Technically Wrong Book Analysis Review the book presentations linked below. Please write a paragraph for each explaining what you believe is the main point

Technically Wrong Book Analysis Review the book presentations linked below. Please write a paragraph for each explaining what you believe is the main point of the book and why you think it is worthwhile reading for Philosophy.

https://sjsu.instructure.com/courses/1322863/files…

https://sjsu.instructure.com/courses/1322863/files…

https://sjsu.instructure.com/courses/1322863/files…

The paper should be DOUBLE SPACE, 2 PAGES LONG, and WRITE BY YOUR OWN WORDS because it will be check by TURN IT IN SOFTWARE. Overcomplicated
Technology at the Limits of Comprehension
Author: Samuel Arbesman
Pro. Lisa Bernasconi
Presenters: Ngoc (Intro + Chapter 1) , Hien (Chapter 2), Van (Chapter 3),
Sridevi (Chapter 4), Duyen (Chapter 5), Destiny (Chapter 6)
About the Author
– Samuel Arbesman
–
A scientist in Residence
of Lux Capital
A Senior Fellow at Unv.
of Colorado
A writer
Introduction
–
Purpose of Technology Making the World better
However, “technology grows
more complex” and making
our lives more
“OVERCOMPLICATED”!
=> How can we adjust ourselves??
“Humanity can
handle what
we have built.”
Chapter 1: Welcome to the Entanglement
–
“Entanglement”
“Welcome to this new age…”
Challenger disaster in 1986 (exploded after 73 secs)
https://www.youtube.com/watch?v=j4JOjcDFtBE (1:37)
– In 2007, a 2005 Toyota Camry
accelerated uncontrollably.
– In 1996, an Ariane 5 rocket exploded.
– Three Mile Island nuclear disaster
Complex vs Complicated:
“Living creatures is complex”
“Dead things is complicated”
“Today, most advanced
technologies are complex
systems.”
“We are in a new era,…. that can’t be
grasped in their totality or held in
the mind of a single person; they are
SIMPLY too COMPLEX.”
“Pages of computer code can be
… a beautiful solution to a
difficult solution, depending on
what you know. But when we
fail to have a complete
understanding,…: we encounter
unexpected outcomes.”
“We have entered the Age of
Entanglement… Each expert
knows a piece if a puzzle, but
the big picture is too big to
comprehend.”
The Limits of Abstraction:
–
–
“Hiding unnecessary
details… while still
retaining the ability to
interact with it in a
productive way”
“…allows someone to
build one technology on
top of another, using
what someone else has
created without having to
dwell on its internal
details.”
–
Author’s hope: “…we can handle these system, at least to some
degree.”
“…need to take a step back & identify the forces that are
propelling us into complexity – and preventing us from
comprehending it.”
Chapter 2: The Origins of the Kluge
Kluge ( kluːdʒ/)
–
is a workaround or quick-anddirty solution that is clumsy,
inelegant, inefficient, difficult to
extend and hard to maintain.
What about Software,
Network system???
–
1876s
–
1920s
–
Present
–
And?
4 Forces:
–
Accretion
Interaction
Edge cases
Common rarities
Interactio
n
Accretion
Kluge & the World
Edge Cases
Common
Rarities
Chapter 3: Losing the Bubble
Human language Vs. Machine language
In 1985, a cancer patient underwent horrible radiation
overexposure because the machine’s having indicated that no
dose of radiation was delivered.
VAN
➔ Burning sensation around her hip
➔ Admitted to the hospital
➔ Died after several months later due to her cancer
➔ The fact: her hip would have needed to be
replaced!!!
VAN
The operator of the large radiation machine:
Therac-25 proceeded with radiation therapy
Its malfunctioning were considered the worst
failures in the history of this type of machine because
it killed some patients because of irradiating them
with many times the dose they should have
received.
VAN
The errors could have been prevented or
minimized if:
★ Both hardware errors and software errors were
checked.
★ Recognized the fact that software is complex and can
fail in many different ways
★ Software bugs are a fact of life
★ How people think and how complex systems operate
are completely different
VAN
Examples
1. Computer programmers count from zero rather than one
❖ That’s the way machines count
❖ Thus, machine counting and human counting is different.
The errors multiply if we fail to adjust.
1. Soldiers are often confronted with complicated situations, but
they will get overwhelmed and loses the capacity to manage
the rush of events if a situations is too complicated, too
stressful, and too messy.
“LOSE THE BUBBLE”
VAN
When Our Brains Fall Short
★ Our neurons are more than
a million times slower
than a computer circuit.
★ Our long-term memory
cannot hold much more
than an old machine from
the 1980s could.
