavirus. Machine
learning is very dependent on historical data to create
meaningful insights. Since there is no
database of prior Covid-19 outbreaks, AI alone cannot predict
the spread of this new

pandemic. What’s more, the claim implicitly overstates the
ability of AI to inform us about
huge and rare events, which is not the strength of AI at all. As it
turns out, while software may
have sounded the alarm, grasping the significance of the
outbreak required human analysis.
AI’s real value lies in its ability to create many minute
predictions. For instance, the AI
epidemiology company BlueDot has successfully helped the
state of California monitor the
spread of the coronavirus. The company augmented traditional
epidemiological models with
machine learning, using flight patterns to predict the spread at
the zip code level. That’s the
value of AI. Those granular estimates can enable precise
allocation of funding, supplies, and
medical staff.
That said, you should not trust all individualized estimates from
AI. Frequently, a company will
report accuracy—the percent of predictions that are correct
during development—to purport
the effectiveness of an AI model. Unfortunately, this number is
easy to juke and often offers an
incomplete picture. For instance, Alibaba has claimed it can

diagnose Covid-19 from CT scans
with 96 percent accuracy. But, if you check in with the subject
matter experts, you’ll see that
the American College of Radiology has said that CT scans
should not be used as “first-line tests
to diagnose Covid-19." Other experts echo that this method is
not yet proven, and further
caution that while the algorithm may be fast, it requires that CT
scan rooms must be cleaned
and their air recirculated between each patient
As for that impressively high rate of accuracy, it’s time to share
a dirty secret of the machine
learning world: any data scientist in the field would scoff at that
level of accuracy. It’s
unbelievably high. Without any caveat, self-criticism, or
external validation, it’s suspicious on
its face. Even if it is true, we often need metrics aside from
accuracy to know if a model is
https://www.vox.com/science-and-
health/2020/3/20/21173472/coronavirus-pandemic-unknowns-
questions-seasonality-reinfection-covid-19
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3710332/
https://apnews.com/100fbb228c958f98d4c755b133112582
https://twitter.com/hellobluedot/status/1239321553558519810
https://www.brookings.edu/blog/techtank/2020/04/23/a-call-for-
a-new-generation-of-covid-19-models/

https://www.cnbc.com/2020/03/04/coronavirus-china-alibaba-
tencent-baidu-boost-health-tech-efforts.html
https://www.acr.org/Advocacy-and-Economics/ACR-Position-
Statements/Recommendations-for-Chest-Radiography-and-CT-
for-Suspected-COVID19-Infection
https://annals.org/aim/fullarticle/2764546/chest-computed-
tomography-detection-coronavirus-disease-2019-covid-19-don
Coronavirus | WIRED
effective, such as the percent of sick individuals who are
correctly diagnosed. While fatigued
medical systems have turned to AI analysis of x-rays for
triaging patients based on the severity
of their lung conditions, AI can’t currently diagnose Covid-19
on its own.
Even AI models that are thoroughly tested and validated in
development need further
skepticism, since real-world situations nearly always degrade
AI performance. In a recent
paper about the diagnosis of malignant moles, researchers
noticed that their AI models had
learned that medical rulers were often present in images of
moles known to be malignant. This

has the dual effect of driving accuracy up in the lab but down in
the real world.
This lesson gives us reason to be dubious of AI systems that
attempt to detect fevers from
thermal cameras. Surveillance technology company Athena
Security claimed that, in the past
month, they had adapted their existing software to do just that.
Even before it was reported
that Athena had allegedly faked the software demonstration, the
claim warranted skepticism.
While the fever-detecting technology may work well in lab
conditions, the software would
require a clear and precise view of a person’s inner face,
something that could be difficult for a
camera to obtain for, say, a person quickly walking into a
grocery store. That’s not to mention
that the analysis is affected by ambient temperature, humidity,
and even the sex of the subject,
which, of course, opens the door to bias.
Fever detection is a plausible use case of AI, but it will take far
more time, effort, and money to
build systems that are robust enough to trust. AI predictions are
only valuable if they enable an
intervention—is the fever detection reliable enough to prevent

