Hey Wisecrack, Michael here. Let me ask you something: how is it that YouTube correctly
guesses, time and again, that I need more Dr. Gregory Johnson in my life?
“Make sure you keep your teeth together, and closed.” (crack)”Yeah.”
In other words, how and why does YouTube use information gathered from search history—say,
a one-time query for “EPIC BACK CRACKING”—to send us down rabbit holes filled with highly
specific content, whether that be chiropractic adjustments, zit-popping, or ASMR?
For that matter, how do social media sites take into account my friend groups, stated
political and entertainment preferences, and browsing habits to serve up ads that are annoying
but often chillingly relevant?
The above are well-known examples of “big data” in action. But, it’s not all ring-dingers
and soap-cutting. While big data has undoubtedly provided us with some cool and useful things
like predicting disease outbreaks or cataloging the Milky Way it’s also influencing your
life in far more subtle, and often spooky, ways.
Big data can and has been used to: predict who will become pregnant, in order to more
effectively target ads; diagnose depression remotely—again,again for the express purpose
of marketing goods and services; determine political leanings; evaluate teachers, and
make predictions about when employees might quit.
These and other big data uses are squirrelly enough on their own. But while most people
are just creeped out by the general concept of some modern-day Big Brother creeping on
their porn browsing habits, we often overlook one of the most important parts of big data:
its organization. We may balk at how it's gathered, sure, but what happens after that?
How do you take a sea of data and turn it into an efficient, ring-dinger-delivering-machine?
And how does the answer to that question pose one of the biggest problems for society in
the 21st century?
Welcome to this Wisecrack Edition on Big Data.
In 2020, it’s fairly common knowledge that internet giants like Google, Bing, and Yahoo,
and Facebook gather user data through search engines, user profiles, and other touchpoints.
But there are many other data collection hotspots available to companies looking to rake in
that sweet, sweet marketing insight dough.
Many of the largest internet companies also gather data through subsidiary services that
the average user might consider a bit more private — think, for instance, of your email
inbox, your phone’s location data, or the high-concept Mortal Engines slashfic you have
saved on Google Drive.
Like one traction city eating a smaller traction city, tech giants gobble up user data at every
possible opportunity—and the practice doesn’t stop with just Google or Facebook..
“Data brokers” such as Acxiom, Equifax, KBM, and credit card companies buy consumer
data from smaller companies in order to repackage and sell it to larger companies. Certain health
information, collected by insurance companies, is also bought and sold, as are data points
gathered from: “quantified self” gadgets such as Garmin; retail loyalty card programs;
satellite imagery; employer databases; bank records; and records of online gaming behavior.
To oversimplify a bit, companies collect this data not to improve consumer experience, as
they often claim, but to provide more and better direct marketing opportunities to advertisers.
The end goal, in other words, is profit—which only happens to coincide with a more streamlined
consumer experience.
For instance, big data isn’t used to create games which have more entertainment value;
it’s used to create games which keep people playing and paying, through both subscription
fees and microtransactions.
In a more grisly example, when data analyst Alex Pentland managed to obtain anonymous
location data on credit card usage, he and his team were able to use it to predict with
a high degree of accuracy which card users were experiencing depression.
They’d found that depressed folks tend to travel shorter distances when making purchases
than those without depression. So the tighter the circle of card-usage, the higher the probability
the card user was suffering from mental illness. Pharmaceutical companies were able to use
this information to start marketing directly to specific individuals.
Now, people have long had their qualms about whether this sort of data should even be gathered
in the first place:
“You took my sonar concept and applied it to every phone in the city. With half the
city feeding you sonar, you can image all of Gotham. This is wrong.”
Rather than dwell on whether or not it’s ok for someone to watch us poop through our
smartphone cameras, we’re going to focus on what people are doing with that data that
instead.
The basic problem is that people are organizing data in a way that’s entirely opaque to
the outside world, and deploying it in a way that could profoundly alter your life. All
this is sold to us on the basic assumption that something an algorithm tells us is objective
and true. That decisions made by them are “proper” and “natural.” After all
- who can argue with basic math? Spoiler alert: this guy.
But hold on: isn’t data objective?
You’d certainly think so. After all, raw data is just that; objective facts. Facts
like: how many Americans own dogs, or how many times it snowed this year. But as you
might have guessed, knowing exactly how many bowel movements occurred in America in 1987
does not a data scientist make. To perform its function as a tool for framing the present
and predicting the future, data must be organized to tell a particular story: we need to connect
the dots.
This is a necessary part of interpretation. Isolated facts have to be turned into narrative—and
that involves picking and choosing what data matters to this story and excluding data that
doesn’t. It also involves interpreting how those different pieces of data connect. For
example, the number of new Nicolas Cage films correlates to more people falling and drowning
to death in swimming pools that year. Is this something I should consider for my next pool
party?