VAN
Too Complex to Handle
For examples: 45 tax professionals were given data
on a hypothetical family’s income, they came up
with 45 distinct conclusions about how much that
family should pay in taxes.
➔ Due to the limits of how much knowledge – not just memorized raw
data, but specialized technical expertise – we can keep in our heads.
➔ As technologies draw on more and more different domains of
knowledge, even experts lose the ability to know them all.
VAN
The End of the Renaissance Man
★ Cabinets of curiosities: wunderkammers
○ Soon realized that they could never be big enough.
★ Specialization is required in order to understand more and more
about the intricate systems around us: human body, physics,
finance, economy, and so on.
○ Driverless cars: requiring collaboration among those with
expertise in software, lasers, automotive engineering, digital
mapping and more.
VAN
The End of the Renaissance Man
Specialization is a successful process that yields impressive
technologies, but it also leads us into the Entanglement where we
are:
❖ Dependent on knowledge of complex technological systems
❖ Individuals do not have.
❖ This puts us in a difficult position and it is too late to recognize
this mismatch.
VAN
Chapter 4: Our Bug-Ridden World
●
1980s: Galaga
○ Glitch caused by some part of the code
●
●
Bugs and Glitches: unexpected and unwanted
byproducts of the complexity of our tech
○ Range from simple and strange (Galaga) to
potentially devastating ones (Heartbleed
Vulnerability)
○ Heartbleed Vulnerability: mistake written into
encryption software that could have
compromised the security of two-thirds of online
websites
Alan Turing ( mathematician)
○ Noted that as machines get more complicated, the divergence
between how we intend the machine to work and how they
actually work increases
●
●
●
What Glitches Can Teach Us
Gmail glitch
○ Caused by slight error, not many thought it would
cause a major meltdown
○ The bug demonstrated a hidden interconnectivity
between certain systems
Beginning of 1982: Vancouver Stock Exchange unveiled its
own stock index
○ End of 1983: it was half its original value
○ Bull Market: market in which share prices are
increasing
○ Problem:
■ Calculation index was wrong
■ Algorithm that was responsible for rounding did
not round properly
Glitches are helping us understand complex systems
Naturalists For Technology
●
●
●
“Miscellaneous” is a fascinating word
○ It shows there is a way of organizing the messy/disorganized things too
Naturalists have been comfortable with this word for a long time
○ Ex) Biologists learned about key genetic sequences through mutation
In technology, we need same sort of approach
○ Ex) To fully understand the system, we need to examine the malfunctions
Chapter 5: The need for Biological Thinking
•Biological thinking begins with observation.
•An English physician, Nathaniel Fairfax, observed intriguing phenomenon.
•Isaac Newton thought about the concept of motion and how light works.
•The perspective of unity and diversity can be identified with biology as a domain of
diversifiers and physics as that of unifiers.
Modes of Thinking
•Physics thinking: is a distinct trend towards
simplifying and unifying the observed phenomenon.
•Oversimplification is reserved within the physics
realm.
•Biological thinking: concerns with diversity and
facts except few biologists like Charles Darwin whose
model tend toward unifying approach.
•Both perspectives tend to develop theories that are
general and predictive.
The kind of Thinking that Technology
Requires
•Biological systems are more complicated compared to physics.
•In biology, there are huge number of distinctive components and a broad diversity of
systems
•The components are also distinct from many physical systems in biological systems, with
history.
•The systems in biology have changed and tinkered over the years.
•Evolution can leave us with obsolete code, just like technology..
Field Biologists For Technology
•Technological systems are becoming more complicated, which
affects both modes of thinking.
•Biological thinking on details and diversity is essential in dealing
with a complex evolved system.
•Application of biological thinking to technology entails
acknowledging tinkering as a way of learning and building a system.
The combination of biology and technology helps in dealing with
disasters, catastrophes and medical issues.
When Physics and Biology Meet
● Physics plays crucial role in technological advancement.
● When systems becomes interconnected, the resolutions levels
intersect.
● The systems in the environment allows us to see a sense of balance
between the sciences.
The Complexity Science
●
Complexity science is a natural path for comprehending complex systems.
●
Models: To understand this science, one needs to abstract away content of messiness to
find amenable regularities to clear mathematical shapes and understanding.
●
In biology, complex science is used to learn behavior of a subsystems.
●
It keeps scientists well informed about both biology and physics modes of thinking.