people from entering a
supermarket or pharmacy? The CDC doesn’t think so and would
require a confirmatory test in
addition to thermal cameras.
All this should give you pause when evaluating claims that tout
AI as our Covid savior, and
that’s before considering the high likelihood that, just as we’ve
seen with other applications of
machine learning, it will introduce unintended consequences
and systemic bias. But while a
dose of skepticism is healthy, the near-future impact of AI on
some of these applications is
bright. AI is a widely applicable technology with tremendous
potential, but its advantages need
to be hedged in a realistic understanding of its limitations.
https://www.technologyreview.com/2020/04/23/1000410/ai-
triage-covid-19-patients-health-
care/?truid=333a9d7f348908d7ee0d240d1d992389&utm_source
=the_algorithm&utm_medium=email&utm_campaign=the_algori
thm.unpaid.engagement&utm_content=04-24-2020
https://www.sciencedirect.com/science/article/pii/S0022202X18
322930?via%3Dihub
https://www.vice.com/en_us/article/epg8xe/surveillance-
company-deploying-coronavirus-detecting-cameras
https://ipvm.com/reports/faked-corona
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3294528/#!po=
3.84615

https://stacks.cdc.gov/view/cdc/24857
https://www.nytimes.com/2020/01/18/technology/clearview-
privacy-facial-recognition.html
https://www.brookings.edu/series/ai-and-bias/
Coronavirus | WIRED
WIRED Opinion publishes articles by outside contributors
representing a wide range of
viewpoints. Read more opinions here. Submit an op-ed at
[email protected]
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Alex Engler is a David M. Rubenstein Fellow at the Brookings
Institution, where he studies the governance
of artificial intelligence and emerging technology. He is also an
adjunct professor and affiliated scholar at
Georgetown University’s McCourt School of Public Policy,
where he teaches courses on data science for
policy analysis.
OP-ED CONTRIBUTOR
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May 28, 2020
IEPA 060 Reading Writing 6
Rhetorical Analysis Essay #2
In the article “Artificial Intelligence Won’t Save Us from
Coronavirus,” Alex Engler(2020) criticizes the effectiveness of
Artificial Intelligence in mitigating the spread of the
Coronavirus. About the applicability of AI technology is real,
but its potential is not. However, the author argues that AI
technology has been lauded by many and regarded as an
omniscient surreptitious weapon. Despite the high incense by
the media and corporate press release, the author argues that AI
will only play a marginal role in our fight against of the
Coronavirus. However, this does not mean that AI is not helpful
in healthcare. AI will be more useful in future pandemics. With
the current moment, the AI has been used in data sharing,
telemedicine, as well as common diagnostic tools, which have
been impacted in the health sector. In this paper, I’m going to
discuss why AI can help with defeating Coronavirus.
Alex uses ethos to convince the audience about his
credibility and arguments. He has accomplished the ethical

appeal by carefully choosing the suitable language for the AI
topic and the audience. By describing AI topic has been hyped
and the actual marginal benefits around the fight against the
Coronavirus make the author sound fair. The author uses correct
grammar and sentences to relay his criticism about the AI
application's ability to fight against the Coronavirus. The author
encourages the audience to consider both sides of the issue. For
example, in the first and the most important rules, the author
argues that the audience should always look at the subject
matter experts. The author argues we should be wary when
applying AI from software companies that do not employ
experts. This convinces the audience that his opinion is fair.
The author also uses pathos to raise a specific idea about
the application of AI for the fight against Coronavirus. Despite
criticizing the AI technology, the author believes that it can still
be impacted if only some actions are taken in its
implementation. The author uses the Coronavirus pandemic
event to raise actions that can ensure AI positively impacts
healthcare. He describes the assumptions made by the AI
models and questions that the users should ask to ensure AI
technology is helpful and not harmful. Modelers should ask
themselves if data from one country such as China can be
applied in the United States. They also need to consider how
long the exponential growth might likely continue and how the
intervention would minimize the transmission rate. If users do
not consider these issues, AI will likely be more harmful than
helpful in society.
Finally, Alex effectively uses pathos to convince the
audience in his opinions about the application of AI in the fight
against Coronavirus. The author has cited some facts and
statistics in the article. For example, he says that Alibaba
requests that it has the ability to diagnose Coronavirus by using
CT scans with 96% accuracy. However, according to the
American College of Radiology, CT scans cannot be used as the
primary tests to diagnose Coronavirus. This supports his claim
on criticizing the usefulness of AI in the fight against the

spread of the Coronavirus, which has been a global challenge.
The author uses this technology to convince the audience to
pause during the evaluation of claims that AI will likely be our
saviors during the Coronavirus.
In conclusion, the author convinces the audience about his
credibility and arguments by using ethos. He also convinces the
audience with citing some facts and statistics by using pathos.
At last, this article can provide us some of the arguments which
we can think about if AI can really help with defeating
Coronavirus.
Reference:
Engler, A. (2020, April 22). Artificial Intelligence Won't Save
Us From Coronavirus. Retrieved from
us-from- coronavirus/
May 28, 2020
IEPA 060 Reading Writing 6
Rhetorical Analysis Essay #2
In the article “Artificial Intelligence Won’t Save Us from