Whenever any act of interpretation happens, it becomes “one person’s interpretation”
vs “another person’s interpretation.” For instance, where one lawyer sees standard
business activity, another sees “light treason.”
“There’s a good chance I may have committed some...light treason.”
One interpretation can benefit one person, and screw another. So in the world of data,
who is benefitting, and who is getting boned?
“I can’t see what’s happening. Are we boned?” - “Yeah, we’re boned.”
You’d think opportunity for said boning would be minimal—after all, this is data
we’re talking about, right? How can objective facts be unfair or biased? Data scientists
can’t make 1+1=3.
The problem, however, is that big data stops being purely objective once humans get involved—which
is to say, 100% of the time. That’s because those humans have to interpret and organize
data, or program a computer to interpret it how they see fit.
Data is organized by algorithms and models. When these models are constructed well, they
arrange data in ways that provide incredibly valuable information. When they are made poorly,
they can be misused to prop up self-fulfilling prophecies that screw over a ton of people
while enriching their creators. Let’s have a look at the difference through the lens
of what some people might say is the world’s most boring sport: baseball.
If you’ve ever read Moneyball or seen that Jonah Hill movie, or are just a sports fan
, you may be aware that baseball uses a ton of data. Many of us are vaguely familiar with
RBIs and batting percentages. But there are plenty of other data points to collect over
the course of a game—enough, even, to predict who is more likely win or lose. In other words,
it’s possible to use data to model a player’s performance. And often times, these models
determine who gets hired or fired in baseball. Seems cold? Well, it’s actually not that
bad.
That’s because these models are transparent, players are fully aware of them. They know,
for instance, that their on-base percentage is low, that their managers are considering
trading them as a result, and that they need to bring it up if they want to keep their
job.
“So you want us to walk more?” - “Good question. Yes.”
Compare this to, say, the YouTube algorithm, which promotes and demonetizes content in
a proprietary manner that is completely inaccessible to the creators being judged by it. Or teachers,
who in some cases have been fired as a result of “value-added modeling” without even
understanding how that modeling works.
Algorithms and models are also being used to give out loans and credit cards, or to
deny or approve parole for prisoners. As mathematician Cathy O’Neill notes in her book Weapons
of Math Destruction, the proprietary nature of these models means that, unlike statistics
in baseball, they are not open to scrutiny
So if I make claims about what kind of players with what kind of data will score home runs
and win games, people can laugh me out of town when those players fail spectacularly.
People make predictions, and then are held accountable if those predictions don’t play
out. Models are tweaked, people are fired, and so on. But if a mysterious model says
that these teachers will make your kids dumber and these teachers will make your kids smarter,
no such scrutiny generally exists.
As O’Neil points out, “Companies go out of their way to hide the results of their
models or even their existence”. Such well-defended company algorithms form a massive, ever-present
black box in our day-to-day lives: we know what goes in, our data, and what comes out,
organizational decisions, whether they be marketing strategies or police patrol patterns
- but the average person has no idea what goes on in the middle.
What’s important is deciding which factors go into making a model, which by necessity
is always a simplification, not a perfect representation of the real-world process we
want to analyze or predict.
Done right, modeling cuts out a great deal of noise while offering useful predictions:
for instance, avionics software is useful to pilots and air traffic controllers because
it tracks airspeed, wind direction, and the locations of landing strips, while excluding
other potential data points like building locations, street names, and restaurant reviews.
Google Maps does track those things, and not others, making it a useful tool for someone
trying to make their way to Taco Bell..
Models are created by people trying to achieve a particular goal, like winning baseball games.
But people are inevitably influenced by their own ideologies in the way they go about collecting
data—and in the stories they choose to tell about that data. As O’Neil puts it: “Models
are opinions embedded in mathematics.”
Again, it isn’t numbers or data points themselves that can reflect bias, but the ways in which
we interpret large numbers of data points. This can be further aggravated when we use
those interpretations to inform what data we’ll gather in the future. If we’re not
careful, we can wind up creating algorithms and models that are designed, consciously
or unconsciously, to reinforce our existing worldview. This is particularly important
to keep in mind today when, as O’Neil explains, poorly made or deliberately misleading models
can “encode human prejudices, misunderstandings, or biases into the software systems that increasingly
manage our lives”.
This bias already has a significant impact on jurisdictions around the country that use
PredPol, a predictive policing service.
In theory, PredPol uses raw data on crimes committed in a given area to predict when
and where crime is likely to occur again—think Minority Report, but with less hand-waving
and more math.