Chapter 6: Walking Humbly with Technology
Moses Maimonides
● Trained physician and rabbi in 12th Century
● Court Physician in Egypt
● The Guide of the Perplexed
○ Jewish thought and Aristotelian philosophy
● Despite Human Intellect there was a
fundamental mismatch b/w our curiosity and
what we can actually understand
● Maimonides was certain of our mental
limits
● Human Mind can understand all that it
wishes
● If we try hard enough, overtime we can
understand everything
● Same applies to our own creations
● Limits of our understandings of
computing, transportation, medical
devices, etc.
● Technology is becoming more complicated to understand
● Our responses tend towards two extremes: Fear and Awe
● Fear of the unknown
● Beauty and Power
● Not the product of a perfect process
● Technologies are Kluges
Glimpses Under the Hood
● Think back of when you last installed new piece a software…
● Did you know what was going on?
● Progress Bar AKA percent done progress indicators.
○ Not completely accurate
○ Invented by Brad A. Myers
● Published computer codes
● Hyper card
● We have little knowledge as to what is going on beneath the surface of
our gadgets
● 1960’s
○ Component of the Telephone System
● Playing simulations
○ Teaching students
○ Seeing how it responds
Mystery and Wonder
● The World of Wonders
○ Intriguing ideas
○ Fascinating things
○ Strange Events
● Demonstrates many ways in which we can find wonder all
around us
● Technologies were so interesting
● Mystery and Wonder?
○ The prerequisite for our ability to learn new things and
solve puzzles that confront us
● 20th Century
○ Limitative Theorems: statements that placed bounds upon what we
could ever know and understand
● Complex technological systems
● William Gibson
○ Unthinkable present
○ Entanglement
■ NOT incomprehensible
■ There’s more to be said on..
Last Thoughts ?
Technically Wrong: Sexist
Apps, Biased Algorithms, and
other Threats of Toxic Tech
Names: Amairany Santos, Anna Pham, Chenwen
Wang, Julie Victa, Xiaowing Huang
About the Author
●
●
●
Sara Wachter-Boettcher
Content Strategy Consultant & Author
Lead strategy and UX projects and
facilitate workshops for tech companies
large and small, as well as a wide range
of Fortune 100 corporations, education
and research institutions, local and
federal government offices, and arts
and culture organizations.
Technology Now
●
●
Relying on technology Ex: ordering food, booking a flight,
applying to jobs online,
Technology’s toxic impact on people
○ Facebook’s end of the year review
○ Snapchat’s racist filter “yellowface”
○ Apple, Microsoft, Samsung and other tech companies
uses Artificial Intelligence (AI) that were not built for
help during a crisis
■ Ex: 2011->2016 (took 5 years) to consider
safety and crisis for users
Fatima’s Story
–
She worked at a Silicon Valley startup lab: experiment with new technology and build prototypes
Suggestion: To do some research before creating a product | Apple Watch (1st gen, 2015)
–
–
Presented her data in front of an all male executive board
Led to a comment “Oh, 51 percent of women can’t be tech savvy”
Result: Partnered with a fashion brand which failed later on
Crawling Over Representation
●
Company diversity reports: having unfair representation for all groups
○
○
○
○
AirBnB
Facebook
Google
Apple
■
In 2014, Apple reported that they had 70% male globally, 80% in technical role
Normal People
●
Maggie Delano tracking her mood and irregular period on
an app called Glow
○
○
●
Online/ In-Person stores focus on ideal users
○
●
Did not consider range of people
■
Only considered women who wants to avoid
pregnancy, wants to conceive, or fertility treatments
Glow created app called Eve
■
Tracks period and sexual health
●
Did not consider women who are gay
Etsy notifies user, “Move over, Cupid! We’ve got what he
wants. Shop Valentine’s Day gifts for him.”
Edge Case – classic engineering term for scenarios that
are considered extreme, rather than typical
Normal People (Cont.)
●
Smartphone assistant
○
●
Default is a women voice
■
Known to be more helpful than men
■
Known for having administrative roles
Most default profile has a male avatar
○
Messer embarked on experimenting with default characters in
games
■
9/50 games used non gendered characters such as animals
■
1/50 games use girls
■
40/50 games use boys
Normal People (Cont.)
●
National Public Radio redesigned mobile app to be more inclusive to diverse audience
○
○
●
Personas
○
●
Used to display stories the same way
■
Same headline with small thumbnail image
Attracted customers by changing headline based on people’s stress
■
Blue for minor news
■
Red for urgent news
Developed to bring empathy into design process
■
Ex. Young women who are highly engaged, company should produce content targeted
at young women
Overall statement: “Normal people” are everyone coming from different backgrounds,
occupation, sex, ethnicity, color of skin, etc.