Coronavirus,” Alex Engler(2020)
criticizes the effectiveness of Artificial Intelligence in
mitigating the spread of the Coronavirus.
About the applicability of AI technology is real, but its
potential is not. However, the author
argues that AI technology has been lauded by many and
regarded as an omniscient surreptitious
weapon. Despite the high incense by the media and corporate
press release, the author argues
that AI will only play a marginal role in our fight against of the
Coronavirus. However, this does
not mean that AI is not helpful in healthcare. AI will be more
useful in future pandemics. With
the current moment, the AI has been used in data sharing,
telemedicine, as well as common
diagnostic tools, which have been impacted in the health sector.
In this paper, I’m going to
discuss why AI can help with defeating Coronavirus.
Alex uses ethos to convince the audience about his credibility
and arguments. He has
accomplished the ethical appeal by carefully choosing the
suitable language for the AI topic and
the audience. By describing AI topic has been hyped and the
actual marginal benefits around the

Jason Marquardt
10020000006029173
That's not what we're looking for in a thesis statement for this
essay. We'll discuss in the conference.
Jason Marquardt
10020000006029173
Let's refer to the author by his last name.
Jason Marquardt
10020000006029173
Remember, we aren't going to use the terms "ethos", "pathos"
and "logos" directly in this paper.
Jason Marquardt
10020000006029173
I don't understand this sentence.
fight against the Coronavirus make the author sound fair. The
author uses correct grammar and
sentences to relay his criticism about the AI application's ability
to fight against the Coronavirus.
The author encourages the audience to consider both sides of
the issue. For example, in the first
and the most important rules, the author argues that the
audience should always look at the
subject matter experts. The author argues we should be wary
when applying AI from software

companies that do not employ experts. This convinces the
audience that his opinion is fair.
The author also uses pathos to raise a specific idea about the
application of AI for the fight
against Coronavirus. Despite criticizing the AI technology, the
author believes that it can still be
impacted if only some actions are taken in its implementation.
The author uses the Coronavirus
pandemic event to raise actions that can ensure AI positively
impacts healthcare. He describes
the assumptions made by the AI models and questions that the
users should ask to ensure AI
technology is helpful and not harmful. Modelers should ask
themselves if data from one country
such as China can be applied in the United States. They also
need to consider how long the
exponential growth might likely continue and how the
intervention would minimize the
transmission rate. If users do not consider these issues, AI will
likely be more harmful than
helpful in society.
Finally, Alex effectively uses pathos to convince the audience
in his opinions about the
application of AI in the fight against Coronavirus. The author

has cited some facts and statistics
Jason Marquardt
10020000006029173
Again, let's avoid this term.
Jason Marquardt
10020000006029173
Perhaps, but the author is also taking a very biased approach,
which he states right in the title.
Jason Marquardt
10020000006029173
I'm not sure that the correct grammar is necessarily worth
pointing out in this analysis. Don't most articles use correct
grammar?
Jason Marquardt
10020000006029173
I have no idea how any of this impacts the readers' emotions.
Jason Marquardt
10020000006029173
I thought you discussed pathos in the last paragraph.
in the article. For example, he says that Alibaba requests that it
has the ability to diagnose
Coronavirus by using CT scans with 96% accuracy. However,
according to the American
College of Radiology, CT scans cannot be used as the primary
tests to diagnose Coronavirus.

This supports his claim on criticizing the usefulness of AI in the
fight against the spread of the
Coronavirus, which has been a global challenge. The author
uses this technology to convince the
audience to pause during the evaluation of claims that AI will
likely be our saviors during the
Coronavirus.
In conclusion, the author convinces the audience about his
credibility and arguments by
using ethos. He also convinces the audience with citing some
facts and statistics by using pathos.
At last, this article can provide us some of the arguments which
we can think about if AI can
really help with defeating Coronavirus.
Jason Marquardt
10020000006029173
Wouldn't this be logos? I'm confused.
Reference:
Engler, A. (2020, April 22). Artificial Intelligence Won't Save
Us From Coronavirus.
Retrieved from https://www.wired.com/story/artificial-
intelligence-wont-save-us-from-

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