In practice, PredPol has been shown to set up an unfortunate “feedback loop”: in
response to a crime, it sends more officers to a given neighborhood, increasing the likelihood
that those officers will find and report someone committing a crime there, which will in turn
result in more patrols. This, in turn, raises the neighborhood’s profile as a hotbed of
criminal activity in the eyes of the algorithm, and therefore the police department, even
though the actual crime rate has remained constant. So if you’re the type to enjoy
a leisurely stroll while puffing on a “jazz” cigarette, you’ll probably be fine assuming
you live in a neighborhood that is wealthy and white. But if you live in an area where
Predpol has sent increased patrols because an abundance of Pickle Rick graffiti, you
might just get arrested. Then consider that some research suggests sending people to prison
makes them more likely to commit other crimes, and the feedback loop gets worse.
The issues with modeling are compounded even further by the fact that most of us have no
idea what the hell is going on during the design process. Big data models use data to
create very specific stories about people and groups. But those algorithms are not created
by, or understood by, the people they’re telling stories about.
This is not an accident.
Unlike baseball players, who can use their publicly reported stats to focus their training
efforts and improve their performance, we can’t readily access company-held data that
might help us improve our own lives.
For instance, what if we knew the factors that, statistically, might sabotage our diet
plans -- like a bad day at work or walking past a delicious pie shop on our way home.
That knowledge could empower us to make different choices or change our habits. BUT that would
also result in us buying fewer pies, which would, in turn, be very bad for Big Pie.
In other words, far better to keep companies profitable, while we are ignorantly belching
up boysenberry. Consumer algorithms and models are valuable to companies precisely because
they are hard for the average consumer to access and interpret.
But it gets even worse, because the little that we do know about big data - namely, that
we are being constantly tracked—still can have a tangible impact on our behavior.
The reason has to do with privacy, which law professor Julia Cohen defines as “shorthand
for breathing room to engage in the process of...self-development.” Big data takes a
giant meat cleaver to that breathing room, which Cohen argues means that we lose the
ability to choose which parts of our life we share with others. So maybe you’re not
quite ready to publicly try and fail at writing your first murder mystery, but would love
to do so privately. But, in the age of the internet, you can’t watch a video on arsenic
poisoning without YouTube making judgments about who you are as a person. And with the
power of recommended videos, it will likely serve you content that will slowly turn you
into that person. You watch one video of feet, and forever the algorithm thinks you’re
a weird feet guy. When our every embarrassing selfie, search query, and online purchase
is logged on a server somewhere out in Silicon Valley, we are reduced to our foibles and
shortcomings.
“Soon my little Box will be on countless TVs around the world...feeding me credit-card
numbers, bank codes, sexual fantasies, and little white lies.”
This all seems complicated enough,but trust me, the situation gets even weirder because
an algorithm that can be genuinely useful in one context can cause lasting harm in another.
So what happens when a highly-specific algorithm goes mad with power?
“Unlimited power!”
You get the credit score system. It was originally meant to determine whether or not the bank
should lend you money to start a fondue chain or something. Today, credit scores are also
used to make decisions about housing applications, and even employment. So if you missed a credit
card payment because you lost your job, good luck finding a new one.
The gathering, organization, and implementation of big data raise some thorny questions around
the issue of free will, which both Aristotle and Thomas Aquinus believe is one of the core
traits that distinguishes the human from the animal or the machine. Knowing what we do
know about the role big data plays in our lives, how do we reconcile the fact that so
many of our decisions seem to be beyond the reach of our conscious minds? To put it bluntly:
how do we make free choices about where and how we work if theoretically banking models,
browsing history, and likelihood of pregnancy or depression can keep otherwise qualified
candidates out of job pools? How do we exercise our free will in our social lives when Facebook
and other social media companies control what we see in our feeds, and who we are most likely
to interact with?
Under the circumstances, how do we stay human?
Philosophy professor Colin Koopman believes that we may require whole new fields of study
to find the answers to these and other questions surrounding big data. Koopman points out that,
to date, our society has always played catch-up with big data. New data-based technologies
are allowed to emerge pretty much without restraint, and only after the fact, and rarely,
do we put regulations in place to control them. To put it another way, we’ve taken
a ‘develop first, ask questions later’ approach to data science.
To form an idea about how we might move forward, Koopman looks to medicine, law, and other
complex fields with entrenched ethical codes. These codes help consumers because if, say,
your lawyer snitches on you, they can be disbarred. In a similar way, data science—and the millions
of us affected by it every day—may benefit from commonly held, enforceable standards.
Or at the very least, it might encourage brands to stop hovering over our shoulders like a
bunch of creepy street-hawkers.
“ You wanna buy a sundial?”
So what do you think, Wisecrack? Will we be able to turn big data into a transparent tool
used for the benefit of all? Or will biased algorithms continue to enable unfair treatment
under the guise of math and science? Is there another way forward that we haven’t considered?
Let us know your thoughts in the comments below, and as always - thanks for watching.
Later.