Select One
●
Forms makes everyone vulnerable
○
○
●
Choosing one generic race
○
●
Ex. Girl is filing out doctor’s form to get birth controls and saw “Have you ever been sexually
abused or assaulted”
Ex. forms that is only offered to male or female, that assumes applicant’s parents lived together
at a single address
Not a lot of people are accurately represented
■
Ex. If you’re Latino, and not also black, Asian, or American Indian, you’re supposed to
check white
■
White people are considered the default for forms
Long names are cut off on airline, tickets, Twitter, etc.
○
Ex. Sara Ann Marie Wachter-Boettcher
■
Driver’s license takes up 2 lines
■
Professional name on Twitter
●
Sara Wachter Boettcher
Select One (Cont.)
●
Facebook Policies on names
○
○
●
2014 – Facebook becomes more aware of different genders
○
○
●
Shane Creepingbear (Native American Name)
Drag Queens names
■
Facebook tells them to set up fan pages rather than having a normal
profile
Facebook included female, male, and custom
Need these genders for advertisement
Marital Status should not be included in forms because it is not necessary
○
Ex. Voting
Delighted to death
Have an eating disorder? Congratulations!
Just started chemo? Congratulations!
Chronically ill? Congratulations!
Smart Scale shaming a toddler for gain weight
McKesson Twitter feed
●
Filled with protest
They don’t know when to put things as a joke and when not to
●
Siri has artificial intelligence, but no emotional intelligence
Tracked, Tagged, and Targeted
Uber tracks the customer’s location to ensure
safety when picking and dropping off
You can use Uber without location data,
company just doesn’t want you to
Uber disabled “allow location when using the
app”
Data Collection
Google knows you more than you.
Facebook filtering discriminatory ads by
interest in race-related content
Algorithmic Inequality
●
COMPAS (Correctional Offender Management Profiling for Alternative Sanctions)
○ Parker=10 (highest risk for recidivism), Fugett=3
○
●
After investigation by ProPublica
○ Black people, twice as likely to be high risk; White people nearly as likely to be falsely
flagged as low risk
○ COMPAS is biased against black people
○
●
Since initial arrest, Fugett arrested 2 more times & Parker arrested 0 more times
Problematic because it can directly affect how long a convicted person spends in jail
Values the tech companies hold are not neutral
○ Reflect their creators (programmers & product teams)
Algorithmic Inequality con’t
●
●
●
Algorithm: specific set of steps needed to perform some type of computation- any
computation at all
○ Never easy to think about how they do it or whether the end result is actually accurate
○ A faulty algorithm can lead to a wrong answer on a math problem or a biased
algorithm doesn’t mean the computer is failing, it means the model is
Example of Yelp’s Algorithm
○ Goes through all the listings not knowing the best option for you
○ It uses what it knows about you & restaurants in its database to make an educated
guess
■ Previous searches, reviews, proximity, & other views
Algorithms start with values that underlie the way the algorithm was designed
○ They are not always “correct,” as powerful as they may seem
Algorithmic Inequality con’t
●
●
●
Gorillas & Google Photos:
○ African American male & friend labeled “gorillas” because of Google photos
○ Images are put into system labeled as they are & system uses those images & labels to be
able to label and categorize costumer’s images
○ Difficult for computers to identify details and variations
○ Had more to do with racially being identified as gorilla than google making a mistake
○ Photo technology failed to identify African Americans for decades
Kodak:
○ Printer kits with Shirley card which was normal, over years retook Shirley cards but never
was a person of color
○ Never made to depict people of color in images
○ Until, furniture retailers & chocolate makers
○ Never changed for a person of color, but for other business’ products
Other:
○ Flickr (labeled man as “ape”, Nikon (shutter blinking detector not tested well on Asian eyes,
HP computer facial recognition wouldn’t recognize African Americans at all)
Algorithmic Inequality con’t
●
●
●
Companies need to:
○ Remove biases from texts
○ Give the system unbiased information from the beginning
○ Ex: removing gender from words such as nurse or doctor
Dylann Roof
○ Influenced through different pages he kept clicking on through wikipedia trying to learn
about Tray-von Martin
○ June 17, 2005: attended bible study group at African Methodist Episcopal Church for an
hour then got up & opened fire, killing 9
■ The influence technology has & the way it is designed
For change:
○ Companies need to assume responsibility for information they put in their systems
○ Be transparent about where the info comes from, assumptions that may be encoded in
them, & if they represent users equally
Built to Break
●
●
●
●
●
Lindy West & Twitter
○ Was attacked & threatened by the “rape train”, reported it, & Twitter did not classify it
as abuse so they did nothing about it
Yiannopoulos & Leslie Jones
○ Talked bad about Jones in Ghostb…
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